AI Automation for Fintech

The top three by ROI: document verification and KYC (reducing time from hours to minutes), transaction monitoring for anti-fraud (real-time detection instead of manual review), and customer inquiry processing (80% of questions resolved by AI without an operator). All three areas pay for themselves in 2-4 months.

We use explainable AI approaches: SHAP values for credit scoring (showing the weight of each factor), rule extraction for anti-fraud (translating ML decisions into readable rules), and audit logs with full context for every decision. The regulator can request an explanation for any automated decision.

Every AI model works with a human-in-the-loop: credit scores below the threshold go to manual underwriting, anti-fraud alerts are reviewed by an analyst, chatbot responses with low confidence are handed to an operator. Plus — automatic rollback when anomalous patterns are detected.

AI scoring processes an application in 2-3 seconds instead of 24-48 hours with manual underwriting. The model analyzes alternative data (transactions, website behavior, digital footprint), enabling approval of borrowers without credit history. Accuracy is 15-25% higher than classic scorecards, reducing default rates.

The minimum dataset for a first model is 50,000 transactions with labels (fraud / not fraud). More data means a more accurate model. Beyond transaction data, we use geolocation, device characteristics, time of day, and behavioral patterns (typing speed, navigation). For cold start, we use a rule-based system while accumulating data for ML in parallel.

Yes, but incrementally. At the start, we use APIs of ready-made models (OpenAI for chatbot, third-party scoring APIs) — this delivers quick results without the cost of training custom models. As data accumulates, we transition to proprietary ML models that more accurately account for your product and audience specifics.