Transforming Legacy Fintech for the Machine Learning Era

When a fintech company first approached us, their loan servicing platform was processing millions of dollars in transactions while showing increasingly serious signs of strain. The system, originally designed for mortgage processing, had been adapted over eleven years to handle simple interest loans through layers of technology: VB6 and VB.NET at its core, wrapped in Ruby on Rails for web functionality. While the business remained profitable, system instability was threatening future growth and consuming massive operational resources.

The client staffed an oversized team, which was required to work around the clock to manage system failures, and they suffered from frequent critical security and operational incidents. Each outage directly impacted revenue, and the frequent 4 AM emergency calls indicated a system that had evolved beyond its architectural foundations.

Traditional consulting approaches would have prescribed a large team and lengthy analysis phase. Instead, we deployed a small group of senior engineers with deep financial domain expertise. Our team's combined background in financial systems and entrepreneurial experience enabled them to quickly grasp both technical and business implications of the system's challenges.

Within a few months, we reduced the number of critical incidents dramatically, and put the client in a position to right-size their support staff. By the six-month mark, our small team had assumed full operational responsibility, demonstrating how focused expertise could outperform larger, less specialized teams.

With operational stability established, we uncovered a deeper opportunity. The company possessed many years’ worth of valuable credit data spread across seven distinct data sources. This information, stored in various formats across S3 buckets and SQL databases, represented untapped potential for business intelligence and competitive advantage.

Our modernization effort transformed this raw data into actionable intelligence while maintaining business operations. The solution went beyond basic data consolidation, creating a sophisticated model waterfall system that fundamentally changed how the platform handled data provider dependencies. This new architecture eliminated system-wide outages from provider failures while reducing decision times to under 45 seconds.

The final phase of transformation revolutionized the platform's capabilities. We helped the client establish their first true data science practice, creating an infrastructure that could finally capitalize on their vast repository of alternative credit data. The new platform enabled sophisticated machine learning workflows that were previously impossible, allowing them to develop and deploy more accurate underwriting models with unprecedented speed.

The system's new architecture decoupled them from external provider dependencies, transforming a previous weakness into a competitive advantage. Where provider outages once caused system-wide failures, the platform now gracefully adapts using a sophisticated model waterfall. This innovation allows them to maintain rapid credit decisions even when data sources are unavailable, while protecting their rigorous credit risk parameters.

The migration resulted in large-scale transformation. We engineered a meticulous transition of their complex data ecosystem, handling massive volumes of credit data, servicing records, and historical performance metrics. Through careful orchestration and automated testing, we achieved perfect data accuracy without a single incident of business interruption.

Today, the platform has evolved from an operational liability into a strategic asset. The transformation unlocked several key capabilities:

Advanced Credit Decisioning: The new infrastructure enables rapid model iteration and sophisticated risk assessment. The client can now deploy new underwriting strategies in days rather than months, responding quickly to market opportunities while maintaining strict risk controls.

Data Science Innovation: Their data science team can now easily access and analyze years of valuable credit data. The modern data architecture enables them to test new hypotheses, develop innovative credit models, and gain insights that were previously locked away in disparate systems.

Operational Excellence: The platform now sets industry standards for reliability and performance. What once required constant intervention now runs smoothly with minimal support, allowing the technology team to focus on innovation rather than maintenance.

This transformation demonstrates how deep domain expertise and systematic modernization can turn technical constraints into market advantages. The client now operates at the fore of lending technology, with a platform that can rapidly adapt to new opportunities and market conditions. Their ability to quickly test and deploy sophisticated credit models, combined with their resilient infrastructure, positions them to continue pushing the boundaries of what's possible in modern lending.