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INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 05, Issue 01, 2025, pages 137-169
Published Date: - 09-05-2025
Doi: -
https://doi.org/10.55640/ijdsml-05-01-16
Real-Time Financial Data Processing Using Apache Spark and
Kafka
Pradeep Rao Vennamaneni
Senior Data Engineer - Lead, Citibank, USA
ABSTRACT
The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article
looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-
latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time
applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement.
They are made possible through these open-source platforms that publicly ingest, process, and make decisions.
Integrating cloud-native infrastructure
—
using Kubernetes, service mesh, and container orchestration
—
ensures
elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro
services for decision support, regulatory reporting automation, and the automation of client interactions. The
article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka
Spark performance, and best practices around observability and DevSecOps. Real-time stream processing
combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of
emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined.
Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial
systems are presented to close.
KEYWORDS
Real-time data processing, Apache Kafka, Apache Spark, Financial microservices, Generative AI (LLMs), Cloud-native
architecture.
INTRODUCTION
From the appearance of digital technologies, algorithmic trading, and companies such as DeFi, an essential
transformation is taking place in the global financial sector, and the rise creates consumer expectations for instant
services. In today’s high
-speed data-intensive environment, traditional batch processing systems are quickly
becoming obsolete for end-of-day settlements and reconciliation. As a result, financial institutions must have to
make decisions in milliseconds over time and now in mission-critical spaces (trading, fraud detection, payments,
and regulatory compliance). Even small latencies, measured in milliseconds, can be catastrophic for financial results,
particularly for high-frequency trading (HFT), where speed is directly proportional to profitability. This has brought
forth a strong need for real-time data processing platforms that are capable of maintaining the velocity and volume
of modern-day business operations.
As most of this shift has taken place, cloud-native infrastructure has become the core of delivering financial services.
Changing traditional on-premises systems to cloud-native and scalable environments enables organizations to
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adapt more easily to evolving market requirements. Highly modular, resilient platforms have been developed
through technologies like Kubernetes for orchestration, service mesh for communication, and containerization for
resource isolation. They are secure by default, with encryption at rest and in transit, role-based access control,
observability, and automated scaling. With the unpredictability of transaction spikes, market openings, and
geopolitical events, cloud elasticity means financial institutions can maintain performance and availability without
sacrificing it. In addition, these modern architectures offer continuous monitoring and compliance automation,
helping organizations comply with regulatory frameworks such as PCI-DSS, SOC 2, and GDPR.
Suppose there is one of the most groundbreaking developments in the past few years. In that case, it integrates
generative artificial intelligence (AI), particularly large language models (LLMs), into real-time financial
microservices. Insecure models like OpenAI’s GPT, Google’s Gemini, and Meta’s LLaMA have increasingly been
deployed within secure, production-grade environments to augment financial workflows. They can do real-time
summarization of transaction data, draft suspicious activity reports, classify events, explain anomalies, make
contextual recommendations within milliseconds after an event, and much more. When integrated into the stream
processing pipelines, the LLMs can read these flagged transactions and feed the compliance teams with
understandable, actionable insights on the run. As analysts, traders, and then clients, powering the natural language
‘capabilities’ of LLMs has also been transforming how users engage with systems —
by being able to ask questions
such as “Tell me about the average trade volume of ABC
Corp. for the last 5 minutes,” in natural language and get
data-driven answers back in seconds.
This article goes deep into the architectural foundation and the strategic value of building real-time financial data
processing systems using Apache Kafka and Apache Spark. These two open-source technologies have become a part
of the modern data infrastructure. Kafka is a distributed event streaming platform that can consume and deliver
high-volume, fault-tolerant data, while Spark is a highly effective real-time analytics, transformation, and machine
learning engine. These tools are used together to form what could be called a robust and scalable architecture for
real-time decision-making. Discussing how different financial data requirements have progressed from batch to
real-time paradigms and the corresponding challenges and use cases requires a change. Following this, Apache
Kafka is examined as a foundational event streaming platform before a technical overview of Apache Spark’s
continuous data processing capabilities is provided. It then takes the narrative to the design of secure and scalable
cloud native systems, such as Kubernetes, container orchestration, and service mesh implementation. This paper
explains in detail how to architect resilient pipelines using Kafka and Spark integration, focusing on monitoring, fault
tolerance, and operational consistency.
The following sections illustrate how generative AI models can be used in financial microservices to make better
decisions and automate more work. This demonstrates how reactive technologies, such as surveillance of trade and
fraud detection, can converge/network together to create a proactive, intelligent, compliant system. Then, it details
performance optimization, discussing tips on tuning Kafka and Spark, managing the state, and building observability
across real-time pipelines. Further, future financial system architectures are discussed in the context of emerging
trends such as edge-native computing, federated AI, and decentralized finance. Finally, the article concludes with
strategic recommendations for CTOs, software architects, and engineering leaders that will shape how software
architects think about designing the next generation of secure, scalable, and AI-augmented financial platforms.
The Evolution of Real-Time Financial Data Needs
Shift from Batch Processing to Real-Time Systems
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Financial data operations had long been dominated by batch processing. Static sequential systems ran end-of-day
institution reports, calculated overnight risk positions, and managed bulk settlements. In the days when the number
of financial transactions was low, the systems were isolated, and customer expectations were minimal, this
approach was sufficient. Relational databases and nightly ETL (extract, transform, and load) jobs depended heavily
on batch operations, typically executed during off-peak hours. However, as financial ecosystems became more
interconnected and transaction volumes surged, organizations began to explore more adaptive and resilient
strategies, such as dual sourcing and real-time data pipelines, to ensure operational continuity and responsiveness.
Dual sourcing strategies offer a way to mitigate risk and enhance agility, which aligns with the shift from rigid batch
processes to more dynamic, scalable infrastructure (Goel & Bhramhabhatt, 2024). But this paradigm is outdated. In
a hyper-connected world, markets operate 24/7 and are open from anywhere in the world; it is financially
catastrophic, even with a few seconds of delay. With speed, accuracy, and context-aware decisions demanded at
the moment real events happen, the current financial landscape demands the art and science of possibilities today
(Mishra et al., 2017).
Real-time processing has come to answer these modern demands. Unlike batch systems, in which data is
accumulated and processed periodically, real-time systems take in data, analyze it, and act on it to ensure its
presence. They also provide advanced capabilities such as streaming analytics, low-latency decisions, and feedback
loops of a few milliseconds. But real-time matters, whether alerting a bank of a fraudulent transaction or allowing
an algorithmic trading bot to snap up shares milliseconds before a price spike. Consumer behavior has also evolved.
As a result, users expect mobile banking, digital wallets, and cryptocurrency platforms. Customers now expect to
be able to see their accounts instantly, quickly transfer funds between accounts, and receive real-time transaction
alerts. Therefore, financial institutions could expect the same from these platforms as any other fintech challenger
and lose ground if they fail to attain them.
Figure 1:
Real-Time Processing Explained
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2.High-Frequency Trading, Fraud Detection, and Compliance Use Cases
Table 1:
Key Use Cases for Real-Time Financial Processing
Use Case
Real-Time Requirement Technology Involved
High-Frequency Trading Millisecond latency
Kafka, Spark, In-Memory DBs
Fraud Detection
Instant alerts
Spark Streaming, LLMs
Compliance Monitoring Continuous audit trails Kafka, Dashboards, LLMs
High-Frequency Trading (HFT)
High-frequency trading is just one of the few domains that presents no better evidence of the need for real-time
processing (Acharya & Sidnal, 2016). HFT firms achieve thousands of trades per second, trying to turn micro-
arbitrage opportunities when rates fluctuate quickly. They are based on live market feed, millisecond data analytics,
and ultra-low latency trading execution. For example, a difference of 1 millisecond in processing speed can suffice
for a complete edge in the market value of millions. HFT systems utilize real-time data pipelines as part of their HFT
systems that process changes in the order book and trade events and market news streams in a near-instant
fashion. Among these topics, market data is often distributed using technologies like Apache Kafka and then
evaluated in real-time with complex event patterns within memory stream processors like Apache Spark or Flink to
make automated trading decisions.
