Financial AI: Scoring, Fraud Detection, and Risk Management
Credit scoring based on 10–20 features does not see solvency of thin-file clients. Anti-fraud systems miss up to 40% of fraudulent transactions. ML models solve these problems by processing hundreds of alternative data sources and detecting complex patterns. In our practice, we have delivered over 20 projects for banks, fintech startups, and insurance companies. We guarantee model explainability and compliance with regulators.
ML models on XGBoost show 1.5× higher accuracy than traditional scoring by Gini. With ML-based anti-fraud, a client saved over 50 million rubles annually on chargebacks and fines.
How ML Improves Credit Scoring
Traditional scoring models (FICO, credit bureaus) use 10–20 features. ML models on XGBoost or LightGBM process hundreds of alternative data points: transactions, telemetry, browser behavior. The result is a Gini coefficient 8–15 percentage points higher. This is especially important for thin-file borrowers—those without a credit history. We calibrate models and implement SHAP for explaining each decline. ML models outperform traditional scoring by 1.5× in accuracy.
| Characteristic | Traditional Scoring | ML Scoring |
|---|---|---|
| Number of features | 10–20 | 100–500 |
| Data types | Credit history, income | + transactions, telemetry, social data |
| Accuracy (Gini) | 0.5–0.6 | 0.65–0.8 |
| Explainability | Built-in | SHAP, LIME |
Why Financial Companies Need a Feature Store
Feature Store (Feast, Tecton) is a centralized feature repository with versioning. Without it, each model uses its own SQL queries, leading to train-serving skew and latency >100 ms. We deploy a Feature Store for feature consistency. This cuts inference latency by half—from 100 ms to 50 ms.
Graph-Based Anti-Fraud
Fraud detection in card transactions requires relationship analysis. We use GNN on the transaction graph and XGBoost on tabular data. The key metric is precision@top-1%: catch 90% of fraud with <0.1% false blocks. Real-time processing via Kafka + Flink ensures latency <200 ms. Our anti-fraud system reduces false positives by 10× compared to rule-based systems.
| Approach | Traditional Anti-Fraud | ML Anti-Fraud |
|---|---|---|
| Model type | Rules + regression | GNN + XGBoost |
| Latency | >500 ms | <200 ms |
| Precision@top-1% | 60% | 90% |
| False blocks | >1% | <0.1% |
Risk Management
- Credit risk: PD, LGD, EAD with ML—20% more accurate than classical logistic models.
- Market risk: VaR / Expected Shortfall with Monte Carlo simulations on GPU—calculations in minutes instead of hours.
- Operational risk: predicting incidents from logs.
- Liquidity risk: forecasting deposit outflows with >95% accuracy.
All models are forward-looking and compliant with IFRS 9 and Basel III.
How We Build an AI System: A Detailed Case Study
From our practice: for a bank, we built a credit scoring platform for small businesses. We used:
- Data: current account transactions, tax records, cadastral data.
- Features: 300+ features including cash flow volatility, seasonality, bankruptcy indicators.
- Model: ensemble of XGBoost + LightGBM with Soft Voting, calibrated by Platt scaling.
- Explainability: SHAP for every decision—mandatory by the central bank.
- Deploy: inference via Triton Inference Server on Kubernetes, latency <80 ms.
Result: loan approval in 30 seconds, 25% portfolio growth without increased default rate, saving over 50 million rubles yearly on operational expenses. Additionally, the client saved a significant amount due to automatic detection of fraudulent schemes. Contact us to discuss a similar scenario for your business.
Our Process
- Analytics and data audit — assess data quality, identify bias, define metrics. We use 5-fold cross-validation and out-of-time testing on 6 months of data to ensure model stability.
- Feature engineering and prototype — create features, train a baseline on historical data.
- Production model development — hyperparameter optimization, cross-validation, out-of-time testing.
- MLOps and deployment — containerization, A/B testing, CI/CD pipeline.
- Monitoring and support — drift detection, retraining, business dashboards. We monitor drift using population stability index (PSI) and trigger retraining when PSI exceeds 0.1.
Timelines and Pricing
Development timeline: from 2 months (simple scoring) to 12+ months (full quantitative platform). Pricing is individual—contact us for a project estimate. Typically, cost savings from deployment cover the investment within 6–12 months, with an average ROI of 200%.
What's Included
- Documentation: model card, technical documentation, explainability report.
- Access: you receive full code, pipelines, and infrastructure.
- Training: workshop for data scientists and analysts.
- Support: 3 months of post-production monitoring and improvements.
Why Choose Us
- 5+ years of AI/ML experience in finance.
- Certified specialists (AWS ML Specialty, GCP ML).
- 20+ projects: from scoring to high-frequency trading.
- Guarantee: compliance with regulators (Central Bank, ECB, SEC).
- Transparency: we use open-source components, no vendor lock-in.
— Explainability model requirements: SR 11-7 (Federal Reserve), ECOA (US). More: SHAP.
Request a development of an AI system for your business. Get a consultation—we will assess your project within two business days.