If you’re running a fintech or BFSI SaaS platform, you already know the tension. Your compliance team wants faster audit trails. Your sales team wants a new payment gateway integration by next quarter. And somewhere in the middle, your engineering lead is quietly telling you that the codebase is starting to push back every time you ask for something new.
That pushback has a name: technical debt. And in regulated industries like BFSI, it’s not just an engineering headache — it’s a business risk. We’ve worked with several fintech clients at Speqto Technologies, and the pattern is almost always the same: debt accumulates fastest right after a product hits product-market fit, because that’s when everyone starts prioritizing speed over structure.
A B2C app can get away with a messy database schema for a while. A lending platform processing KYC documents or a payments SaaS handling settlement reconciliation cannot. One of our clients, a digital lending platform in the NBFC space, had a loan disbursement module that had been patched so many times over 18 months that a simple RBI-mandated reporting change took their team three weeks instead of three days. That’s technical debt showing up as a compliance risk, not just a code quality issue.
The core problem is that debt in these systems compounds silently. Every shortcut taken to hit a funding milestone or a regulatory deadline adds interest that gets paid later — usually at the worst possible time, like during an audit or a scaling event.
Most teams try to “clean up code” without first measuring where the actual pain is. We usually start client engagements with a technical debt audit that maps three things:
For a payments client we worked with, this exercise revealed that 70% of production incidents over six months came from just two services: the webhook handler and the reconciliation engine. That told us exactly where to focus, instead of doing a blanket “refactor everything” sprint that would have eaten a quarter of runway for uncertain gains.
Engineering teams often lose the argument for refactoring time because they pitch it in terms of “cleaner code.” Founders and CTOs in BFSI respond better to numbers tied to revenue or risk. When we proposed rebuilding the reconciliation engine mentioned above, we framed it as: this reduces settlement discrepancies that currently require 4 hours of manual finance team intervention per week, and it cuts the risk of a reporting error during the next audit cycle. That got budget approved in one meeting instead of three.
If you’re a decision-maker, ask your engineering leads to bring debt-reduction proposals with a “cost of inaction” attached — support tickets generated, hours spent on manual workarounds, or incidents per quarter.
We’ve seen teams try to “fix everything” in a dedicated tech-debt sprint, and it almost always backfires — stakeholders start asking why a whole sprint produced no visible feature, and the next sprint reverts to 100% feature work.
A more sustainable approach: reserve roughly 20% of every sprint for debt reduction, tied directly to the areas you identified in your audit. One of our insurance-tech clients used this model for a year and cut their average bug resolution time by 35%, without ever pausing feature delivery for stakeholders.
Just like a company sets a budget ceiling, set an engineering rule: no new feature ships without basic test coverage on the code paths it touches, especially for anything involving money movement or personal data. This one rule, when enforced strictly, prevents debt from being added faster than it’s paid down. It feels slow in month one. By month six, teams ship faster because they’re not firefighting regressions.
Code isn’t the only place debt hides. We’ve seen BFSI clients running on database configurations chosen for a 10,000-user MVP that are still in place at 2 million users. A microlending client of ours was paying nearly 40% more in cloud costs than necessary simply because auto-scaling was never revisited after their initial launch config. Infrastructure debt is easy to overlook because it doesn’t throw errors — it just quietly drains budget and performance headroom.
Reducing technical debt isn’t about achieving some mythical “clean codebase.” It’s about making your delivery speed predictable so that when the next RBI circular or PCI-DSS update lands on your desk, your team can respond in days, not months. That predictability is what actually protects your growth trajectory — and it’s usually worth more to a BFSI board than any single new feature.
At Speqto Technologies, we’ve found that the products which scale smoothly aren’t the ones with zero debt — they’re the ones that treat debt reduction as a continuous, measured practice rather than a one-time cleanup. If your roadmap keeps slipping and nobody can quite explain why estimates are always wrong, that’s usually where the conversation needs to start.
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