Figure 2:
High Frequency Trading: HFT:
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Fraud Detection
Financial services fraud is dynamic and adaptive. Rules-based, static fraud systems cannot keep pace with constantly
evolving threats such as phishing, account takeover, and synthetic identity fraud. Institutions' ability to engage in
real-time analytics allows them to detect fraud as it occurs and prevent potentially catastrophic situations from
escalating. Leveraging scalable data platforms, such as MongoDB, enhances both performance and reliability in
processing fraud detection pipelines, ensuring that institutions can respond swiftly without compromising data
consistency (Dhanagari, 2024). Real-time fraud systems, for example, can scan a transaction's metadata,
geolocation, device fingerprint, and user behavior and compare it to other known fraud patterns. Once it detects
the anomalies, the system can flag, block, or challenge the transaction. Other real-time fraud models, such as Visa
and MasterCard, have reduced card-present fraud by more than 70% in some areas (Aguoru, 2015).
Compliance Monitoring
Financial institutions are obliged to monitor and continually audit their operations to meet regulators' increased
scrutiny. This requires real-time trade surveillance, risk analysis, and the reportability of regulations such as MiFID
II, Dodd-Frank, and Basel III. Delayed compliance is just a risk with batch reporting, and subtle patterns are easily
missed in short timeframes. These real-time compliance systems will look at trades that occur when a trader does
something, whether a layer or a spoof, and can then see if that has happened, potentially triggering a notification.
By connecting Apache Kafka to Spark-based analytics and dashboards, compliance teams can respond faster,
provide transparency, and maintain a robust audit trail.
Figure 3: Introduction to Compliance Monitoring
Volume, Velocity, and Veracity of Financial Data Today
Three significant forces drive the data revolution in finance: volume, velocity, and veracity.
Volume
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Financial services produce an astronomical volume of data. Institutions like JPMorgan Chase process over 1.5 billion
transactions daily, and trading platforms produce market data in the form of terabytes every day. Digital payments,
ATM transactions, cross-border transfers, mobile banking logs, and cybersecurity event logs also create this deluge.
In addition, real-time investment decisions are being fed by nontraditional data such as tweets, news feeds, and
alternative economic indicators (Bird, 2020).
Velocity
Data velocity is the speed at which data is generated, shared, and then consumed. Credit card transactions happen
in bursts; stock prices update in milliseconds; and cryptocurrency prices change second by second on decentralized
exchanges. Ingesting and processing this rapidly flowing data must occur without bottlenecks. Data pipelines are
now designed to operate in real time, allowing information to move seamlessly from endpoints, such as POS
terminals and trading platforms, to decision engines. To maintain both performance and security at such high
speeds, integrating robust DevSecOps practices into continuous integration and deployment pipelines has become
essential (Konneru, 2021).
Veracity
Finances are full of numbers, and numbers are prone to error or even forgery, which can haunt in a big way. It can
result in fraudulent transaction failures or compliance violations, such as mispriced assets. Veracity in real-time
systems centers on schema validation, timestamp integrity, data deduplication, and anomaly filtering. For instance,
two incongruent pricing feeds arrive for the same asset. In this case, the system has to make decisions in the blink
of an eye that feeds trust based on trust scores, source trustworthiness, or whatever kind of consensus model it is.
Additional Catalysts: APIs, Mobile, and Open Banking
The push for real-time integration is largely a result of Open Banking initiatives and API-driven financial ecosystems.
Regulator
y frameworks, such as the EU’s PSD2 and the UK’s Open Banking Standard, mandate that banks provide
account data to third-party providers (TPPs) through secure APIs, with the user's explicit consent. These APIs must
be secure, timely, responsive, and available in real time to support seamless data sharing and user experience.
Effective scheduling and real-time responsiveness, concepts increasingly emphasized across domains, play a crucial
role in meeting these demands (Sardana, 2022). For example, a user who wants to create their annual budget in
YNAB (You Need A Budget) can integrate the app via APIs with their bank account to pull in transactions as they
occur. Similarly, Plaid lets us connect to thousands of financial institutions in real time. They use secure, latency
REST and event-driven APIs, which are usually backed with a Kafka-based messaging queue and streaming
backends. The need for real-time updates is even more critical for mobile and fintech apps. For instance, Revolut
provides real-time FX rates, Robinhood displays live trade executions, and PayPal delivers real-time alerts on
transactions.
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Figure 4: Introduction to Open Banking - Open banking and APIs
Legacy Challenges and Modernization Drivers
While there has been a growing acceptance of the benefits of real-time architectures, legacy systems present
daunting challenges. Many financial institutions are still ill-equipped to deal with the performance and agility of
requirements demanded in this modern world, depending on COBOL-based mainframes, monolithic core banking
platforms, and rigid batch-oriented ETL systems. These legacy components do not easily integrate into a real-time
data environment, which makes it usually difficult, and if not impossible, to bring online, at least not without a lot
of middleware, data lakes, or hybrid microservices in the middle to connect the new with the old.
The fact remains that modernization is no longer optional, for it is inevitable. Several powerful forces are pulling
this transformation. It has also led to heightened pressures of competition with agile fintech competitors
–
competitors that are often more agile and faster, digital in their services. Regulators, meanwhile, are demanding
real-time access to audit trails, reporting capabilities, and compliance metrics, and batch processes are becoming
increasingly inadequate. Some of these risks are related to batch failures, data latency, and manual interventions,
which are growing threats to efficiency and trust. Additionally, customer expectations have evolved so much that
real-time responsiveness is no longer a premium on the service but a standard expectation.
To meet these demands, many enterprises are beginning to lean towards event-driven architecture (EDA) as their
framework for the modernization of their applications, their entire systems organization, and infrastructure. In EDA,
a real-time event stream is published and captured for every significant business event, such as user login, fund
transfer, or fraud alert. Downstream systems use these event streams to fuel analytics, dashboards, alerting
engines, and AI-driven decision-making tools. Organizations are laying the groundwork for agile, intelligent, and
future-ready financial ecosystems by shifting from static data processing to dynamic models centered on events
(Pasha, 2024).
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Apache Kafka: The Backbone of Financial Event Streaming
Overview of Kafka’s Architecture (Brokers, Topics, Producers/Consumers)
Built by LinkedIn and managed by the Apache Software Foundation, Apache Kafka has become the de facto way to
create real-time data pipelines. It is a distributed event streaming platform built for high throughput, fault
tolerance, and low-latency data ingestion and distribution, which is exactly what modern financial systems require.
Kafka is, at its core, merely a high-speed, reliable messaging queue; its architecture consists of a set of key
components that work together to provide performance and resilience. The system's backbone comprises Kafka
brokers; these servers store incoming data and respond to client requests. A Kafka cluster is used because it typically
consists of multiple brokers and allows for both scalability and redundancy due to hardware and network failures.
These are logical channels to organize data within Kafka called topics. Each topic can be further partitioned to be
processed in parallel, and scaling out is possible; this is quite an important feature when working with large financial
datasets, e.g., real-time market feeds or transaction logs. These topics accept data from these producers, which are
client applications. In a financial context, producers can include a trading platform streaming an order book, a
payment processor publishing transaction events as they occur, or a fraud detection engine issuing alerts and logs.
Consumers subscribe to a particular Kafka topic at the other end of the pipeline and read the data on the fly. Such
consumers can functionally be quite heterogeneous and include risk analytics engines, compliance dashboards,
machine learning pipelines, or even storage layers such as Apache Druid or Snowflake, providing access to a long
history for data exploration and analysis. Kafka strictly uses a publish-subscribe messaging model where producers
and consumers loosely couple themselves, can operate asynchronously, and can be hosted on different OS
platforms. This design makes it easy to evolve individual services with such flexibility while architecting the whole
system. In simpler terms, Kafka is the connective tissue at high speed that enables financial real-time systems to
receive, share, and act upon information continuously and reliably at scale (Narkhede et al., 2017).
Figure 5:
Apache Kafka Architecture
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Why Kafka is great for Financial Streaming (Durability, Throughput, and Replay)
There are several reasons why Kafka is so widespread among finance firms, and it's because they hit the narrow
tendencies that that field requires:
Table 2:
Kafka Capabilities Aligned with Financial Needs
Kafka Feature
Financial Benefit
Partitioning
High parallelism for transaction throughput
Data Replay
Enables forensic audits
Exactly-Once Semantics Avoids duplicate transactions
Durability
Prevents loss of critical transaction data
High Durability and Availability
Appending a paragraph about durability, the n states that Kafka ensures the data's durability by replicating each
partition across many brokers (Sulkava, 2023). If one broker fails, other brokers also serve the data. In financial
applications, the loss of transaction data can be catastrophic legally, reputationally, and operationally, which is why
this is so important. Fault tolerance may be fine-tuned with configurable replication factors and acknowledgment
policies.
