Scoring models may decide in milliseconds, but the quality of those decisions comes from months (and often years) of engineering, iteration and late-night debugging. And here’s the thing: scoring is the core of the fintech lending business. The ability to predict repayment probability and to do it reliably is what defines margins, risk and competitiveness. That’s exactly how they build their edge in the market.
We spoke with our fintech veteran and Chief Strategy Officer Ivan Shkarupa about what actually goes on behind the scenes of scoring. Turns out it is messier, more interesting and way more human than the case studies sometimes show.
The Reality of Data Pipelines: It’s Messier Than You Think
If scoring is the assembly line for financial decisions, it’s also the profit engine. Get it right, and you have a business advantage that everything else revolves around.
People love to talk about the sexy parts: machine learning algorithms, real-time decisions, sophisticated risk models. But anyone who’s actually built a scoring system knows the real challenge is making all those pieces work together in a way the business can rely on.
Ivan describes the typical scoring architecture like this:
“Scoring in fintech is like an assembly line for decisions. We collect data from different sources, clean it up, and turn it into understandable features. A model version is trained and stored on them. When a request comes in, the service pulls the necessary features, calculates risk, and along with a set of simple rules, issues a decision (score) + (possibly reasons too)”.
Every station has to work perfectly, and they all have to work together. The entire system needs to handle real-world complexity while staying fast, reliable, and explainable.
The Data Juggling Act
Working with financial data means dealing with sources that have their own personalities. Internal transaction data, credit bureaus, open banking APIs, accounting systems, public registries – each one speaks a different language and has different ideas about what constitutes “clean” data.
“We collect data from various sources, clean it and convert it into a single format. We then compare and align the data in time to the ‘decision moment’, count the indicators and store them in a repository. Finally, we monitor them. The hardest part: data matching, controlling analysis “at the moment of decision” (not taking future events into account), resilience to gaps and sudden changes in sources”.
When External APIs Have Moods
Integration with external services – credit bureaus, KYC providers, document verification – is where things get really fun. These aren’t just technical integrations, they are relationships. And like all relationships, they have good days and bad days.
Our experience spans everything from major providers to specialized fintech tools. Each integration teaches you something new about resilience, failover strategies and the art of graceful degradation when a critical service decides to take an unscheduled nap.
Business Logic: Where Theory Meets Reality
Here’s where scoring systems get genuinely complex. Yes, in common it is about building a model that can predict default rates. But more importantly, it is about embedding that model into a world of regulatory constraints, industry-specific rules, regional variations and product parameters that change based on moon phases (okay, maybe not moon phases, but sometimes it feels like it).
“The most difficult thing is to combine the “hard rules” (like limits and regulatory) with scoring and escape the drown in segments and exceptions, properly coordinate antifraud and credit risk, make decisions only based on data that exists now, and still keep the model stable and explainable – with a change log and the ability to quickly roll back the fix”.
Translation: you are building a system that needs to be simultaneously rigid (regulatory compliance) and flexible (business needs), fast (real-time decisions) and careful (explainable outcomes), simple (maintainable) and sophisticated (effective risk assessment).
Real-Time Decisions in a Complex World
Making scoring decisions in real-time means your architecture needs to be bulletproof. Our experience with one of our long-term partners involved a core system with all the logic and rules, surrounded by multiple microservices, each handling specific tasks like parsing bank account money movement data.
“Each “step-microservice” has its own team of responsible people, executing strict procedures. They accept data at the input — they give a decision at the output. The process worked in two modes: automatic and semi-automatic. In the automatic mode, intake rules were triggered first, and then staff could step in if needed”.
Many stages. Different teams. One decision. And it all needs to happen fast enough that the user doesn’t get bored and wander off.
When Things Go Sideways (And They Will)
The most valuable lessons come from the moments when something breaks. Our team has some stories about critical business logic problems discovered on Friday, leading to weekend-long debugging sessions and Tuesday morning production deployments.
“The lesson is that in fintech there’s no concept of “tested before” – you need to test everything completely, the entire chain, always. Plus: real-time monitoring for anomalies and quick reactions are a must, because this is money and delays cost”.
That’s not paranoia. That’s wisdom earned through experience. Every change, no matter how small, gets the full treatment because financial systems don’t do “mostly working”.
Looking Forward
“The future of scoring models? Well, it is definitely not replacing the core risk decision engine with large language models. It is more about making scoring richer and more understandable through closer integration of LLM models around scoring, but not instead of it”.
The mathematical models handle core risk assessment, they are proven and regulated. LLMs become the intelligent layer that makes scoring more comprehensive and actionable while maintaining the stability financial institutions require.
What We’ve Learned About Doing Fintech Right
After years of building these systems, a few principles have crystallized:
Subject matter experts are critical from day one. You can’t build a good scoring system without people who understand both the technical and business sides deeply.
Analysis and description come first, then proof of concept, then implementation. Rushing to code without understanding the problem thoroughly is a recipe for expensive rewrites.
Knowledge base from the start, maintained and regularly updated. Documentation is the difference between a maintainable system and a house of cards.
Team stability is everything.
“The project team should be as stable as possible from the start: only expansion, no replacement of key knowledge carriers”.
The Human Side of Automated Decisions
At the end of the day, scoring systems are about people making financial decisions about other people, just with really sophisticated math in between. The models decisions affect real lives: someone’s ability to get a loan for their business, buy a house or weather a financial emergency.
That’s why we obsess over the details and treat every model deployment like it matters. Because it does.
The relationship between scoring systems and the teams that build them is collaborative. The models provide the mathematical foundation, but the engineering, iteration, and yes, weekend debugging sessions are what turn those models into reliable, explainable and fair decision-making tools.
Building fintech systems that work is understanding that behind every decision is months of careful engineering. It is respecting both the power and the responsibility that comes with automating financial decisions.
And sometimes, it’s about being prepared to spend your Friday night making sure everything still works on Monday morning.
