AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
9
INTERNATIONAL JOURNAL OF BUSINESS AND MANAGEMENT SCIENCES
(ISSN: 2693-3500)
Volume 05, Issue 05, 2025, pages 09-26
Published Date: - 21-05-2025
Doi: -
https://doi.org/10.55640/ijbms-05-05-02
Transforming Loan and Distribution Processing in Retirement
Systems: A QA Automation Approach
Sanjay Kumar Das
Independent Researcher
ABSTRACT
Providers of retirement plans are facing increasing pressure to modernize loan and distribution processing in
401(k) and other retirement systems to improve efficiency, accuracy, and compliance. Legacy processing
environments often mean manual processes and fragmented legacy systems, which mean long turnaround times
and a high error rate. This paper proposes a Quality Assurance (QA) automation approach to update these critical
processes. We design and implement an automated test and processing framework that leverages behavior-driven
development and state-of-the-art test automation tools to enable straight-through processing and comprehensive
verification of loan and distribution transactions. The method was applied to real retirement system environments
with much shorter processing cycles and quasi-elimination of processing errors. Results demonstrate that
automation-driven modernization can provide 50% faster processing, improved compliance with regulatory
requirements, and improved participant satisfaction. The findings of the research have significant implications for
the modernization of financial technology, illustrating how QA automation can bridge the gap between legacy
retirement platforms and the demands of contemporary financial services, while ensuring robust compliance and
reliability.
KEYWORDS
Retirement Plan Loan Processing, CARES Act Distribution Adaptation, Financial Services Automation, Risk
Management in Retirement Systems, OmniScript Integration, Mainframe Testing, Legacy System Modernization,
Straight-Through Processing
INTRODUCTION
Retirement plan systems in the United States manage enormous volumes of transactions and assets. As of 2022,
401(k) plans held over
$7 trillion
in assets for roughly
70 million
active participants [1]. Among the transactions
supported by these systems, participant
loans and distributions
are particularly important and frequent. Most
401(k) plans allow participants to take loans from their retirement accounts
–
at year-end 2022 about
84%
of
participants were in plans offering loans
–
yet roughly
15%
of eligible participants had a loan outstanding [1] at any
time, translating to millions of loan origination and repayment events annually. Similarly, distribution processing
(e.g., withdrawals, rollovers, and retirement payouts) affects virtually every plan participant over the plan lifecycle.
Ensuring these transactions are processed efficiently and correctly is mission-critical, given the fiduciary stakes and
regulatory oversight in retirement systems.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
10
Despite the criticality of these processes, many retirement plan administrators still rely on
legacy IT systems
and
manual procedures
that struggle to meet modern demands. Industry analyses have highlighted that plan loan
processing errors are a common occurrence
–
participant loan issues rank as the
#9 operational failure
in the IRS
401(k) compliance “Fix
-
it” guidelines [2].
Such errors can lead to loan limit violations or missed repayments,
triggering tax penalties and requiring complex corrections. More broadly, outdated recordkeeping technology and
fragmented processes impede efficiency. A McKinsey study of retirement recordkeepers noted that achieving scale
is difficult with aging systems “with extensive
deferred maintenance
and a prevalence of manually intensive
processes,” and that evolving client expectations are
driving investment in digital capabilities
to modernize these
systems [4]. In the wake of events like the COVID-19 pandemic, it became evident that legacy platforms could not
adapt quickly to sudden rule changes (e.g., special CARES Act withdrawal provisions), as
“laws can change
unexpectedly” and firms might
“not get months to implement these changes”
[3]. These factors have created an
urgent impetus to modernize loan and distribution processing in retirement systems.
This paper addresses the modernization challenge by focusing on
quality assurance automation
as a catalyst for
process improvement. Rather than attempting a risky “big bang” replacement of legacy systems, we advocate an
incremental approach that uses automated testing and process validation to overlay and improve existing systems.
By employing
QA automation tools
and frameworks, organizations can rapidly introduce enhancements (such as
rule changes, new web interfaces, or batch process optimizations) with a safety net of automated regression tests
ensuring that critical loan and distribution functionalities remain correct. The approach combines
behavior-driven
development (BDD)
techniques with robust test automation to achieve what the industry envisions as
straight-
through processing
–
i.e., end-to-end automation from request initiation to completion with minimal manual
intervention [3]. Early indications show that such an approach can dramatically reduce processing times and errors:
for example, intelligent automation has been shown to
accelerate loan processing cycles by 50%
while reducing
operational costs [7].
In the following sections, we provide a detailed overview of the legacy challenges impeding efficient loan and
distribution handling, and then present the tools and framework of our QA automation approach. We describe the
methodology used to implement and integrate the automation framework within existing retirement plan systems.
Two case studies are discussed, demonstrating the application of the approach to a 401(k)
loan processing
modernization
and to a rapid
distribution process update
in response to regulatory change. We then present
results and discussion, highlighting performance improvements and comparing our findings with industry
benchmarks. Finally, we conclude with broader implications, including how QA automation can inform future
retirement system modernization efforts and the potential policy and industry impacts of more automated, reliable
retirement operations.
2. METHODOLOGY
Our modernization methodology centers on introducing a
QA automation framework
into the retirement plan’s
loan and distribution processing pipeline. The goal is to systematically
identify, automate, and validate
all critical
business rules and workflows associated with these processes. Figure 1 outlines the key phases of our approach,
which are further detailed below:
2.1 Process Analysis and Requirements Capture:
We began by documenting the legacy loan and distribution
processes, including all business rules (e.g., IRS limits such as IRC §72(p) loan limits, age-based withdrawal rules)
and pain points (error-prone steps, bottlenecks). This involved collaboration with business analysts and operations
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
11
staff to ensure the automation would cover real-world scenarios. We identified typical use cases (e.g., initiating a
new loan, processing a loan payoff, taking a partial distribution) as well as edge cases (violation of loan maximums,
multiple loans, hardship withdrawals with special tax withholding) to shape our test scenarios.
2.2 Behavior-Driven Test Design:
Using the insights from Phase 1, we adopted a
Behavior-Driven Development
(BDD)
approach to design test scenarios. BDD allowed us to write scenarios in plain language
(“Given
-When-
Then”
format) that describe the expected behavior of the system under various conditions [8]. For example, a loan
scenario was written as:
“Given a participant with su
fficient balance, when they request a loan of X with Y terms,
then the system approves and sets up repayments correctly.”
These scenarios, written in a domain-specific language,
serve as both specifications and automated test cases. The BDD approach enhances collaboration
–
stakeholders
from IT, QA, and business could all review the scenarios for completeness and accuracy, ensuring the tests reflect
business expectations. Each scenario was mapped to test scripts (step definitions) in the automation framework.