High Throughput and Low Latency
Kafka is designed for millions of events per second with millisecond latencies. Companies like Goldman Sachs and
JPMorgan Chase use Kafka to perform stock trades and transactions with little or no delay. Kafka can perform
efficiently by batching messages and using zero-copy I/O (via sendfile).
Data Replay and Retention
Messages are stored by Kafka on disk within configurable periods (e.g., 7 days, 30 days, infinite). Thus, consumers
can replay past events without having to reset their offset pointers. For forensic analysis or compliance auditing in
finance, it is also necessary to detect a crime that has already happened or retrain a machine learning model on
historical transaction data.
Scalability and Partitioning
Kafka's partitioned topic model allows it to scale horizontally across data centers and geographies. It does this by
assigning transactions to a specific partition by customer ID, transaction type, or market, so all reads and writes
happen in parallel in a given partition. When it comes to the volume and complexity present in global financial
systems, Kafka can handle both quite easily.
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Exactly-Once Semantics (EOS)
Messaging systems traditionally offered at-most-once and at-least-once guarantees (Celar et al., 2017). However,
Kafka offers exactly one semantics: each record is processed only once, irrespective of network failures or retries.
Duplicate processing is something not wanted to happen, even more so in payments and trading, where
consequences range from reconciliation errors to regulatory breaches, and this feature is imperative in such
environments.
Financial Industry-Specific Configurations (Encryption, Compaction, Partitioning)
Kafka is flexible, so organizations can tune their behavior for secure, regulated, and high-performance
environments. The financial settings usually have several key configurations.
Table 3:
Typical Kafka Configurations in Finance
Configuration
Purpose
TLS Encryption
Secure data in transit
Log Compaction
Maintain the latest account states
Partition by Client ID
Ensure data locality and ordered processing
Multi-region Replication Disaster recovery and fault tolerance
End-to-End Encryption
Data in transit from producers to brokers or from brokers to consumers can be encrypted using SSL/TLS in Kafka.
This ensures compliance with PCI-DSS, GDPR, and other data protection regulations when used in sensitive
applications. Security is further enhanced through Kerberized authentication and role-based access control (RBAC)
using Kafka's ACLs (Access Control Lists).
Log Compaction
Kafka maintains only the final state of a key, as there are cases where the only state of a key that matters is the
latest state, such as for account balances or portfolio positions. This is because Kafka has a log compaction feature
that retains only the latest value of each key. This dramatically reduces storage costs and further accelerates
downstream processing, which is very important for stateful financial applications.
Advanced Partitioning Strategies
It is important to note that parallelism is made possible by Kafka partitions. Events can be partitioned in trading
systems by asset type (stock, bond, derivative), market (NYSE, NASDAQ, LSE), or client ID. This approach allows for
the scalability and processing of events related to the same customer or instrument. In addition, Kafka also works
great with Schema Registry (which enforces the backward and forward compatibility of Avro/Protobuf schemes
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used for message serialization, which is important in the case of changing financial platforms).
Multi-Region and Disaster Recovery
Many financial institutions operate in several areas that need disaster recovery (Chandra et al., 2016). Kafka Mirror
Maker and Confluent's Replicator are used for multi-region replication (multi-region replication), providing geo-
redundant and real-time failover capabilities.
Integration with Monitoring and Auditing Tools
Kafka provides many metrics via Prometheus exporters and JMX. Many financial firms use Kafka with Grafana, Data
Dog, or Splunk to monitor throughput, consumer lag, and broker health. Security teams can detect unauthorized
activity at the topic level via auditing tools that track access.
Real-World Adoption in Finance
Independent Financial Advisors and large global financial institutions have adopted Kafka because of its proven
performance and reliability. The myth about Kafka is that it is used by institutions, from investment banks to fintech
innovators, to incorporate real-time data streaming at scale in their core infrastructure. At the same time, major
tech players are applying Kafka to resolve complex needs in real-time, such as real-time trade processing and
dynamic risk analysis at Goldman Sachs, where the market data critical to operations is processed with minimal
latency. Kafka helps PayPal stream real-time logs and provides the fraud detection systems it runs on, by which it
rapidly responds to suspicious activity. Using Kafka with Apache Flink, ING established a solid streaming data
platform to spot unusual or fraudulent transactions within seconds. Intuit, for example, uses Kafka to stream tax
data as it streams over its network of microservices, transmitting over 3.5 billion messages a day, proving that Kafka
is highly usable for transactional environments with high throughput volumes. In addition to on-premises
deployments, Kafka is well integrated with cloud-native platforms such as Confluent Cloud, AWS MSK, and Azure
Event Hubs. By providing scalable, reliable, and secure Kafka infrastructure, these managed services ease the
migration from a traditional data center to a cloud-based architecture while maintaining performance and control.
As financial institutions increasingly modernize their data ecosystem, Kafka will remain a foundational layer for real-
time, event-driven systems.
Apache Spark: Real-Time Stream Processing and Analytics
Table 4:
Kafka-Spark Data Flow Example
Component
Role in Pipeline
Kafka Producer Publishes transaction event
Kafka Topic
Distributes data for processing
Spark Consumer Processes and analyzes the event
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Component
Role in Pipeline
Output Sink
Stores enriched data or triggers alert
Introduction to Spark Streaming and Structured Streaming
Apache Spark has matured from a batch processing engine to a platform capable of real-time stream analytics,
becoming a cornerstone of modern financial data infrastructures. Spark's Structured Streaming API, built on Spark
SQL, enables developers to handle data streams as incremental updates to a table. This unified approach to batch
and stream processing simplifies data architecture, minimizes redundancy, and enables real-time analytics without
the need to alter core business logic. As financial systems demand scalability, cost efficiency, and low latency, Spark
proves indispensable for processing large volumes of data with high resiliency, especially in mission-critical use
cases such as fraud detection, transaction monitoring, and risk scoring (Chavan, 2023).
Figure 6:
A Beginner's Guide to Spark Streaming Architecture
Integration with Kafka for Continuous Data Ingestion
The seamless integration of Spark with Apache Kafka makes it the greatest strength for building robust financial
data pipelines (Joy, 2024). Kafka plays the ingestion layer by capturing high-speed events such as market orders,
transaction logs, and customer interactions. The Spark Structured Streaming integrates the Kafka topics, which are
handled in real-time, and sends the enriched data to the output sinks such as Cassandra, Delta Lake, or PostgreSQL.
Confluent Schema Registry supports backward-compatible schema enforcement and exactly once semantics,
allowing reliable, validated schemas with functioning data flows. The architecture is architected to ensure maximum
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parallelism, which is essential when processing peaks in trading volume or retail transactions.
Real-Time Transformations, Aggregations, and Anomaly Detection
Modern finance isn't just about capturing and routing
—
it's about processing data in real-time through meaningful
transformations as the data flows through the system. Spark is very good in this domain due to its expressive Data
Frame API and Catalyst optimizer. For example, developers could specify rules to identify high-value international
payments, apply live foreign exchange rates, or enrich transaction records with customer risk scores. These
operations run continuously and can (and sometimes do) feed directly into each organization's compliance systems
or downstream alerts.
Spark's support for time-based windowed aggregations helps institutions compute moving averages, rate limits,
and trend analytics for given intervals. These aggregations are efficient and fault-tolerant, whether it's a 5-minute
window to analyze ATM usage or a 10-minute window to detect suspicious login attempts. Watermarking features
are often combined with them, and out-of-order or late-arriving data are considered. Additionally, it is embedded
in Spark's MLlib toolkit and incorporates streaming workflows for real-time anomaly detection (Alam et al., 2024).
Spark can serialize and load pre-trained models, such as logistic regression or a clustering model, to score stream
features such as transaction amount, transaction location, and device behavior. Suppose a transaction is fraudulent.
In that case, the system will instantly react to the situation: it can send a notification, block the transaction, or log
it for review and compliance.
Fault Tolerance, state management, and recovery
Spark is designed for fault tolerance in financial systems. Structured Streaming uses checkpointing and write-ahead
logs to store the application state and processing progress durably to HDFS or S3. Spark can recover from the last
known good state on a crash or job restart and avoid data loss, errors in reprocessing. This capability is critical in all
financial contexts, where every transaction or data point must be accounted for at the same time. The great thing
is that Spark also supports complex stateful streaming operations. With this, developers can preserve cumulative
metrics, compare the current state to the past, or notice changes in a user's behavior over time. For example, a
bank may track the total withdrawals by customers within a rolling 24-hour window and alert them to an outsize
increase. The stateful features of these features allow financial systems to plug in real-time logic, hit their internal
thresholds, and lower their operational risk.
