Predictive Modeling & Transaction Audit System for Financial Platform

Project Goal

For a client in the financial services space, the project aimed to develop a robust AI system for behavioral prediction and quantitative analysis of customer transactions, along with a comprehensive audit of payout and balance calculations. The objective was twofold:

• Detect potential fraud and collusion by modeling user behavior across large datasets.
• Guarantee transaction and payout accuracy for a new suite of financial products, ensuring correctness even in highly complex edge cases.

Challenges

This project came with a unique set of challenges:

• Domain Expertise Barrier: The work began with no prior knowledge of the client's specific transaction domain. Critical concepts—ranging from product mechanics to academic research on strategic customer behavior—had to be absorbed quickly.
• Massive and Unstructured Data: The data consisted of hundreds of gigabytes of raw JSON logs detailing millions of transactions. This required efficient data processing pipelines and custom-built parsers to convert data into a usable format.
• High-Stakes Accuracy: The audit had zero tolerance for miscalculation. Edge-case complexity was extreme—requiring a deep understanding of business rules, rounding errors, and multi-party transactions.
• Behavioral Modeling Complexity: Understanding and simulating customer behavior in uncertain environments required statistical modeling and probabilistic simulations, including Monte Carlo methods.

Solution

We delivered a comprehensive AI-powered system that combined fraud detection, transaction auditing, and behavioral prediction to ensure financial accuracy and security across all customer interactions.

Key System Components

Design Decisions

• MongoDB was chosen over traditional RDBMS to handle the semi-structured nature of transaction logs and flexibility in schema evolution.
• LightGBM provided a performant, interpretable baseline model—essential for understanding fraud-like behavior before deeper neural architectures were introduced.
• PyTorch allowed greater flexibility for modeling sequences and experimenting with temporal embeddings.
• Using W&B enabled transparent experiment tracking and model comparisons over dozens of iterations.

If you're looking for scalable SaaS design, deep integration with complex APIs, or predictive tooling for real-world operations—this project is a proven case study of robust, end-to-end execution.