2.3 QA Automation Framework Implementation:
We implemented the tests using a combination of
open-source
automation tools
and custom scripting. In particular, we leveraged
Selenium WebDriver
to automate browser-
based interactions with the retirement system’s user interface, mimicking the actions of a user or administrator
processing loans/distributions. This allowed us to verify that the front-end and back-end were working together as
expected for each scenario (for instance, submitting a loan request through the web portal and checking the
resulting database entries or confirmations). To structure the automation code, we employed the
Page Object
Model (POM)
design pattern, which is a widely used approach in test automation to enhance maintainability and
reduce code duplication [9]. Each page or screen (e.g., loan request form, approval screen, distribution calculation
page) is represented as an object with methods to interact with it, making the test scripts more readable and easier
to update if the UI changes. Our framework was built primarily in Java with JUnit for organizing test cases, and
integrated with Cucumber for tying the BDD scenarios to the Selenium-based steps. We also included modules for
API-level testing (invoking backend services for loan interest calculation and tax computation) to verify logic that
might not be visible through the UI.
2.4 Tool Integration and Data Management:
A critical aspect of the methodology was integrating supporting tools
for a complete QA ecosystem. We set up a
Continuous Integration (CI)
pipeline using Jenkins to automatically run
the full suite of automated tests on a scheduled basis (nightly) and on-demand (e.g., triggered by code changes or
configuration updates). This ensured that any new software release or configuration change in the retirement
system would be validated against the full array of loan and distribution scenarios immediately. We also addressed
test data management
–
creating and managing participant accounts and plan configurations needed for
repeatable test execution. In financial systems, realistic test data is essential; however, using production data can
raise privacy and compliance concerns. We employed data masking and synthetic data generation techniques so
that our tests could run on anonymized yet realistic datasets (aligning with financial industry practices to protect
customer data [6]). For example, participant personal details were scrambled, but their account balances and loan
histories were representative. We found
that careful test data design was necessary to avoid the “test environment”
being a limiting factor; according to industry reports, roughly
46% of financial firms struggle with test data
management in compliance with regulations
[6], so our framework treated this as a first-class concern.
2.5 Pilot Deployment and Iteration:
We initially piloted the QA automation approach in a controlled test
environment that mirrored the legacy system. The pilot involved running the automated suite in parallel with
traditional processing for a subset of transactions. This allowed us to validate the accuracy of the automation
(ensuring no false positives/negatives in test results) and to fine-tune performance. We iteratively improved the
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
12
test scripts for stability
–
for instance, adding synchronization waits for background jobs and enhancing our
verification logic to account for acceptable variances (such as minor rounding differences in interest calculations).
Once the automated tests consistently passed and demonstrated alignment with expected outcomes, we
progressively expanded the coverage to all loan and distribution transactions and integrated the automation into
the main software development lifecycle.
Throughout the methodology,
formal QA principles
guided our decisions. By using BDD and page object patterns,
we ensured the solution remained
extensible
and
maintainable
even as underlying systems evolved. The choice of
widely adopted tools like Selenium was motivated by the need for reliability and community-proven support
(Selenium can replicate complex user interactions in the browser, which was essential for our web-based
interfaces). Moreover, aligning the test design with business language via BDD meant that the test cases served as
up-to-date documentation of the retirement syst
em’s intended behavior, reducing the communication gap
between pension administrators and IT teams.
3. LEGACY CHALLENGES
Modernizing loan and distribution processing requires first understanding the
legacy challenges
that impede
efficiency and accuracy. In our study of the retirement system’s current state, we i
dentified several key issues
common to legacy retirement recordkeeping platforms:
3.1 Rigid Legacy Systems:
The core recordkeeping platforms for many retirement plans are built on decades-old
technologies (often mainframe-based) with tightly coupled business logic. Even a simple change
–
for example,
updating the maximum loan amount or altering a withdrawal rule
–
can require modifications deep in monolithic
code. These changes necessitate expertise in outdated programming languages and systems. As a result,
enhancements are slow and expensive to implement. Industry observers note that many providers operate with
“legacy mainframe systems in which business logic and code are tightly intertwined,”
such that adjusting a rule (like
a penalty-free withdrawal limit) demands specialized mainframe developers and significant effort [3]. This technical
debt leads to what McKinsey described as
“deferred maintenance”
–
necessary improvements deferred due to
system complexity
–
which accumulates over time [4].
3.2 Manual Workflow and Silos:
A significant portion of loan and distribution processing is still
manual or semi-
manual
in legacy environments. For instance, participants may submit loan requests on paper or through basic web
forms that generate back-office tasks. Administrators then manually review requests, enter data into separate
systems (e.g., payroll and recordkeeping not fully integrated), and trigger payments or tax withholdings. These
multi-
step manual processes introduce delays and opportunities for error. It’s no
t uncommon for a loan request to
take several business days to fulfill due to hand-offs and validations performed by staff. During peak periods or
special events, these teams can be overwhelmed
–
they are typically staffed for steady-state volumes, not surges.
One provider noted that
manual processing can cause backlogs
whenever request volumes spike (for example,
during economic downturns when more participants take loans or emergency withdrawals), and that legacy
systems offer little support for remote or automated work to handle the overflow [5]. In summary, the lack of end-
to-end automation (also called
straight-through processing
) in legacy setups means each transaction needs human
intervention at multiple points, slowing it down considerably.
3.3 Error Prone Operations:
With manual steps and complex rules, errors in loan/distribution processing are a
persistent risk. Mistakes can range from simple data entry errors (typos in the loan amount or a distribution payee
address) to misapplication of rules (allowing a loan above the permitted limit, or failing to withhold taxes correctly
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
13
on a distribution). Such errors carry significant consequences in the retirement context. Loan processing errors, for
example, can lead to
compliance violations
under IRS regulations. If a participant loan exceeds the maximum
allowed or isn’t paid on schedu
le, it might be deemed a distribution, triggering taxes and penalties for the
participant. The IRS has documented that plan loan errors are a common issue requiring correction programs [2].
Even when not rising to the level of reportable violations, operational errors cause rework and participant
frustration. Missing a loan payment due to an administrative oversight requires issuing arrears notices and
scrambling to collect payments to avoid default. Similarly, mistakes in distribution processing (e.g. calculating the
wrong withholding or failing to distribute by a required deadline) can violate regulations and necessitate costly
fixes. In one industry whitepaper, it was noted that well-designed automation
“can eliminate overpayments or
missed payments resulting in retroactive corrections and associated fines,”
highlighting how prevalent those errors
are when processes rely on manual administration [2, 3].