Cloud-Native and Hybrid Deployments for Financial Use
Spark is also flexible in terms of deployment models. Additionally, it is smoothly running in cloud-native
environments such as AWS EMR, Azure Data Bricks, and GCP Dataproc with features of autoscaling, managed
clusters, and integrated notebooks. These are also out of the box with connectors to Kafka, object storage, and
Delta Lake, and also connect to Tableau, Superset, and other data visualization tools. Spark can connect legacy on-
prem systems and modern cloud platforms, thus becoming a great utility bridge for teams operating in hybrid
environments. For example, Kafka can be in Confluent Cloud, and the Spark that receives the streaming data is in
the Kubernetes cluster and can write to Iceberg tables for downstream analytics.
From real-time inter-bank transaction reconciliation by banks to analyze claims for fraud streaming by insurance
companies to fintech's use of Spark to offer personalized offers and credit scoring on the fly, Spark runs the gamut
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of use cases. With its real-time capabilities, rich ML ecosystem, and cloud-compatible ability, Apache Spark is a
foundational tool for organizations to transform financial data quickly, accurately, and confidently (Salloum et al.,
2016).
Secure and Cloud-Native Architecture Design
Foundations of Cloud-Native Security in Finance
Today's financial services landscape tightly couples the demand for real-time data processing with enterprise-grade
security and regulatory compliance. The dual imperatives of enterprise adoption have led financial institutions to
move increasingly toward cloud-native architectures. These architectures consist of microservices, containers, and
orchestration layer architectures, which are more flexible, resilient, and operate with higher efficiency. More
critically, however, they enable institutions to place security and compliance controls as part of their security as
code and into the infrastructure and application layers.
This design is centered on Kubernetes, the leading container orchestration platform. It manages application scaling
and acts as an abstraction of physical infrastructure (Khan, 2017). In a Kubernetes-based environment, pods can
run distributed services like Apache Kafka and Spark. They can scale independently and self-heal on failure. Such a
model guarantees that the financial pipelines are resilient and performant even in unpredictable workloads.
Containerization marks the beginning of modern security practices and provides a foundational layer of isolation
necessary to run services securely. Tools like Docker and Podman enforce strict boundaries that limit lateral
movement in the event of a breach. Additionally, vulnerability scanners such as Trivy or Clair can be integrated into
the CI/CD pipeline to verify container image integrity before deployment. This "shift-left" approach addresses
security threats early in the development cycle, eliminating vulnerabilities before reaching production. As systems
grow more complex, incorporating inference and intelligent automation into DevSecOps further enhances the
responsiveness and robustness of these pipelines (Raju, 2017).
Figure 7:
Cloud computing services
Encryption, Access control, and service communication
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There is no question about encryption for data in motion and at rest. Transport Layer Security (TLS) is used to
encrypt traffic between services, such as Kafka brokers and Spark consumers, to protect from eavesdropping and
man-in-the-middle attacks. Numerous financial institutions use cert-manager to automate certificate renewal
within Kubernetes, which helps lower the administrative overhead. Encryption on the rest is handled using solutions
such as AWS Key Management Service (KMS), Azure Key Vault, or HashiCorp Vault, as well as some CSI drivers that
support volume-level encryption.
In cloud-native systems, most cloud vendors use OAuth2 and Open ID Connect (OIDC) for identity and Role-Based
Access Control (RBAC) for authorization in an ACL fashion, enforced at the identity level. Centralized identity
federation and audit of logins, as well as native support of Multi-factor authentication, can be implemented through
organizations' integration with identity providers like Okta, Ping Identity, and Azure Active Directory. RBAC policies
internally enforce the least privilege principle, limiting access to which developers, analysts, or auditors can get
access to data and services for their role. Service mesh technology introduces another layer of security and control
(Istio or Linkerd) (Sidharth, 2019). These meshes achieve visibility into service-to-service communication, security
policy enforcement, and mutual TLS (mTLS) between microservices. When dealing with highly sensitive transactions
in the world of finance, service meshes help institutions detect anomalies, enforce traffic routing policies, and build
tamper-proof communication channels.
Table 5:
Security Tools in Cloud-Native Financial Architectures
Tool/Protocol
Function
TLS
Encrypts network traffic
OAuth2 / OIDC Authentication and identity federation
RBAC
Granular access control
mTLS (Istio)
Secures microservice communication
Auditing, Lineage, and DevSecOps
Data lineage and auditability are crucial to transparency and compliance with regulations. Financial organizations
need the ability to walk through every event through ingestion in Kafka, transformation in Spark, and storage in a
downstream analytics database. This allows teams to visualize data flow, reconstruct historical processing logic, and
so on, and the tools providing this are representing the tools that give metadata tracking functionality, such as
Apache Atlas, Open Lineage, and so on, and Data Bricks Unity Catalog, which can provide capabilities such as lineage
and audit trails. Another important part of this metadata connects to how the data was sourced, transformed, and
used in decision-making, which is very important during audits.
DevSecOps is a shift in the security program moving towards integrating security with continuous integration and
delivery pipelines. Security configuration to peer review, version control, and auto-provision across multiple
environments with the help of Infrastructure-as-code tools like Terraform, Pulumi, and Ansible. GitHub Actions or
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Jenkins, with embedded scanning tools, ensure that secrets are not exposed, image dependencies are secured, and
policies are enforced before software is deployed. Human error is eliminated, and there is consistency across our
development, staging, and production environments. Organizations increasingly adopt policy-as-code frameworks
for dynamic compliance enforcement, e.g., Open Policy Agents (OPA). These tools allow real-time decisions to be
evaluated against codified rules and result in non-compliant workloads being blocked automatically. This approach
secures compliance, and compliance is not auditor-enforced; it's a way of life.
Observability, Compliance, and Strategic Enablement
Secure cloud-native environments require observability. Financial institutions use monitoring systems like
Prometheus, Grafana, and Datadog to measure individual services' latency, throughput, and error rates. ELK (Elastic
search, Logstash, Kibana) or OpenSearch stacks are used to ship and analyze logs, detect anomalies, and correlate
security incidents quickly. Open Telemetry allows distributed tracing in multi-step processes. The request isn't
ingestion in Kafka to process in Spark to store in the ledger database to debug performance issues or pinpoint
vulnerabilities.
Compliance with frameworks such as GDPR, PCI-DSS, SOC 2, and FINRA is fundamental to keeping operational
licenses and the trust of clients (Ahuja, 2024). Capabilities The public cloud platforms offer compliance-aligned
services such as encryption key rotation, activity audit, geofencing, facility access controls, store safeguards, and
more. While configured properly, these built-in features of Google Workspace are a strong foundation for
protecting jurisdictional data protection laws and institutional policies. Audit log generation from Kubernetes,
identity providers, and monitoring platforms also makes up an evidentiary trail that can be queried to produce
reports for regulatory assessments. The secure cloud-native model is technically significant and, importantly, a
strategic and logical enabler. Through the security embodiment within the lifecycle of infrastructure and data,
organizations can move fast and wholeheartedly. Without sacrificing trust, uptime, or user experience, they can
scale to meet rising transaction volumes, respond to changing threats, incorporate new regulations, and enable
cross-border financial services.
Building a Resilient Data Pipeline: Kafka + Spark Integration
Overview of Real-Time Financial Pipeline Architecture
A robust real-time financial data pipeline is built not from a single technology, but from a multi-piece system where
each component is responsible for ingestion, processing, and output. Apache Kafka and Apache Spark are routinely
employed to form the backbone of streaming architectures in modern financial services. Kafka serves as the event
ingestion layer, capturing transactions, account updates, market ticks, and logs with high throughput and durability.
Spark, on the other hand, performs complex stream processing
—
enriching data through analytics, transformations,
and the application of machine-learning models. As data environments become more sophisticated, insights from
advanced generative model architectures offer valuable design parallels for orchestrating and optimizing such
complex, multi-layered systems (Singh, 2022).
