AI Dependency Mapping in Action: Cutting Modernization Costs for a Fintech Platform by 30%

A mid-sized fintech company has been delivering digital finance tools since 2015. Its platform powers AI-driven lending, budgeting, and investment tracking — serving hundreds of thousands of users and processing over a billion dollars in loans annually.

David R., Head of Engineering, had been guiding the tech side since the company’s early growth. His job was to keep the platform rock-solid under tough compliance rules while still pushing new features in lending and investment.

He inherited a 450,000-line .NET monolith packed with C# services, SQL databases, and integrations for credit scoring and compliance. That setup fueled fast growth in the early years. But for David, it quickly turned into a cage: dependencies slowed every release, cloud adoption stalled, and even small updates risked breaking critical compliance flows.

By 2024, leadership set a clear mandate: transition to Azure, carve out micro-frontends, and modularize services to enable entry into adjacent markets. Internal forecasts placed the program at $3.2M over 15 months, with nearly half allocated to auditing and refactoring legacy code.

The platform had grown on top of a .NET monolith — 450,000 lines of code built over the years by different teams. Inside were C# services, SQL databases, and compliance integrations layered for credit checks, reporting, and security. Each addition solved an immediate need, but complexity compounded with every release.

As user demand increased, the system struggled to cope under load. Entangled dependencies made even small updates risky. Outdated frameworks limited Azure integration. Compliance requirements amplified the pressure, as every change carried regulatory weight.

By mid-2024, leadership recognized the architecture had reached its ceiling. Expansion into new product lines required cloud scale, modular services, and an upgrade path that preserved business continuity.

• Modernization Architect
• 2 × Senior Software Engineers (refactoring & microservices)
• DevOps Specialist (Azure, IaC, CI/CD)
• QA Automation Engineer
• Security Advisor (compliance & governance)

The breaking point came when David’s team set out to launch an AI-driven lending feature. A two-week sprint ballooned into three months. Investor pressure kept climbing, and team knew: without a new modernization path, growth goals would collapse.

Modernization in fintech, though, isn’t just a switch. It demands precision at scale. Years of rapid growth had left the platform heavy with risk — and three conditions made that risk impossible to ignore.

• Compounded Dependencies. The monolith contained 450k lines of .NET code with entangled states, brittle dependencies, and hidden couplings. Audits with SonarQube flagged 600+ issues across database layers and services. Beyond that, nuanced interactions stayed invisible until they broke under load.
• Compliance Under Pressure. Outdated .NET frameworks limited Azure adoption. At the same time, PCI DSS and GDPR required strict control of every transaction. Database replication and volume planning added weight. Each migration step demanded assurance that security and availability stayed intact.
• Capacity and Cost Constraints. A lean internal team carried out daily operations, roadmap delivery, and support. Manual reverse engineering consumed weeks per module. Each cycle raised costs and delayed competitive features, such as real-time AI lending.

Without structure, these forces pushed the program toward budget overruns of 30–50% and placed the company’s 20% growth targets at risk.

.NET Framework, .NET 8, C#, SQL Server, Azure Functions, Azure App Services, Micro-frontend modules, Terraform, GitHub Actions, SonarQube, AI-assisted refactoring, automated testing pipelines.

The team applied an AI-accelerated modernization flow, designed for continuous delivery and zero service disruption. Each step advanced the system through controlled slices, backed by automation and human oversight.

• Semantic Backlog Extraction. AI parsed meeting transcripts and requirements into a structured backlog. Contradictions were resolved, assumptions surfaced, and priorities set on flows with the highest business impact — lending, compliance reporting, and user dashboards.
• Legacy Code Understanding. Graph-based analysis dissected responsibilities across 450k lines of code. Fragile couplings, dead paths, and hidden dependencies were mapped, creating a clear model for safe decoupling.
• AI-Guided Refactoring. Agents embedded in IDEs suggested precise structural improvements, while engineers validated logic and compliance. This kept delivery cycles short and ensured consistent code quality.
• Automated Test Generation. Unit, integration, and end-to-end tests were generated in parallel with refactors. Visual validation and headless runs confirmed functionality across modules before deployment.
• DevOps Acceleration. Infrastructure was codified with Terraform. GitHub Actions pipelines handled provisioning, deployments, and regression checks. Blue-green strategies on Azure ensured live traffic remained uninterrupted.
• .NET Upgrades. Framework dependencies were scanned, packages upgraded, and migration paths validated through automated tests. Each increment moved to .NET 8 without breaking flows.
• Governance and Compliance Guardrails. Sensitive data stayed masked. Access was logged and monitored. Every action is aligned with PCI DSS and GDPR from the first line of code to production rollout.

Through this framework, modernization advanced slice by slice — with measurable outcomes at each release.

BUSINESS OUTCOMES

• 30% cost efficiency achieved. Total program spend closed at $2.24M vs. the $3.2M forecast. Automation, AI-assisted refactoring, and phased releases eliminated redundant cycles and reduced maintenance overhead.
• Acceleration to market. Modernization finished in 10 months, five months ahead of plan. This enabled early rollout of AI lending features and expansion into new regional markets.
• 16% revenue growth realized. Faster product launches and improved user experience drove higher lending volume and portfolio engagement. Projections for the following year point to +19% growth with similar conditions.
• Operational resilience strengthened. Streamlined delivery reduced opportunity costs from stalled innovation. The platform supported continuous feature releases, boosting customer satisfaction and competitive positioning.
• Resource redeployment. Reduced infrastructure and manual engineering overhead freed internal teams to focus on product differentiation, not firefighting.

TECHNICAL OUTCOMES

• Latency reduced by 35%. Loan approval and transaction flows dropped from 2s to 1.3s, aligning with industry benchmarks for high-performing fintech systems.
• Scalability increased by 40%. The platform absorbed peak loan-processing loads without additional hardware, supported by Azure-native elasticity.
• Incident rates cut by 60%. Dependency-related errors and runtime regressions dropped sharply, backed by automated test generation and quality gates.
• Security posture enhanced. Esilience against breaches improved 3×, validated through governance guardrails and continuous PCI DSS/GDPR alignment.
• Engineering cycles shortened by 70%. AI-driven refactoring compressed code audits from months to weeks. Rework decreased by 55%, while new feature integration accelerated.
• Infrastructure overhead reduced by 50%+. Azure migration, serverless adoption, and IaC-driven environments eliminated on-premise dependencies and cut recurring costs.