3.4 Scalability and Cost Concerns:
Legacy processes do not scale well to growing plan sizes and transaction volumes.
The defined contribution market has been growing (assets and participant counts rising annually), and providers
must service more transactions with the same or smaller staff. However, manually intensive processes scale linearly
with headcount
–
a model that is both costly and impractical. Margins in recordkeeping business are thin, pushing
providers to do more with less [4]. Without automation, adding more plans or participants means nearly
proportional increases in operational workload. This lack of scalability also shows up in the IT maintenance costs:
supporting old software consumes a large share of IT budgets, leaving few resources for innovation. For example,
in the financial services sector, firms historically have spent an estimated
30
–
40% of IT budgets on testing and QA
,
much of it on labor-intensive manual testing [6]. This reflects how keeping legacy systems running (and bug-free)
can drain resources. Moreover, multiple siloed systems (one for loans, one for distributions, separate payroll, etc.)
mean duplicate data entry and reconciliation efforts, further driving up costs. These challenges underscore that
without modernization, retirement providers face escalating costs and complexity to meet participant needs.
3.5 Suboptimal Participant Experience:
Today’s plan participants (and sponsors) expect digital convenience on par
with consumer finance platforms. Legacy loan and distribution processes often fall short: participants might have
to fill out physical forms or endure long wait times for approval and funding. There may be no self-service portal to
model different loan scenarios or initiate a distribution with real-time guidance. Communications about status are
slower and less transparent. All of this contributes to a poor user experience and potential dissatisfaction. In
contrast, a modern approach would allow participants to initiate transactions online with instant validations (e.g.,
the system immediately flags if a requested loan amount is above the allowable limit), electronic signatures, and
timely updates. The gap between these expectations and reality is a strong motivator for providers to modernize.
By addressing the above legacy challenges, plan administrators aim to provide
self-service and near-instant
processing
for routine transactions, which has become the norm in other areas of financial services.
These legacy challenges provided clear targets for our QA automation initiative. The next sections describe how the
chosen tools and framework directly tackle these pain points
–
automating repetitive tasks to eliminate manual
errors, encoding complex rules to ensure compliance, and enabling faster end-to-end processing. By doing so, the
framework brings the organization closer to a modern, resilient loan and distribution processing capability.
4. TOOLS
Successfully implementing QA automation for retirement systems requires a carefully chosen toolset that addresses
web automation, test case management, data handling, and continuous integration. In this project we integrated
several
tools and frameworks
, each serving a specific role in the overall solution:
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
14
4.1. PCOMM and Omni Suite Integration:
IBM Personal Communications (PCOMM) is a key enabler in automating
mainframe-based interfaces by providing a programmable 3270 terminal emulation layer. In our QA automation
approach, PCOMM interacts with the OmniPlus recordkeeping system
—
developed by FIS
—
which handles core
participant record administration, including loans and distributions. PCOMM’s EHLLAPI interface allows test
automation frameworks to simulate real-time data entry, navigation, and validation over green screen sessions,
which are native to Omni’s user interface. The Omni Suite, composed of OmniPlus and supporting tools like
OmniScript, executes the business logic and batch processing associated with retirement transactions. Test
automation integrates with these systems by using PCOMM to drive front-end interactions (e.g., initiating loans)
and OmniScript to simulate backend scenarios and validate business rules. Batch operations triggered via JCL enable
validation of scheduled tasks, such as loan amortization or distribution withholding, ensuring that all aspects of
Omni’s processing can be verified end
-to-end. This tight integration enables comprehensive regression testing,
particularly important in a retirement industry context where rule complexity is high and transaction accuracy is
critical.
4.2 Selenium WebDriver:
We used Selenium WebDriver as the core engine for automating web interface
interactions. Selenium is an open-source framework that
automates web browsers by simulating real user actions
(clicking links, entering text, submitting forms, etc.). This choice was natural given that the retirement system
provided a web-based interface for loan requests and distributions. By scripting Selenium to perform the same
steps an administrator or participant would, we could verify UI functionality and the correctness of workflows
without human effort. Selenium’s compatibility with multiple browsers ensured our tests covered different client
environments. In addition, the rich Selenium ecosystem allowed integration with our BDD layer and the use of
design patterns like Page Object Model for better code organization.
4.3 Cucumber (BDD Framework):
For bridging the gap between business requirements and automated tests, we
employed Cucumber, a popular BDD framework. Cucumber allows writing test scenarios in plain English using a
syntax like
“Given
-When-
Then”
. These feature files act as executable specifications. We chose Cucumber because
it enables collaboration with non-developers
–
plan administrators and business analysts could read and even
author scenario descriptions, increasing confidence that the tests cover the intended behavior. Under the hood,
Cucumber binds these scenarios to our Selenium step definitions (written in Java). This means when a scenario says
“Given a participant has a plan loan available,”
the automation knows to call the setup routine creating a dummy
participant in the database who meets that condition. The use of BDD with Cucumber thus made our QA process
more
inclusive and transparent
, qualities that are highly valued in an industry where compliance and cross-
functional sign-off are important [8].
4.4 JUnit and TestNG (Test Harness):
We leveraged traditional testing frameworks (in our case JUnit, with some
TestNG concepts) to structure and execute the automated tests. JUnit provided annotations for setup/teardown
and grouping tests, which we used to ensure that each test case (each loan or distribution scenario) started with a
known state and cleaned up after execution. The test harness also handled assertions
–
verifying that actual
outcomes match expected outcomes
–
and reporting. For example, after Selenium simulated a loan issuance, JUnit
assertions would check that the account balance was reduced appropriately, the amortization schedule was
created, and no rule violations were logged. The choice of JUnit was mainly because of its maturity and seamless
integration with build tools and CI servers. It also allowed us to easily tag tests (e.g., smoke test vs. full regression)
and manage dependencies between tests if any.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
15
4.5 Page Object Model (Design Pattern):
Though not a tool per se, the Page Object Model (POM) guided how we
implemented our Selenium interactions. In POM, each page or component of the web application is represented
by a class, encapsulating the locators and operations for that page. We created page object classes for all major
screens
–
LoginPage, LoanRequestPage, ApprovalDashboard, DistributionForm, etc. This encapsulation improved
maintenance: if a web page’s layout changed, we only needed to update the page class, not every test script. The
POM approach is recommended in test automation as it
enhances test maintainability and reduces code
duplication
[12]. We found POM especially useful given the number of fields and steps in a typical loan or
distribution form
–
by reusing page methods (e.g., submitLoanRequest(amount, term)), test scenarios stayed high-
level and readable.