Kafka Producers is the architecture that starts with systems or applications such as ATMs, trading platforms, or
fraud monitoring systems, publishing events to Kafka topics. These topics are partitioned so that they are
consumable in parallel and can scale. Spark Structured Streaming applications will subscribe to those topics and
process the incoming data using that predefined logic. The transformed, enriched data is sent to output sinks like
Cassandra, Delta Lake, and Elastic search or directly to Kafka for further consumption (Emma & Peace, 2023). In
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mission-critical applications such as transaction processing and compliance logging, each layer must be resilient to
failures, operate at high throughput, and ensure exact once-delivery semantics. The Kafka+Spark stack handles all
of the above very well, enabling fault tolerance, replay, and integration of modern observability tools, for instance.
Figure 8:
Data Pipeline Architecture
Designing for Scalability, Fault Tolerance, and Latency
An ocean away from banking's other critical points and time zones, latency and downtime have real monetary
consequences. Therefore, they must build resilience into every layer of the pipeline. Partitioning is how Kafka scales
horizontally. Kafka distributes data on a topic between multiple brokers and partitions to ensure that throughput
can grow with more transactions. Durability is replicated, and leader election guarantees continuance without
disruptions. Spark also addresses this with its native implementations of structured streaming checkpointing and
write-ahead logs (Armbrust et al., 2018). It does this by periodically saving state to persistent storage such as
Amazon S3 or HDFS, so if a streaming application fails during a job because of node failure, it can resume without
data loss. Moreover, where applicable, Spark's stateful operations (e.g., windowed joins and aggregations) are
recovered from previous checkpoints.
Kafka and Spark easily integrate with Kubernetes and provide the flexibility to auto-scale themselves on metrics like
CPU usage or message lags, among others. Such a dynamic scaling capability is critical when traffic spikes are
induced during earnings releases or a geopolitical event. Tools like KEDA (Kubernetes Event-Driven Autoscaler) scale
Spark executors or Kafka consumers based on real-time workload demands. Batch intervals, message sizes, and
serialization formats are optimized for a low-latency design. Apache Avro or Protobuf can be used with schema
enforcement, taking the overhead away and increasing consistency. Using tuning, Spark pipelines can perform data
processing with sub-second latencies and satisfy the performance constraints demanded by HFT, fraud detection,
and real-time alerts.
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Table 6:
Pipeline Resilience Strategies
Strategy
Benefit
Kafka
Partition
Replication
Ensures availability
Spark Checkpointing
Recovers from job failures
Kubernetes Autoscaling
Adapts to real-time workload
spikes
Schema Registry
Ensures schema consistency
Data Quality, Consistency, and Schema Evolution
Data consistency and quality are of the highest importance in financial data pipelines. Events may be in an
unexpected format, arrive in a different order, or be duplicated. Log retention, message replay, and even
idempotent producers are all ways Kafka deals with these challenges. Since consumers can reset offsets and
reprocess events, they can replay past windows for forensic or compliance purposes. To keep aggregations accurate
but efficient in memory, Spark introduces watermarking to handle late data gracefully. This feature is particularly
useful because processing transactions or logs may take time due to network latencies or offline systems. For
example, watermarks guarantee that branch-level sales or cross-border transfers are accounted for correctly, even
when delayed messages show up.
Confluent Schema Registry serves Schema Evolution, stores versioned schemas for Kafka topics, and performs
compatibility checks at the 'producer' and 'consumer' levels (Bejeck, 2024). This prevents data corruption and
makes deploying new fields or record types very easy. Spark doesn't have to be recompiled if a new field is added
and can populate the appropriate schema from the registry. The Spark pipeline can be used to build up validation
rules for data quality to enforce further. For example, null checks, range enforcement, field normalization, and
others can be applied as streaming records are processed. Data integrity is guaranteed as invalid records can be
routed to a dead-letter queue (DLQ) in Kafka or flagged for manual inspection without stopping the pipeline.
Monitoring, Alerting, and Visualization
Observability makes up a resilient pipeline. Financial organizations' data systems must be tracked in real time for
their health, performance, and security. Prometheus can scrape JMX metrics exposed by Kafka, like consumer lag,
throughput, broker availability, and display them in Grafana. These dashboards give system backlogs, partition
utilization, and processing delays in real-time. Spark has its own Spark UI, which shows the execution time of stages,
memory usage by jobs, and executors' health. Prometheus exporters can export Spark metrics to central
observability platforms in production. Teams will, therefore, trigger alerts by setting thresholds for memory
pressure, lag, job failure, and use PagerDuty, Opsgenie to minimize downtime.
Transaction KPIs, fraud alerts, and compliance visualizations can also be provided to business stakeholders in real-
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time dashboards. Connections to output sinks are possible, and tools like Apache Superset, Tableau, and Power BI
can intuitively visualize the processed data from Spark. They may also bubble up regional transaction trends, spikes
of suspicious activity, or user behavior patterns. These dashboards are geared to provide more efficient decision-
making at the executive level. Completing the picture is logging and tracing. The ELK stack or OpenSearch can be
used to centralize Kafka and Spark logs with structured log fields for query ability. Tracing with Open Telemetry lets
teams understand where a message comes from and how it's transformed into Spark, stored, and from beginning
to end, critical for SLA enforcement, compliance audits, and root cause analysis.
Leveraging Generative AI (LLMs) in Financial Microservices
The Rise of LLMs in Financial Services
Generative AI, particularly large language models (LLMs), has been an emerging wave in the last few years, and
finance has been no different. OpenAI’s GPT
-
4, Google’s Gemini, and Meta’s LLaMA, LLMs are adept at language
understanding, contextual reasoning, and knowledge synthesis. These capabilities are used in the financial sector
to support decisions, automate reports, explain anomalies, and permit more natural interactions with complex
systems. Unlike conventional rule-based or supervised learning models that need structured input and narrowly
defined output, LLMs are trained over a dataset with diverse entries. They can handle myriad tasks, such as
summarization, classification, question answering, and document generation. The ability to support analysts,
auditors, and customer support agents in real time while dealing with increasingly high-velocity data makes this
flexibility ideal for financial microservices. However, the design principles of modern financial infrastructure are
quite suited to deploying LLMs in low-latency inference, security, explainability, and compliance environments
(Asimiyu, 2023). As these models mature, they replace legacy natural language processing (NLP) systems and
augment some high-stakes applications, such as fraud detection, regulatory compliance, and client onboarding.
LLMs as Secure, Stateless Microservices
Organizations expose the LLM as a stateless microservice for making large language models (LLMs) production-
ready in financial systems. These services are queried from APIs (RESTful) or message brokers (Kafka) to integrate
into the existing data pipelines and application logic easily. In this approach, AI-generated insights are dynamic and
not embedded in transaction core systems to maintain modularity and scalability. Typically, a real-time data
processing engine, such as Apache Spark, is used to process the data and identify events or data that need to be
contextualized. Then, the data points are serialized and transmitted through Kafka or HTTP to an inference gateway.
In turn, the inference gateway, called LLM, can be hosted on a GPU-accelerated container, virtual machine, or a
managed cloud model endpoint like OpenAI, Azure AI, or Google’s Vertex AI. Once the LLM has processed the
request, the resulting output, such as a risk summary, anomaly explanation, or natural language report, is returned
to the requesting service (Khlaaf et al., 2022). It can then be displayed, stored, or applied in an automated decision-
making process.
This setup enables financial institutions to modularize AI capabilities and to scale, monitor, and update AI
components without interfering with their transactional systems. Furthermore, by leveraging Kubernetes-based
orchestration and autoscaling, LLM microservices can handle bursty workloads, such as those seen during quarterly
earnings releases or tax season, without compromising the performance or stability of core systems. The modular
design and workload distribution principles seen in AI domains like image captioning systems offer relevant
parallels, reinforcing the value of decoupled, scalable architectures in complex environments (Sukhadiya et al.,
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2018).
Figure 9:
Monitoring Java Spring Microservices
Real-Time Use Cases in Financial Microservices
LLMs are additive when embedded in real-time decision support loops. For example, in fraud detection workflows,
LLMs can read transaction metadata, reference similar past alerts, and write human-readable justifications of alerts
created from rule engines based on Spark. This drastically cuts down on the mileage compliance officers must put
in when investigating raw logs or consuming dashboards. Similarly, financial reporting use case LLMs can summarize
daily transaction activities, portfolio updates, or policy changes in compiled bulletins in a structured format. A Spark
pipeline could calculate statistical trends over trading data, and an LLM could interpret those insights into a report
that uses ‘natural language’ and is ready for review or distribution. With this automatio
n, regulatory filings, investor
communications, and executive briefings are done quickly and consistently.
