Fintech Tech Debt Case Study: From 40 Incidents a Month to 4

In this article:

• The situation before remediation
• Diagnosis: where the debt was concentrated
• The remediation approach
• Results: incidents down from 40 to 4 per month
• What made the difference
• Conclusion

This technical debt case study documents the remediation work Eden Technologies conducted with a fintech platform that was experiencing 40 production incidents per month. The platform handled payment processing and account management for end users. At the time Eden Technologies was engaged, the engineering team was spending the majority of its time responding to incidents rather than building product. Deployments were slow, risky, and infrequent. The team had stopped trusting the codebase. The outcome of the work was a reduction from 40 incidents per month to 4, achieved through a structured, incremental remediation process that kept the platform operational throughout.

The Situation Before Remediation

The platform had been in production for several years and had grown through a combination of rapid feature development and acquisition of smaller products. The codebase reflected this history: multiple architectural styles coexisted in the same repository, business logic was duplicated across modules that had been merged without reconciliation, and the test suite covered approximately 15 percent of the production code.

The 40 incidents per month were not uniformly distributed. Roughly two-thirds originated from three specific areas of the codebase: the transaction processing module, the notification service, and the data synchronisation layer between two products that had been integrated without a clean interface.

The engineering team knew where the problems were. The obstacle was not knowledge. It was capacity. Every hour spent responding to incidents was an hour not available for fixing the root causes. The team had reached a state where incident response consumed enough capacity that the debt-generating work continued by default, because feature commitments still existed and the only time available was borrowed from sleep and weekends.

This is the technical debt trap: the system produces enough problems to consume the capacity that would be needed to fix it. External intervention is often the only way out.

Diagnosis: Where the Debt Was Concentrated

Eden Technologies began with a software due diligence assessment of the codebase. The goals were to identify the highest-risk areas, quantify the cost of the existing debt in terms of incident frequency and delivery time, and define a remediation sequence that would produce measurable results quickly enough to create capacity for deeper work.

The assessment confirmed what the engineering team had reported. The transaction processing module had grown to over 8,000 lines of code with no internal structure. Business logic, database access, external API calls, and error handling were mixed throughout. There were no unit tests. The module had been modified by more than fifteen developers over three years, each adding to the surface without ever restructuring it.

The notification service had a different problem: it had been partially rewritten twice, and the old and new implementations ran in parallel, with routing logic that was not clearly documented. In practice, some notifications went through one path and some through the other, and the conditions were not always predictable. This was a consistent source of incidents where notifications were sent twice, not at all, or to the wrong recipients.

The data synchronisation layer was the third major problem area. It had been built to bridge two products that had different data models, and it used a polling mechanism that created race conditions under load. The conditions were not reliably reproducible in development, which meant fixes were often partial and sometimes introduced new symptoms.

The Remediation Approach

The remediation was structured in three phases, designed so that each phase reduced incident frequency enough to create additional capacity for the next phase.

Phase 1: Stabilisation. The immediate goal was to reduce the incident rate quickly, without restructuring the code. This involved adding monitoring and alerting to the three problem areas so that failures were detected faster and mean time to recovery improved. It also involved writing characterization tests for the most brittle paths in the transaction module, not to improve the code but to document its current behaviour and catch regressions during subsequent changes.

Phase 2: Isolation. The notification service parallel implementation was resolved by routing all traffic through the new implementation and removing the old code path. This required a careful migration with feature flag control, tested incrementally before full cutover. The data synchronisation layer was refactored to use an event-driven approach rather than polling, which eliminated the race conditions that caused the majority of synchronisation incidents.

Phase 3: Restructuring. The transaction processing module was decomposed over a period of six weeks. Business logic was extracted into a separate layer with unit tests. Database access was consolidated behind a repository pattern. External API calls were wrapped in adapters that could be tested and monitored independently. The refactoring happened in a sequence of small, independently mergeable changes, each of which kept the module in a working state.

Results: Incidents Down from 40 to 4 Per Month

After the three phases, the incident rate dropped from 40 per month to 4 per month. The four remaining incidents were in areas outside the three modules that had been addressed.

The reduction was not immediate. After Phase 1, the incident rate dropped to approximately 28 per month, primarily because faster detection reduced the time that individual incidents remained active but did not prevent them from occurring. After Phase 2, the rate dropped to approximately 12, because the notification and synchronisation incidents were largely eliminated. After Phase 3, the rate reached 4.

The time taken per incident also decreased significantly. Before the remediation, incidents in the transaction module required hours to diagnose because the code was difficult to reason about and monitoring was sparse. After the refactoring, the same class of incidents could be diagnosed in minutes because the code had clear boundaries and monitoring covered the key paths.

The delivery side improved as well. With less time consumed by incident response, the team had capacity to maintain the quality improvements and to start addressing debt in the parts of the codebase outside the three primary areas.

What Made the Difference

Several factors made the outcome measurable and durable.

Sequencing the work by impact. Addressing the three highest-incident areas first produced results fast enough to justify continuing the work. If the team had started with a general refactoring effort without prioritising by incident frequency, the early results would have been slower to appear and harder to justify.

Not stopping feature work. The remediation was structured to run alongside product development. Phase 1 required minimal engineering time. Phases 2 and 3 were scoped to use the capacity freed by incident reduction. At no point did the team stop shipping product.

Treating incidents as debt signals. Every incident was logged with its root cause area during the project. This data guided prioritisation and demonstrated progress in terms that the business understood: fewer incidents, faster resolution, more product delivery.

Clear definition of done for each phase. Phases had explicit completion criteria: specific incident types eliminated, specific code changes deployed, specific test coverage thresholds reached. This prevented scope creep and gave the team measurable milestones to work toward.

Conclusion

This technical debt case study demonstrates that structured remediation produces measurable results, even in codebases with years of accumulated debt. The key is starting with diagnosis rather than prescription, sequencing work by impact, and maintaining delivery throughout.

Reducing incidents from 40 to 4 per month was not achieved by rewriting the platform. It was achieved by addressing the specific structural problems that were generating the most incidents, in a sequence that allowed each phase to fund the next.

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