4.6 Jenkins (Continuous Integration Server):
Jenkins was our choice for orchestrating test execution continuously.
We configured Jenkins jobs to run the full suite of automated tests on a nightly schedule and also trigger on any
code check-
ins to the retirement system’s code repository. This CI setup provided r
apid feedback; if a developer
introduced a change that inadvertently broke a loan calculation or a distribution workflow, the automated tests
would catch it within hours and Jenkins would flag the build as failed. The Jenkins dashboard then provided test
reports (via JUnit XML and Cucumber reports) that developers, QA, and management could review. Continuous
integration of tests is a cornerstone of modern QA, ensuring that quality is built into the development process
rather than treated as an afterthought. By integrating with Jenkins, we also laid the groundwork for continuous
delivery
–
the idea that eventually, software updates to the retirement platform could be deployed with confidence
because the automated tests assure quality at each step.
4.7 Test Data and Database Tools:
We utilized a combination of database automation scripts and in-memory data
tools to handle test data. For setting up scenarios, direct database seeding was sometimes used (for instance, to
create a participant with a specific account balance and loan history, we ran SQL scripts before the test scenario
executed). We automated these using Flyway (for managing DB migrations in tests) and custom SQL runners.
Additionally, to verify back-end results, we tapped into the database after test actions; for example, checking that
a new loan record was inserted with the correct attributes after a test scenario ran. On data masking, we integrated
a simple tool to replace sensitive info (names, SSNs) with dummy values in test outputs and logs, aligning with best
practices for data security in QA. While these tools are lower-level, they were crucial in ensuring our tests were
repeatable and did not depend on fragile data conditions.
4.8 Reporting and Logging Tools:
We instrumented the framework with reporting libraries (ExtentReports for HTML
reports of test execution, and log4j for detailed logging). This gave us human-friendly test reports that could be
shared with stakeholders. For instance, a test report would show a scenario narrative (from BDD), each step
executed (with screenshots captured at key points via Selenium), and the pass/fail result with details. If a test failed,
logs and screenshots were attached indicating where the discrepancy occurred (e.g.,
“Expected error mes
sage for
exceeding loan limit not displayed”
). These reporting capabilities greatly sped up debugging and also served as
evidence of due diligence for compliance auditors
–
we could demonstrate that every business rule (loan limits,
withdrawal taxes, etc.) was being automatically tested and verified on every release.
By combining these tools and frameworks, we constructed a comprehensive QA automation environment tailored
to the needs of retirement plan processing. Importantly, all the tools chosen are either open-source or widely
supported, which kept costs manageable and ensured longevity (e.g., community support for Selenium, frequent
updates to Jenkins, etc.). The next section will describe how these tools come together in our
framework
architecture
, enabling end-to-end automation of the legacy processes.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
16
5. Framework Overview
The QA automation framework we developed operates as an
end-to-end test harness and process orchestrator
for
loan and distribution transactions. It interfaces with the retirement system at multiple levels
–
web UI, database,
and API
–
to validate every step of the process. Figure 2 conceptually illustrates the architecture of the framework,
which can be described in three layers: (1) Test Specification Layer (BDD scenarios), (2) Automation Execution Layer
(Selenium-driven test engine with page objects), and (3) Outcome Validation Layer (assertions, reports, and
integrations).
5.1 Test Specification Layer:
At the top, business-readable specifications drive the testing. Using Cucumber, we
wrote
feature files
that contain dozens of scenarios covering various aspects of loan and distribution processing.
For example, a feature file for loans includes scenarios for normal loan issuance, loan denial when requests exceed
balances, calculation of loan amortization schedules, handling of multiple concurrent loans if allowed by the plan,
and scenarios for loan defaults (missed payments). Each scenario is essentially a sequence of steps describing an
event (e.g., a participant requests a loan) and the expected system responses. These feature files are stored in
version control alongside the application code, ensuring they version-
match the system. They serve as the “single
source of truth” for expected behavior. One powerful aspect of this approach is reusability of steps: steps like
“Given
the participant has no existing loans”
or
“Then the system displays an error message”
can be used in multiple
scenarios. This layer abstracts away the technical details
–
it reads almost like a policy manual for loans and
distributions but is actually tied directly into the automation.
5.2 Automation Execution Layer:
This is the core of the framework where the test specifications are turned into
actions. The Cucumber runner, integrated with JUnit, reads each scenario and triggers the corresponding
step
definitions
implemented in Java. These step definitions employ the
Selenium WebDriver
and supporting code (page
objects, utilities) to carry out interactions. For instance, consider a step:
“When the participant submits a loan
request for $5,000 with a term of 2 years”
. The bound step definition would locate the Loan Request page object,
call a method to fill in the amount (5000) and term (24 months), and then call the submit action. Behind the scenes,
Selenium drives the browser to perform these actions on the web application. After submission, the next step might
be
“Then the loan request is approved”
, for which the automation might either check the UI for an approval
confirmation message and/or query the database to ensure a new loan entry is present with status “Approved”.
This layer thus executes both
UI-level actions
and
backend verifications
. We included many assert checks in the
step implementations to verify not just that a transaction went through, but that all calculated fields were correct
(for example, after a loan is approved, the monthly payment computed by the system is compared against an
independent calculation we perform in the test to verify the formula). The framework also handles cross-cutting
concerns here: synchronization (waiting for pages to load or background jobs to complete), error handling (if an
unexpected popup appears, capture it and log a warning), and test data setup/cleanup (using API or DB calls to
create prerequisite data or reset states). The use of page objects ensured that if, say, the position of the “Submit”
button on the form changed, we updated it in one place in the LoanRequestPage class, and all scenarios would then
work without modification.
5.3 Outcome Validation Layer:
After and during test execution, the framework collects results and validates
outcomes against expected results. Each scenario yields a pass/fail status depending on whether all assertions in its
steps succeeded. We paid special attention to validating business rules: for loans, we encoded expected behaviors
such as
“no more than 50% of vested balance can be borrowed”
. During test execution, if a scenario intentionally
tries to borrow 60% of balance, the framework expects a specific error message from the system; if the message or
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
17
behavior deviates, the test fails, flagging a potential bug. In this sense, the framework acts as an automated
QA
auditor
, constantly checking the system’s outputs against the retirement plan’s documented rules. The validation
layer also ensures integration points are working
–
for example, if a distribution triggers an external tax calculation
service, the framework verifies that the correct tax code was applied by simulating the same calculation. All
comparisons and checks are logged. We integrated the reporting tool (ExtentReports) here so that at the end of a
test run, a comprehensive report is generated. This includes a high-level summary (e.g., 50 scenarios executed, 49
passed, 1 failed) and detailed trace for each scenario. Failed scenarios are highlighted with details on where the
mismatch occurred. One advantage of having such a robust validation layer is that it not only finds outright errors,
but also subtle deviations. In one case study, our framework caught a scenario where the system allowed a loan
term of 61 months (exceeding the typical 60-month maximum)
–
the transaction went through in the UI, but our
validation flagged it as a defect because the expected behavior (enforcing 60-month limit) did not occur.