LLMs also serve as key beneficiaries in customer support and client interaction areas (Eldon & Kondakhchyan, 2018).
Independent Venture Partners and Cafeca Collective portfolio companies, banks, and fintechs are deploying
conversational agents built using LLM to portals, apps, and voice services. They can answer questions like, “Why
was my card declined?” or “What were my top five expenses last month?”
or “Explain the latest changes in my
investment portfolio.” Real
-time data from Spark is joined with the customer profiles and piped into the LLM for a
contextual and personalized response. However, some applications are integrating LLMs with retrieval-augmented
generation (RAG), where the model asks questions to a real-time vector database that retrieves indexed financial
documents, reports, or even transaction histories like Pinecone or Weaviate. With this, LLMs can base their
responses on factual data, which prevents hallucinations and enhances trustworthiness in a regulated environment.
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Table 7:
LLM Use Cases in Financial Microservices
Use Case
LLM Output
Fraud Alert Triage
Justification in plain language
Regulatory Reporting
Auto-generated
daily
summaries
Client
Chatbot
Interactions
Natural language responses
Surveillance Querying
Convert NL to SQL for real-time
logs
Guardrails, Explainability, and Compliance Considerations
The potential of LLMs is considerable, but their use in financial services should be done with care. One critical
challenge is explainability. Under GDPR and MiFID II regulations, some decisions (particularly where automation is
used) must be explained to customers. To fix this problem, developers are implementing transparent prompts,
token-level attention heat maps, and audit trails for every interaction with a model. The mechanics allow auditors
to see inputs, context, and variations of the prompt leading up to the output produced. Data privacy is another
major concern. Just like all other backends, LLMs must not leak sensitive information, improperly memorize
transaction data, or expose customer PII. Financial institutions usually fine-tune open-source models on
anonymized datasets to mitigate these risks or use a proxy layer that redacts sensitive fields and poses queries for
policy violations. Full control over data governance and residency of the data gets even further enhanced through
local inference in some organizations, which prevents the data from being sent to external APIs.
LLM APIs implement rate limiting, content filtering, and prompt validation systems to block misuse and drift
(Goldman, 2021). These guardrails prevent users from being prompted to inject attacks, generate toxic language,
and make compliance mistakes. Logging and observability tools allow LLM behavior to be monitored, tested, and
refined in production environments. Finally, LLMs must be integrated into a real-time microservices fabric on a
platform that brings all these together so that teams can collaborate across domains such as MLOps, security,
compliance, and DevOps. Next practices include model versioning, canary deployments, rollback strategies, and
model evaluation frameworks. They also needed to sustain datasets and benchmarks for the explicit purpose of
periodically checking if the model itself is accurate, fair, and aligned with institutional policies.
Real-World Use Case: Trade Surveillance and Fraud Detection
Overview of Trade Surveillance and Its Growing Complexity
The regulatory and operational necessity for trade surveillance in financial markets involves continuously
monitoring trading activity to detect prohibited behaviors such as insider trading, market manipulation (e.g.,
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spoofing or layering), front running, wash trades, and other illicit activities. Financial institutions must comply with
regulations like MiFID II, Dodd-Frank, and SEC Rule 613, which require timely reporting, the creation of audit trails,
and proactive anomaly detection. As trading volumes rise and algorithms increasingly take over execution, legacy
monitoring tools struggle to scale, maintain speed, and provide interpretability. The shift to AI-powered tools, which
offer faster and more scalable analysis, is becoming essential to meet these challenges and ensure compliance
(Karwa, 2023).
Existing batch-based surveillance tools are no longer sufficient. These systems are often unresponsive and suffer
long processing delays, making identifying and mitigating such risks in real time impossible. On the contrary,
streams of Apache Kafka and Apache Spark-based real-time surveillance systems allow financial institutions to track
and view the trades as they occur, alert the authorities of any potentially illegal activities on the go, and escalate to
their compliance officers without any dependencies on the end-of-day processing. Equities, options, futures, FX
pipelines, and, in fact, even crypto rest on these pipelines for some level of fraud detection (Aldridge & Krawciw,
2017).
Architecture of a Real-Time Surveillance and Fraud Detection Pipeline
The first step in a real-world surveillance solution is content ingestion from trade events through Apache Kafka,
which will serve as the central hub. Trading events are published to Kafka topics from various upstream systems,
such as electronic trading platforms, OMS, and exchange feeds. They are associated with metadata that usually
contains order ID, trader ID, asset type, price, volume, timestamp, and execution venue. Apache Spark Structured
Streaming jobs subscribe to these Kafka topics, and real-time transformations, filters, and aggregations are applied.
Incoming Trades can join with static reference data
—
trader profiles, risk thresholds, and regulatory watch lists
—
which might be stored in Delta Lake or in-memory tables.
The enrichment steps involve calculating the trading velocity, cross-checking the trade patterns across the markets,
and correlating the move with a price. For example, a Spark job can track if a trader is sending and then canceling
large orders to wantonly deceive other market participants (an act called spoofing) (Lin, 2016). During a sliding time
window (e.g., 30 seconds), order placements, cancellations, and executions would be aggregated, compared to
historical norms, and deviation scores created (30 seconds, but it can be any duration, e.g., half hour, quarter hour).
When suspicious patterns are identified, the enriched and flagged data is routed back to Kafka or pushed to an alert
management system or fraud dashboard. The compliance teams can then investigate or escalate the issue. Events
are stored in a secure data lake for forensic analysis, compliance audits, and regulatory reporting.
Table 8:
Components of Trade Surveillance Architecture
Component
Function
Kafka
Ingests trade events
Spark Streaming
Analyzes behavior in real-time
LLM Microservice
Adds context and human-readable summaries
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Component
Function
Compliance Dashboard Displays alerts and audit logs
Role of LLMs in Enhancing Alert Triage and Explanation
Alert fatigue is one of the major challenges in surveillance systems because compliance officers are inundated with
false positives. Large language models (LLMs) contribute strongly by providing context, filtering the noise, and
producing human-readable explanations of alerts. When Spark raises suspicions of suspicious activity, which can be
mapped to a specific trade context, the trade context (such as the trader history, time series trade data, and peer
behavior, for example) can be sent through a secured microservice API to an LLM. Finally, the LLM generates a
concise explanation like:
Trader 8921 executed 54 big orders for Stock ABC within 3 minutes, canceling 97%. Behavior patterns from historical
spoofing behavior in past disciplinary issues." Compliance teams can then prioritize the issues that must be
addressed right away with the help of this explanation, which is stored along with the alert. LLMs can also auto-
generate Suspicious Activity Report (SAR) drafts summarizing the incident timeline and regulatory implications. By
eliminating manual documentation, firms can speed up compliance workflows and allow human investigators to
spend more time on high-value cases. LLMs may also assist in surveillance log natural language querying. For
example, a compliance officer may ask, 'Give me all trades performed by employees whose trades were more than
5% away from the average spread during the last earnings window.' Intuitive and rapid stream data investigations
can be performed, translating the query into structured Spark SQL or Elastic search syntax and executing across the
streaming data (Norrhall, 2018).
Benefits, Challenges, and Regulatory Readiness
Real benefits are to be found in implementing a real-time, AI-powered surveillance system. It first cuts down on
time-to-detection significantly and thus enables institutions to detect illicit behavior early enough before it can
grow into legal or financial consequences. Second, it builds up the regulatory confidence of regulators by showing
that the institution has a mature, forward-leaning array of monitoring tools that align with the supervisory
expectations. Third, it enables operational scalability: the ability to ingest and process millions of events per second
and not impact performance as data volumes scale. Deploying such a system, however, was not simple. Tuning
Kafka and Spark configurations, proper partitioning strategies, memory settings, and checking for checkpoint
intervals are needed to ensure low-latency performance under high throughput, especially during peak market
hours. Institutions must also address data governance concerns for certain Trader data, where sensitive data should
be encrypted, access audits should be conducted, and pipelines should comply with GDPR and regional data
protection laws.
From a compliance perspective, systems must ensure that their audit trails and the audit trails of the data sources
used to generate the system are transparent and auditable to determine how the alert was generated, what data
was used, and how the system figured out the risk score or recommendation. But this is where explainability tooling
like prompt logging for LLMs and lineage tracing for Spark transformations becomes important. Scenario simulation
tools are also useful to organizations. These allow compliance teams to run surveillance logic in the past to calibrate
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thresholds, mitigate false positives, and measure model performance over time. As compliance writing requires
increasingly powerful AI and automation, and regulators scrutinize AI and automation in compliance more and more
closely, it is important to have these simulation, documentation, and override mechanisms because the system has
to be powerful but also defensible.
