Sapiensdev.com

Proven fintech delivery

Measurable outcomes from Sapiens engagements with banking and financial services clients.

FINTECH

Gesa Credit Union

From One Week to 1.5 Hours: Accelerating Releases with Automated Regression Testing

Challenge

Gesa Credit Union needed to release software faster without increasing quality, security, or compliance risk. Its regression testing process depended heavily on manual work, took as long as one week, and became increasingly difficult to scale.

Manual regression testing was slowing releases and placing a growing burden on QA teams. The absence of standardized automation frameworks made testing error-prone, difficult to maintain, and challenging to expand across the organization. The new approach had to:

  • Reduce regression time without compromising quality.
  • Replace repetitive manual testing with reliable automation.
  • Integrate testing into CI/CD pipelines.
  • Operate within strict enterprise security and compliance requirements.
  • Establish reusable standards that other teams could adopt.
  • Create dependable test data with clearly defined expected results.

Solution

Sapiens designed and implemented an end-to-end QA automation strategy built around real user behavior and sustainable engineering practices. This created a scalable QA foundation that made quality checks faster, repeatable, and easier to govern.

  • An end-to-end automated test suite using Playwright.
  • CI/CD optimization through test sharding and shared execution capacity.
  • Structured test datasets containing both inputs and expected outputs for accurate validation.
  • Modular, reusable automation components and consistent testing standards.
  • A secure automation foundation aligned with the client’s enterprise policies.
  • Mentoring and adoption support to help teams use and extend the new capability.

Gesa Credit Union moved from a slow, reactive testing process to a structured quality-engineering capability. Faster regression cycles reduced delivery friction and gave teams earlier feedback, while standardized automation helped lower release risk and support long-term scalability.

Outcomes

  • 1.5 hours regression testing time (down from up to 1 week)
  • 96% reduction in regression testing time
  • 86% faster release cycle
  • 90% reduction in manual testing effort
  • 100% compliance with stated enterprise security requirements
QA AutomationAutomated TestingRegression TestingPlaywrightCI/CDComplianceFinancial Services
FINTECH

Nelnet

Stabilizing and Scaling a Loan Repayment Platform for Millions of Accounts

Challenge

Nelnet operates a microservices-based loan repayment platform serving multiple U.S. banking partners and millions of loan accounts. As the platform expanded, a critical legacy module accumulated technical debt, performance bottlenecks, and recurring production issues—putting reliability and future growth at risk.

The platform applies payment-relief and loan rules shaped by U.S. regulations, with different configurations for each bank and program. Supporting additional partners meant handling more accounts and more rule variations without sacrificing accuracy, stability, or performance. Nelnet needed to:

  • Scale the platform to support millions of loans.
  • Continue delivering features without destabilizing existing services.
  • Resolve deep technical debt in a business-critical module.
  • Eliminate performance bottlenecks and recurring production issues.
  • Replace obsolete dependencies and implementations.
  • Introduce automated testing where no coverage existed.
  • Maintain accurate processing across complex loan workflows.

Solution

Sapiens modernized and stabilized the module while the platform continued to evolve, treating it as an interconnected engineering problem rather than applying isolated fixes.

  • Refactoring outdated code to reduce technical debt and improve maintainability.
  • Resolving performance issues affecting long-running financial processes.
  • Updating obsolete dependencies to strengthen reliability and long-term support.
  • Introducing automated testing and raising coverage from 0% to 60%.
  • Improving data accuracy and precision across loan workflows.
  • Maintaining platform stability while delivering new capabilities for banking partners.
  • Strengthening the architecture to support more banks, programs, and loan accounts.

The engagement turned a fragile system component into a stable, scalable asset. Nelnet reduced operational risk, improved processing reliability, and created a stronger technical foundation for serving millions of accounts across multiple banking partners—without treating every new feature as a threat to stability.

Outcomes

  • 2 hours long-running process duration (down from up to 6 hours)
  • 67% reduction in execution time
  • 60% automated test coverage (increased from 0%)
  • up to 70% reduction in system errors and production issues
AI-Native Software Development TeamPlatform modernization
FINTECH

Nelnet

Optimizing Payment Processing with Rule-Based Automation and Scalable Bulk Payments

Challenge

As payment volume and transaction complexity increased, Nelnet needed its payment and tax modules to support more scenarios without creating new data issues, processing delays, or operational risk.

The platform processes payments and collections for both individual and entity loans. Growing usage exposed limitations in payment calculations, data consistency, database responsiveness, and the ability to process high transaction volumes efficiently. Nelnet needed to:

  • Improve the accuracy and flexibility of payment calculations.
  • Support payment rules for both individuals and entities.
  • Distribute funds correctly across multiple loans.
  • Process high volumes of bulk payments efficiently.
  • Reduce database response times for payment operations.
  • Resolve payment errors and data inconsistencies.
  • Standardize and modernize backend code without disrupting existing transactions.

Solution

Sapiens took ownership of critical payment and tax components and improved them across business logic, data ingestion, database performance, and maintainability.

  • Enhancing the rule engine to calculate payments for individuals and entities.
  • Implementing rule-based distribution of funds across multiple loans.
  • Testing and refining the rule engine for accuracy, stability, and reuse across transaction types.
  • Designing bulk-payment file uploads for efficient, high-volume processing.
  • Building scalable ingestion workflows for enterprise payment data.
  • Reviewing database schemas, adding necessary indexes, and removing redundant ones.
  • Standardizing repositories and upgrading Node.js versions for maintainability and long-term support.

Nelnet gained a faster, more reliable payment-processing foundation. Complex payment logic became reusable and easier to govern, while bulk ingestion allowed clients to process larger volumes with less friction.

Outcomes

  • 94% reduction in payment errors and data issues
  • 73% reduction in database response time
  • 83% increase in bulk-payment upload usage
  • Expanded across individual and entity loans payment calculation support
  • Standardized and modernized backend foundation
Payment ProcessingRule EngineBulk PaymentsDatabase OptimizationNode.jsBackend ModernizationData IntegrityFinancial Services

Related pages

Related service pages include Fintech Software Development, Banking Software Development, and Services.