The framework was designed to be extensible to accommodate future needs. For example, we built hooks for
API
integration
testing: if in the future the retirement system exposes web services for loan initiation (bypassing the
UI), we can write API-level tests that use the same validation logic. We also structured the framework to support
multiple environments (development, QA, staging) by externalizing environment configurations. This way, the same
test suite can be pointed at a new version of the system or a different client’s instance simply by changing a
configuration file. The BDD test scenarios remain the same, which underscores a key benefit: as the system is
modernized (e.g., modules rewritten or moved to cloud), the test scenarios can remain consistent, providing a
regression safety net throughout the modernization journey. Essentially, the framework can accompany the system
through its evolution
–
it is not a one-off testing tool, but a continuously useful asset.
Moreover, our QA automation framework contributes to
compliance and audit readiness
. Every test run produces
artifacts (logs, reports, screenshots) that demonstrate what was tested and the outcomes. This is valuable for
internal audit or external regulators. For instance, if questioned whether the p
lan’s loan limits are properly enforced
by the system, we can produce evidence from our automated test runs showing that in 100% of test cases the
system behaved correctly (or identifying the specific edge case where it didn’t, which can then be fixed). By
automating these checks, we ensure they are performed regularly, not just as a one-time certification.
In summary, the framework provides a
robust scaffolding around the legacy retirement system
, allowing us to
rapidly test and validate loan and distribution processing. It effectively creates a modern “wrapper” of automated
QA around the older core system. This approach gave the organization confidence to make changes and upgrades
–
knowing that the automated suite would catch any regression
–
which is a crucial enabler for modernization. The
next section will illustrate how this framework was applied in practice through two case studies, and the tangible
improvements observed.
6. CASE STUDIES
To evaluate the effectiveness of the QA automation approach, we applied the framework in two distinct real-world
scenarios. The first case study involves a
major 401(k) loan processing modernization
initiative at a large plan
administrator. The second focuses on a
distribution process overhaul
triggered by urgent regulatory changes. These
case studies demonstrate the versatility of the framework and its impact on both day-to-day operations and the
ability to respond to external events.
Case Study 1: Streamlining 401(k) Loan Processing
–
A Large Recordkeeper Modernizes a Cumbersome Loan
Workflow.
A national retirement services provider administering 500,000+ participant accounts sought to improve
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
18
its loan processing, which was plagued by manual steps and frequent errors. Prior to modernization, a participant
loan request (initiated via an online form) would enter a queue for staff review, then require batch job updates in
the recordkeeping system, and finally a confirmation sent out days later. On average it took
3
–
5 business days
to
complete a loan from request to disbursement, and about 8% of loan requests encountered some form of error
requiring re-work (incorrect interest rates, or missed initiation of payroll deduction). Using our
QA automation
framework
, the provider re-engineered this process for straight-through processing. The BDD-driven tests were
crucial during this re-engineering: as the IT team introduced a new automated loan module, the QA tests
continuously validated that each new build met all legacy requirements and did not introduce regressions. Over a
6-month period, we developed ~120 automated scenarios covering all business rules (loan eligibility, limits, interest
calculation, repayment scheduling, default handling). These tests ran nightly against the staging environment as
new features of the loan module were rolled out. The impact was dramatic
–
upon go-live of the modernized loan
system, the end-to-end processing time dropped to
<1 day
(with many loans approved and funded within an hour
of initiation), and the error rate fell to near zero (no compliance violations or miscalculations detected in the first
three months of operation) as
shown in Figure 1 below. The automation framework didn’t just operate in testing;
it was repurposed in a monitoring role in production dry-runs, executing synthetic transactions to ensure the system
was working before real participant requests were allowed through. This gave stakeholders great confidence. The
results in this case study align with other industry experiences where intelligent automation
cut loan processing
times by about half and improved cost efficiency [7]
. Internally, the provider estimated a reduction of 30% in
operational workload related to loans
–
staff who previously spent time on routine loan tasks could be reassigned
to customer service and exception handling. The QA automation framework continues to serve in this organization
for continuous regression testing whenever loan policies are updated annually.
Figure 1. This line graph compares the loan workflow stages and the time taken before automation (in red) versus
after automation (in green), clearly showing significant time reductions across each step.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
19
Case Study 2: Rapid Adaptation to Regulatory Change (Distribution Processing)
–
Enabling Agility During the CARES
Act Emergency.
In 2020, the COVID-19 pandemic led to the U.S. CARES Act, which among many provisions, allowed
retirement plan participants to take penalty-free emergency withdrawals up to $100,000. A regional retirement
plan administrator needed to implement these distribution rule changes almost immediately, as participants began
requesting COVID-related distributions. Their legacy distribution processing was largely manual and rule-bound
(with IRS early withdrawal penalties hard-coded into the system). The challenge was not only to update the rules
but to do so quickly and confidently, given the limited time. Our QA automation framework became an essential
tool in this scenario. We rapidly developed new BDD scenarios to represent the CARES Act provisions (e.g.,
“Given
a participant affected by COVID-19 requests a $50,000 distribution, when it is processed, then no 10% early
withdrawal penalty is a
pplied and special tax reporting codes are used”
). These scenarios covered both the presence
and absence of the special conditions. The team then updated the system’s distributio
n logic accordingly. Thanks
to the automation, we were able to regression-test the entire distribution workflow (including the new cases) in
a
matter of hours
, something that would have taken weeks with manual testing as shown in figure 2 below. The
framework caught a few initial issues
–
for example, a scenario where the system still applied a penalty on a certain
distribution subtype
–
which were quickly fixed and re-tested. Within roughly two weeks, the administrator fully
implemented the CARES Act changes and was able to process the surge of distribution requests with confidence.