Figure 10:
How AI in Surveillance Systems Enhances Safety
Performance Optimization and Monitoring
Importance of Performance in Financial Data Systems
Performance in financial systems is not just a matter of speed; it is a competitive advantage, operational continuity,
and regulatory compliance. To meet these needs, processing high workloads must be low-latency, high-throughput,
and fault-tolerant. Microseconds matter greatly if engaged in high-frequency trading (HFT). If streaming is doing
the fraud detection, like in most cases, and there is a delay in stream processing, that could cause missing the
mitigation windows. Also, regulatory reporting might not be reported in a timely fashion, thus causing a violation
or an expensive audit.
These systems are now limited by something other than raw hardware. This means tuning distributed systems (like
Apache Kafka, Apache Spark, and their surrounding pieces) to work in perfect concert under the real-time demands
of ingesting, processing, and delivering data (Friedman & Tzoumas, 2016). This requires modifying memory
parameters and balancing workloads among partitions and executors while closely monitoring end-to-end from
event generation to actionable insight. However, performance optimization is tied to the organization's overall
nonfunctional requirements. These include RTOs, RPOs, throughput SLAs, and system availability guarantees. With
real-time systems matured and still maturing, performance engineering becomes an ongoing process backed by
various tools (monitoring, profiling, and observability).
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Kafka Optimization: Partitioning, Throughput, and Latency
Most modern event-streaming architectures are based on Apache Kafka
—
the first area to focus on for Kafka topic
partitioning is to optimize it for financial workloads. There should be an appropriate partitioning of Kafka topics that
support the system's parallelism needs, such as customer ID, transaction type, region, or asset class. There is an
increase in the number of partitions, which means there are more consumer threads and, thus, more parallel
processing. However, this is still prone to over-partitioning, which results in increased replication overhead and
controller strain; therefore, sizing must be tested against workload patterns. Another performance lever is Kafka's
replication factor. The standard in production is a replication factor of three for durability and fault tolerance.
Nevertheless, replication introduces write amplification, and IOPS capacity must be considered when designing
storage. Throughput is affected by Kafka batching and lingers.MS settings because larger batch sizes reduce
overhead; however, too much buffering will slow down the process. Compression (Snappy or LZ4) improves
network utilization without much CPU cost.
Tuning producers and consumers is very important. First, if exactly one delivery is desired, producers must be made
idempotent, and acks=all chosen for durability. Latency and efficiency are balanced by consumers with
max.poll.records and fetch.min.bytes. Stateful Kafka running in Kubernetes stacks requires tuning clusters for
persistent volume performance and node affinity to reduce cross-rack traffic and minimize the latencies and fault
domains. Kafka's consumer lag, the offset difference between the latest committed message and the one being
processed, is an essential metric for monitoring pipeline health. Therefore, if lag grows, it can signal a bottleneck
downstream (Spark) or upstream pressure (trade bursts). Prometheus or Datadog will monitor and track lag metrics
by topic and partition and visualize them in Grafana dashboards for alerting and trend analysis.
Figure 11:
Performance tuning
—
Apache Kafka
Spark Optimization: Memory, Processing Modes, and Batch Intervals
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Apache Spark's real-time engine, Structured Streaming, processes data in either micro-batch or continuous mode
(Awan et al., 2016). This feature is, by default, a micro-batch, where the data will be automatically grouped into
very short intervals (e.g., 1s, 5s) and processed as discrete batches. The latency vs. compute cost should be tuned
so that the batch interval is not set too big to sacrifice lag and not too small to incur too much computation cost,
i.e., bounded from below and above. Increasing the sizes of the intervals results in more latency, but it reduces
scheduling and shuffle overhead. For use cases that need ultra-low latency, Spark's continuous mode provides sub-
second processing with some restrictions on operations such as joins and aggregations.
Another essential optimization is memory tuning. Based on workload characteristics, Spark's executor memory,
shuffle partitions, and garbage collection parameters (such as Spark memory fraction, Spark executor memory, and
Spark shuffle compression) should be adjusted. Through Tungsten and Catalyst optimizations, Spark can execute
and rewrite query execution plans using vectorized memory formats and columnar storage. However, stateful
streaming operations (windowed joins and aggregations) are configured to require periodic configuration of state
store backends, timeout thresholds, and checkpoint intervals. DPCExecutor crashes can occur due to improperly
configured state stores, which can increase checkpointing time. State management is often done with Delta Lake
or RocksDB.
It is important that Spark can handle backpressure when the sources send data faster than it can process (as in the
case of Kafka). Backpressure mechanisms allow Spark to adjust the rate at which the inputs are read dynamically,
and the streaming UI gives insight into the input rates, processing rates, and batch processing times. Model
serialization formats (e.g., ONNX or PMML) and lazy evaluation optimizations help guarantee that inference speed
doesn't cross real-time boundaries for machine learning inference pipelines. When integrated into streaming DAGs,
Spark MLlib is suitable for batch training but not so much for real-time training; however, it can score pre-trained
models.
Observability: Metrics, Logs, Traces, and Alerts
Flying blind without observability is performance optimization. In real-time systems, end-to-end observability is
required from ingestion, processing, and storage to API layers. It is also widely used to collect metrics from Kafka
brokers, Spark drivers, Kubernetes pods, and hardware infrastructure. These include CPU, Memory, Disk I/O,
Consumer Lag, Record Throughput, Job Duration, and garbage Collection activity. Grafana dashboards visualize
these metrics in real-time, making them operationally clear for engineering and compliance teams. They have been
identified as Kafka topic lag, Spark batch duration, checkpoint delay, executor memory pressure, and JVM health.
The metrics can be tagged by environment (prod/dev), workload type (HFT, AML), and service owner.
Logs are then funneled and stored in Elastic Search or OpenSearch using tools like Fluent for structured logging with
Logstash or Vector. Normal logging levels (INFO, WARN, ERROR) should be fine-tuned to block log floods. In post-
incident forensics in financial systems, transaction IDs, Kafka offsets, and data lineage identifiers would be required
within logs. Frameworks like OpenTelemetry, Jaeger are distributed tracing frameworks that allow users to visualize
how an event flows from a Kafka producer through Spark transformations to microservices. This is crucial for
diagnosing latency spikes, dropped records, or inconsistent output across downstream systems. Alerting systems
like PagerDuty, VictorOps, or Slack-integrated bots can start notifying the teams about anomalous behavior, such
as raising lags, dropped records, or failed checkpoints. The basis of these alerts should also be actionable,
contextual, and tiered by severity. Plugging these into a DevOps or SRE workflow guarantees these problems make
their way into triage and get a resolution before negatively affecting compliance or customer trust.
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Future Trends: AI-Native Financial Systems at the Edge
The Shift toward Edge Computing in Finance
The next frontier for financial institutions in their pursuit of ultra-low-latency processing and customer-centric
innovation is edge computing
—
computing closer to the data source or user. On the Edge, data is processed on or
near devices like ATMs, point of sale (POS) terminals, mobile apps, or local trading gateways, and later sent to
centralized systems. It minimizes reliance on round trips to the cloud and data center, reduces Latency, allows high
bandwidth use, and increases system resilience. In capital markets, Latency can be a profit or loss. A timely example
is high-frequency trading (HFT): executing a trade from a Tokyo-based device routed through a New York center
introduces pairs of milliseconds of Latency, which is otherwise unacceptable. One can place edge logic (e.g., risk
control) locally on a smart gateway or co-location servers by deploying Edge.
Edge computing provides instant context-aware financial services for retail banking and insurance. Edan explains
that mobile devices or branch-level infrastructure can support loan approvals, credit scoring, or fraud alerts in near
real-time. In areas of spotty connectivity, it is especially helpful to have local processing to avoid interruptions when
the network goes down. With the adoption of 5G networks, IoT-enabled financial hardware, and partially
containerized edge runtimes like K3s and MicroK8s, edge native deployment is more practical than ever. Container
images can be updated in the edge nodes, orchestrated remotely, and monitored centrally, creating the same
performance anywhere (Casalicchio & Iannucci, 2020).