The QA framework continued to run daily tests on the new logic throughout the high-volume period of 2020,
effectively acting as an early warning system for any processing anomalies. As a result, the firm reported
zero
compliance errors
in handling CARES Act distributions. They were able to handle a 5x spike in distribution volume
at the peak of the pandemic response without needing to increase staff, owing to the high level of automation. This
agility showcased how modernization investments pay off when unpredictable changes occur. One industry
commentary had presciently noted that
“not only can laws change unexpectedly, but [organizations] might not get
months to implement these changes either”[3]
–
our case study validated that with the right tools (like QA
automation), even large-scale changes could be absorbed rapidly. Beyond the CARES Act, this experience left the
organization in a stronger position: the automated tests for distributions were left in place and later used to
implement provisions of the SECURE Act and other regulatory updates with minimal fuss.
figure 2. It visually maps
the rapid implementation of distribution processing changes in response to the CARES Act, showing each major
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
20
milestone and the number of days taken to achieve it
—
from the Act's announcement to production go-live and
daily monitoring.
Comparing the two case studies, we observe that the QA automation approach provided benefits in both a
proactive modernization project
(Case 1) and a
reactive urgent update
(Case 2). In the loan processing overhaul,
automation was key to iterative development and ensuring quality during a major system enhancement. In the
distribution scenario, the framework delivered agility and ensured compliance under tight deadlines. In both cases,
the common outcome was a faster, more reliable process:
•
In Case 1, participant loans became faster (hours instead of days) with virtually no errors, improving
customer satisfaction and trust.
•
In Case 2, the firm demonstrated compliance and responsiveness in a crisis, avoiding penalties and
bolstering their reputation with plan sponsors and participants.
These case studies underscore that modernization is not just about new features, but about building
confidence
and
resilience
into systems. By having a robust QA automation framework, organizations can undertake significant
changes with a safety net in place, which encourages innovation rather than fear of breaking things. Next, we
quantify the improvements and discuss broader implications drawn from these implementations.
7. Results and Discussion
The implementation of the QA automation framework yielded substantial improvements across multiple
dimensions. We summarize the key
results
observed in figure 3 below and provide a discussion in the context of
industry benchmarks and future considerations:
7.1 Processing Speed and Efficiency:
Both case studies showed dramatic reductions in end-to-end processing times
for loans and distributions. Quantitatively, the automated loan processing pipeline in Case 1 achieved a ~
60
–
70%
reduction
in cycle time, cutting what was a multi-day process down to same-day (often same-hour) completion.
This aligns with external reports of automation in financial services yielding roughly
50% faster processing cycles
on average [7]. The distributions case saw similarly improved throughput despite volume spikes. Faster processing
not only means better service for participants (receiving their funds sooner) but also reduces work-in-progress
queues, making the operation more manageable. Our findings reinforce that removing manual touchpoints and
introducing straight-through workflows can halve or better the time required for routine transactions, consistent
with the promise of digital transformation in finance [3].
7.2 Accuracy and Error Reduction:
A primary goal was to eliminate the kinds of errors that plagued the legacy
processes. The automated validation of business rules and calculations led to near
zero processing errors
in
production during our observation period. In Case 1, after go-live, no instances of loan limit violations or mis-
calculated schedules were reported, whereas previously such issues occurred frequently (dozens of errors annually
requiring correction). In Case 2, the success was measured by the absence of compliance flags or restatements
needed for the emergency distributions
–
a stark contrast to some peers who, lacking automation, had to later
correct mistakes in tax reporting. The framework essentially acted as a continuous guardrail, enforcing policies
exactly as written. This outcome matches the expectation that
comprehensive automation “flags potential errors
and manages the process to ensure the most accurate data”
is passed between systems. Our work corroborates
that notion: by encoding rules in tests, we caught potential errors before they affected real accounts. Industry data
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
21
suggests that automation can eliminate common mistakes such as overpayments or missed loan repayments [2],
and our results provide concrete evidence of this in a large-scale setting. An incidental benefit we noted is improved
data accuracy and consistency across systems (payroll, recordkeeping, etc.), since the automated process enforced
synchronization
–
e.g., every loan issuance test also checked that the payroll system was updated with the new
deduction, something that might be overlooked in manual operations.
7.3 Compliance and Risk Management:
The automated QA approach significantly strengthened compliance
assurance. With regulations in retirement plans being strict (IRS rules, Department of Labor requirements, etc.),
having an automated suite that checks compliance in each test run greatly reduces the risk of violations. In
discussions with the plan provider’s compliance officers, they expressed increased confidence that the system
adheres to all plan provisions because those provisions are essentially tested daily. It is worth noting that regulators
are paying more attention to system robustness; the Department of Labor and other agencies have been
encouraging retirement plan providers to improve their technology to prevent errors and protect participants. Our
approach provides a concrete way to do that. In fact, we can envision regulators in the future asking for evidence
of such automated controls. In our case, the provider was able to demonstrate, for example, that
100% of loan
requests beyond allowable amounts are caught and prevented
by the system (with test logs to prove it). This kind
of proof could be persuasive in an audit. From a risk management perspective, automation also reduces key-person
risk and process drift. Previously, so much know-how resided in individuals who manually processed edge cases;
now that knowledge is institutionalized in the automated tests and code. One point of discussion is that QA
automation doesn’t remove all risk –
it is only as good as the scenarios anticipated. We mitigated this by extensive
scenario brainstorming (with business and compliance input), but unknown unknowns can still occur. That said, a
robust automated test bed allows for very quick testing of any new scenario that arises unexpectedly. We argue
that the
presen
ce of this framework fundamentally improves the organization’s operational risk posture
.
7.4 Operational Cost Impact:
By reducing manual work, the QA automation approach is associated with cost
savings, although exact amounts depend on each organization’s context. For our Case 1 provider, they e
stimated
an annual saving of several thousand person-hours in loan processing and exception handling. This translates to
either labor cost savings or the ability to repurpose staff to more value-added roles (e.g., personalized financial
guidance to participants rather than paperwork). Automation also cuts costs related to errors
–
every prevented
error avoids not just potential penalties but also the internal cost of correcting that error (which can be high, as it
may involve research, re-running calculations, communicating with affected participants, etc.). The UiPath industry
analysis reports up to
30% reduction in overhead costs
with modern test automation and increased coverage [6].
Our experience suggests a comparable order of magnitude of savings when considering the full picture (labor,
rework, compliance costs). It’s important to note, however, that t
here is an upfront investment to build the
automation framework
–
our project took months of effort from a dedicated QA automation team. The return on
investment (ROI) became evident within the first year, and from that point on it’s largely net gain. We a
lso foresee
future cost avoidance: as the system evolves, new features can be delivered faster because testing effort will not
scale linearly (the regression suite is already automated). This is a form of “speed to market” benefit that is hard to
quantify in dollars but is very tangible in competitive terms.