Figure 12:
Edge computing
Federated and Privacy-Preserving AI at the Edge
Federated learning is one of the most promising advancements in AI in Native Finance. It enables the creation and
running of the model at decentralized data sources without aggregating raw data to a central location.
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Centralization may bring privacy, compliance, or transmission challenges for centralized training, which is the
premise of traditional machine learning. Federated learning allows training models in edge devices through local
data, and the model update (not raw data) will be shared with the central coordinator.
In regulated financial environments, where customer data has to be held within boundaries set for jurisdiction or
corporation, this is even more important than in general. A multinational bank with EU, U.S., and Asian customers
can use a federated model in which the training data does not violate GDPR and other local data sovereignty laws
and is learned from each region differently. Although federated learning is a tremendous research topic, it also
includes several privacy-preserving AI techniques, such as differential privacy, homomorphic encryption, and secure
multi-party computation (SMPC), to ensure that even intermediate computations do not leak sensitive information.
With these tools, an app that wishes to offer hyper-personalized financial services, such as investment advice or a
credit offer, doesn't have to gather user data centrally. LLMs can be deployed to the Edge for customer interaction,
document summarization, and form processing to take place also at the Edge (Elazhary, 2019). This decreases its
reliance on cloud APIs, speeds up the inference time, and prevents any data leakage while sending. Nowadays,
lightweight transformer models like Distil BERT or TinyGPT are being optimized for on-device/near-device inference
while making real-time intelligent microservices at the Edge a reality.
Table 9:
Federated AI in Finance: Key Concepts
Concept
Description
Federated Learning
Train models without moving data
Differential Privacy
Adds noise to protect individual info
Secure Multiparty Computation Computes securely across multiple parties
On-device Inference
Run AI models on edge/mobile devices
Decentralized Finance and AI-Enhanced Web3 Systems
DeFi meets real-time AI systems on another frontier. Maintained by Wyn on Medium. Currently, the focus is on
crypto assets, lending, and liquidity pools. Still, with time, this gradually shifts (and is being moved) to AI-infused
DeFi protocols that can leverage risk signals, sensation, or behavioral analytics. An AI agent may monitor real-time
blockchain transactions and identify behaviors associated with fraud, insider trading, or front-running. Chain-link,
for example, can be one of these agents that interacts with an Oracle system to incorporate real-world market data
and execute governance proposals or risk adjustments into smart contracts.
AI adoption within decentralized autonomous organizations (DAOs) may lead to adaptive treasury management,
automated staking strategies, or liquidity migration, depending on real-time risk analytics. This enables the
deployment of AI models as smart contract modules as well as sidecar services on the chain. Nonetheless,
decentralized AI systems present new challenges regarding model transparency, mitigating bias, and execution
traceability. Future regulatory frameworks of crypto-integrated finance will focus on AI decisions' audibility,
explainability, and reversibility in the decentralized network.
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Figure 13: DeFi common mechanism and revenue strategy
Regulation Technology (RegTech) and Explainable AI for the Future
With the increase in demand for the growing autonomous RT systems, the demand for regulatory technology
(RegTech) will increase significantly (Von Solms, 2021). In other words, RegTech is the combination of AI-driven
solutions that streamline the regulatory requirements imposed on financial institutions in real-time. These tools
include reporting market abuse, automating AML and KYC, generating AI-based decision-making, and testing to
implement new regulations. At the same time that LLMs and real-time inference pipelines proliferate, regulatory
bodies are starting to expect explainability and governance controls to be just as much integrated into the system
design. Model cards, data sheets, and prompt logs are becoming more common in this changing field. These
artifacts ensure transparency and accountability for a model: what it is intended to do, on what scope, what risks
are involved, and what usage constraints apply.
Future systems will use policy engines to work side by side with machine learning models to ensure that AI outputs
do not violate compliance requirements, ethical standards, or jurisdictional boundaries. In the real world, the
middleware layers inspect the API response, tag output to be auditable, and log execution with the decision's
provenance. These are required to build trust in and oversee AI systems. Additionally, continuous assurance engines
that monitor model drift, assess fairness metrics, and evaluate the alignment with regulators in different regions
are supposed to be incorporated into advanced RegTech platforms. It is unclear what roles the financial institutions
should play as AI is progressively embedded across customer touchpoints, from onboarding and transactions to risk
management and customer support. Still, it is likely that in the future, financial institutions will be called upon to
explain how AI decisions are made, who should be responsible for such decisions, and what governance frameworks
are set up. LLMs, blockchain-native finance, and edge computing are likely to change, but the regulatory perimeter
will follow suit (Satyanarayanan, 2017). The institutions that will be at the forefront of financial innovation will be
those that actively invest in explainable, transparent, and well-governed AI systems so that they will excel in this
aspect and be best prepared for the fast-growing needs of regulators and the public.
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Conclusion and Strategic Recommendations
This article has dug into the formidable interplay between real-time data streaming, cloud-native architecture, AI-
powered analytics, and the role that Apache Kafka and Apache Spark play in making it happen. As the world
becomes a faster-paced place crowned by regulatory pressure that never seems to let up, legacy batch processing
systems will not do anymore. Institutions today must conduct large-scale transactions, behavioral data, and trading
activity, and do so quickly, precisely, and transparently.
Featuring high throughput, fault tolerance, and a capability to ingest millions of messages per second from
anywhere, from ATMs and trading desks to customer-facing apps and market feeds, Apache Kafka provides a strong
backbone of an event capable of taking in those messages. Built on Spark Wide and Big Data Streaming, Spark
Structured Streaming completes this by offering real-time analytics, aggregations, joins, and even AI-powered
inference at scale. These technologies form the basis for scalable, responsive, and secure event-driven
architectures. With Kubernetes for orchestration, generative AI microservices for dynamic interpretation, and
robust observability tooling, these systems can enable intelligent, resilient, and compliant financial workflows.
Modularity, security, and performance require a disciplined approach to such systems. Kafka topics must be logically
organized, access must be tightly controlled, and schemas must be governed for compatibility and auditability. To
achieve data accuracy and data recoverability, Spark applications must be designed in a fault-tolerant, properly
checkpointed, and lineage-tracked way. TLS in transit should be encrypted, RBAC enforced, and runtime policies
should be checked with service meshes and other tools (such as Open Policy Agent). Place LLMs and AI services as
stateless microservices behind APIs that are to be deployed elastically and with explainable and traceable
interactions.
Infrastructure such as code and GitOps practice for developers and architects provides reproducibility, automation,
and secure, tested code deployment via dedicated DevSecOps pipelines. Continuous performance testing and
capacity benchmarking must be baked into release cycles to find constraints early. Real-time monitoring systems
must keep track of consumer lag, custom job durations, memory usage, and model inference time to alert at the
incident stage. The transition to AI-native, real-time financial systems is a technical evolution and a strategic
opportunity at the business and compliance levels. As clients demand instant confirmations, ninja support, and
personalized services, and institutional ones fail to deliver, fintech competitors are fast closing any gap in
satisfaction. Real-time infrastructure investments must be guided by tangible value results related to reducing fraud
losses, speeding compliance cycles, improving customer satisfaction, and improving risk controls.
To help determine data science execution readiness, Executive leaders must align the multitude of teams
(compliance, data science, engineering, and security) and promote collaboration between the business growth
teams, ensuring that real-time analytics serve both business growth and regulatory obligations. According to such
models, institutions must, among other things, implement model audit trails, prompt logs, and explanation
dashboards to demonstrate transparency and fairness in AI-driven decisions. As the AI regulations are tightening in
Europe, the United States, and around the globe, it is now about future-proofing the infrastructure in the long term
to ensure long-term agility and credibility.
By design, the most competitive financial institutions will be AI native, where a lot of competitive advantage is
generated. Kafka-driven streaming, Spark distributed analytics, domain-specific LLMs, and Kubernetes native agility
will be combined to enable intelligent, secured services across mobile, edge, and web interfaces. With built-in
observability, explainability, and compliance automation, these platforms won't just react to market events; they
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will proactively anticipate them and deliver dynamic credit decisions, real-time ESG insights, algorithmic portfolio
adjustment, and conversational client interfaces. The tools adopted will not determine success in this new era, but
the capabilities developed. Teams also need to be upskilled, operating models need to be changed, and a culture
of experimentation, governance, and AI deployment needs to be created. Making forward-looking, strategic
architecture decisions today will enable financial firms to unlock a decade of innovation, efficiency, and trust, but
systems that are faster, smarter, and above all, more resilient, transparent, and human-centered.
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