7.5 Test Coverage and Quality Assurance:
One often overlooked result is the improvement in
test coverage
and
depth of quality assurance. Manual testing, due to time constraints, often samples a few scenarios and paths. In
contrast, our automation was thorough
–
we automated edge cases that manual testers rarely attempt, such as
simultaneous loans, back-to-back distributions, or unusual sequencing of actions. We achieved nearly
100%
coverage of documented business requirements
for loans and distributions. Moreover, automated tests can be
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
22
run repeatedly with varied data, effectively covering far more combinations than a human could. This led to the
discovery of legacy bugs that were previously unknown. For instance, during the project we uncovered a defect in
the legacy system where if a participant changed their repayment frequency mid-loan, the system mishandled the
amortization
–
a subtle bug that had not been caught before. By catching and fixing such issues, the overall quality
of the system is improved beyond the scope of the modernization at hand. This demonstrates how an automation
initiative can drive up quality holistically. Our results resonate with the general trend that
modern automated
testing can increase test coverage by 2
–
3×
compared to manual methods [6]. Higher coverage means fewer
escaped defects, which again ties back to cost and risk improvements.
7.6 Developer and Team Productivity:
An ancillary benefit observed was a change in team dynamics and
productivity. With the automated tests as a safety net, developers became more willing to refactor and improve
code in the retirement system. In the past, fear of breaking something (given the complexity of legacy code) made
developers hesitant to touch certain modules. The presence of a comprehensive test suite mitigated that fear, since
any unintended impact would be immediately flagged by a failing test. This led to a cleaner, more modular codebase
over time as technical debt was gradually refactored. It also shortened debugging cycles
–
when a test failed,
pinpointing the cause was faster thanks to detailed logs and the reproducibility of automated tests. From a process
standpoint, the collaboration between business analysts, developers, and QA improved due to BDD. The BDD
scenarios became a common reference point, and this shared understanding reduced miscommunication. These
qualitative improvements support the notion that investing in quality assurance (especially automation with BDD)
has multiplicative effects on the software development lifecycle, an insight echoed in agile development research.
Figure3: Summary of QA Automation Impact Across Key Result Areas
–
This bar chart highlights the effectiveness of
the QA automation approach across six dimensions: processing speed, error reduction, compliance assurance,
operational cost savings, test coverage, and team productivity. The results demonstrate substantial improvements,
reinforcing automation as a transformative driver in retirement system modernization.
Despite these positive outcomes, the
discussion
would be incomplete without addressing some challenges and
limitations encountered:
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
23
•
Maintenance of the Automation Suite:
As the system changes, the test suite must be maintained. While we
designed for maintainability (using POM, etc.), any change in requirements or UI does necessitate updating the
tests. This introduces ongoing overhead. We mitigated this by training the QA team and even some developers
to continuously maintain and extend the test suite as part of normal development work (shifting some QA left to
developers). In effect, the test code becomes part of the codebase to be cared for. Organizations must plan for
this and not treat automation as a one-time project. Encouragingly, maintenance effort has been manageable;
for example, a minor UI redesign post-modernization took two QA engineers about two days to adjust all relevant
tests. The key is that management must acknowledge automation scripts as assets that require upkeep
–
much
like one maintains documentation or infrastructure.
•
Tool and Environment Issues:
We faced occasional issues typical of test automation
–
e.g., Selenium tests failing
due to timing issues or environment instability rather than genuine defects (so-called false positives). Early in the
project, such hiccups caused some mistrust in the test results. We addressed this by improving synchronization
in the scripts and by implementing a strategy of re-running failed tests to see if issues persist, thereby filtering
out flaky tests. Over time the stability reached a very high level (tests either consistently pass or surface real
problems). Additionally, test environments need to be managed carefully; we had to ensure our test runs did not
conflict with other uses of the QA environment, especially when simulating large transaction volumes. For future
implementations, containerized test environments or service virtualization could further isolate and stabilize
automated testing.
•
Scope Boundaries:
Our framework was comprehensive for loans and distributions, but it did not automate every
aspect of the retirement platform (for instance, we did not initially automate investment trades or complex
actuarial calculations unrelated to loans/distributions). Thus, the modernization via QA automation tackled
specific pain points but wasn’t a total system rewrite. Some might argue this leaves other areas of the system still
needing modernization. However, our strategy was to focus on the most impactful areas first. In practice, the
success with loans and distributions has set the stage to expand automation to other processes (such as
enrollment processing, employer contribution processing, compliance tests like discrimination testing). This
phased approach is sensible in large systems
–
bite off the parts that yield high ROI and demonstrate success,
then iterate. In discussion with the provider’s leadership, they are now planning to leverage our framework to
cover additional modules, essentially scaling the modernization effort. This raises an important point: the QA
automation approach is
incremental
and can gradually lead to comprehensive modernization without the big risks
of a full system replacement project.
•
Comparison to Alternative Approaches:
It’s worth discussing why we chose QA automation as the lever for
modernization rather than, say, adopting a new off-the-shelf recordkeeping system or building RPA (Robotic
Process Automation) bots on top of the legacy system. Full system replacement was deemed too risky and
expensive (multi-year projects with uncertain outcomes, as many failed core system replacements in government
and finance have shown). RPA bots were considered
–
indeed RPA is often touted as a way to integrate legacy
systems by mimicking user actions like a human would. However, pure RPA can be brittle and was seen as a short-
term fix; RPA would automate the manual work but wou
ldn’t necessarily enforce rules unless programmed to,
and complex decision logic can become hard to maintain in RPA scripts. We opted for a more
engineering-driven
automation (directly at the application logic level through tests) rather than an ops-driven RPA overlay. That said,
RPA could complement our approach for parts of the process that require interacting with external systems
lacking APIs. Our approach was also aligned with a broader DevOps culture shift
–
integrating QA into
development
–
whereas RPA is often outside of that cycle. In summary, the QA automation approach proved to
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
24
be a balanced strategy: it leveraged existing system investment, minimized disruption, and delivered quick wins,
all while building towards a more robust future state.
In light of these results, we observe a few
trends and implications
. First, the success of automation in a traditionally
conservative domain like retirement services suggests that even highly regulated, batch-oriented industries can
greatly benefit from modern QA and DevOps practices. The barrier to entry (learning test automation, changing
culture) is well worth the outcome. Second, our work highlights the importance of focusing on quality as a path to
modernization. Often, modernization is thought of purely in terms of new technology (e.g., moving to cloud,
adopting microservices). Our case illustrates that focusing on
quality and testing
can itself drive modernization
–
by
enabling those other tech changes safely. Essentially, QA automation became the enabler for broader innovation
here.
Finally, we note that as retirement systems modernize, participants and plan sponsors will experience tangible
benefits: faster loan turnarounds mean participants get needed funds in emergencies promptly (potentially
improving financial well-being), and error-free processing means fewer costly mistakes impacting retirement
savings. Providers that invest in such capabilities may also gain competitive advantage in the marketplace (sponsors
choosing recordkeepers who offer superior service). This creates a virtuous cycle encouraging further
modernization in the industry.
8. CONCLUSION
This paper presented a comprehensive, research-driven approach to modernizing legacy retirement plan operations
using QA automation. Focusing on the critical use cases of participant loan processing and distribution processing,
we demonstrated that a well-designed automated testing and process validation framework can serve as a powerful
catalyst for modernization. Our QA automation approach, grounded in behavior-driven development and powered
by tools like Selenium and Cucumber, enabled the transformation of sluggish, error-prone legacy workflows into
efficient,
straight-through processing
pipelines. The case studies of a 401(k) loan modernization and an emergency
distribution rule change illustrated how the framework not only improved day-to-day efficiency (with up to 70%
faster processing and near-
zero errors) but also enhanced the organization’s agility in the face of change.
The key contributions of this work are twofold. First, we provided a
framework and methodology
that others in the
retirement and financial services industry can emulate
–
one that emphasizes incrementally overlaying automation
to achieve quick wins while laying the groundwork for deeper system upgrades. By integrating QA automation early
in the modernization process, we showed that organizations could de-risk the modernization effort and
continuously verify compliance with complex regulations. Second, we offered an
analytical evaluation
of the
outcomes, using both quantitative results and qualitative insights, bridging the gap between theoretical benefits of
test automation and actual realized benefits in a production environment. We integrated academic rigor by
referencing industry data and best practices, thereby positioning our approach in the context of broader fintech
and software engineering trends.
The implications of these findings are significant. For industry practitioners, this approach provides a roadmap to
enhance legacy systems without the “freeze and replace” method that often fails. It suggests that modernization
budgets and efforts should allocate resources to building robust automated QA suites as an integral part of system
renewal. For policymakers and regulators, our work highlights that encouraging or even mandating higher standards
of automation and testing in recordkeeping operations could greatly reduce operational failures that ultimately
affect retirees. Regulators might consider frameworks for certifying automated controls in retirement systems,
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
25
analogous to how financial controls are audited
–
the evidence from our study indicates that such automation leads
to more compliant outcomes with potentially fewer errors that harm participants [2].
In terms of
future research and development
, there are several avenues to explore building on this foundation.
One area is the incorporation of
AI and machine learning
into the QA automation framework. For instance, machine
learning could analyze historical transaction data and user behavior to suggest additional test scenarios (edge cases
that humans might not think of) or to predict which parts of the system are most likely to fail after a given change.
Another prospective advancement is using
model-based testing
techniques to automatically generate test cases
from formal specifications of retirement plan rules
–
this could further increase coverage and reduce manual effort
in writing tests. Early research indicates that model-based approaches could ensure a
“seamless change to the
modernized system”
by systematically covering all states and transitions [10]. Combining model-driven methods
with our behavior-driven framework would be an interesting hybrid approach to guarantee robustness.
Additionally, extending the automation framework to encompass
performance testing and scalability
will be crucial
as transaction volumes grow. In our project, we primarily focused on functional correctness, but for a complete
modernization, one must also validate that the system can handle peak loads (for example, a surge of loan requests
during an economic crisis). Automating performance tests and integrating them into CI (e.g., using tools like JMeter
or Gatling alongside Selenium) would help ensure that modernization does not introduce bottlenecks.
From a
policy perspective
, as the industry modernizes, standardization of processes and data formats (perhaps
through regulatory guidance) could further facilitate automation. If all plan providers followed certain data
standards for loans and distributions, QA frameworks could be more easily shared or benchmarked across the
industry. Our results showing reliability and compliance improvements could inform such standard-setting.
Policymakers interested in the resilience of the retirement system might view automation not just as an operational
improvement, but as a way to safeguard participants’ assets –
for example, by reducing the incidence of loan
defaults caused by administrative error, thereby preserving retirement savings.
In conclusion, modernizing retirement systems is a multi-faceted challenge, but this study demonstrates that
QA
automation is a practical and effective strategy to drive modernization
while controlling risk. We modernized
critical facets of loan and distribution processing without a risky system replacement and achieved outcomes on
par with or exceeding expectations from a full modernization. The language of quality assurance and automation
can be a unifying force between business stakeholders, IT professionals, and regulators
–
all can agree on the end
goals of accuracy, efficiency, and reliability. By making the legacy new again through the lens of QA automation,
retirement service providers can better meet the needs of today’s workforce and retirees, ensuring that the promise
of technology
–
faster service, fewer errors, greater transparency
–
is delivered in an arena that directly impacts
the financial well-being of millions. The techniques and lessons detailed here are broadly applicable to any domain
where legacy processes and rules-heavy workflows prevail, offering a template for others to follow in the ongoing
journey of digital transformation.
REFERENCES
1.
S. Holden, S. Bass, and C. Copeland, “401(k) Plan Asset Allocation, Account Balances
, and Loan Activity in
2022,”
ICI Research Perspective
, vol. 30, no. 3, Apr. 2024.
2.
B. Cross, “Lamenting over loans: Operational failures related to participant plan loans,”
Milliman Insight
, Jan.
19, 2023.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijbms
26
3.
Congruent Solutions, “Processing 401k Withdrawals: Is your retirement plan technology agile?,”
Congruent
Blog
4.
McKinsey & Company, “Long
-
term value creation in US retirement,” McKinsey Report, 2019.
5.
Congruent Solutions, “The CORE Platfor
m
–
Loans & Distributions Module,”
Product Information Page
, 2022.
6.
S. Gustafson, “Why financial services firms are rewriting their testing strategies,”
UiPath Blog
, Aug. 2024.
7.
Sutherland Global, “Transforming Loan Processing for a leading US financial institution with intelligent
automation,”
Case Study
, Dec. 2024.
8.
H. Akhtar, “What is BDD? (Behavior
-
Driven Development),”
BrowserStack Guide
, Dec. 17, 2024.
9.
Selenium Project, “Page Object Models –
Seleniu
m Documentation,” Selenium.dev, 2023.
10.
T. Ritter et al., “Model
-
based testing in legacy software modernization,” in
Proc. JAMAICA 2013 Workshop
,
2013.
