Intelligent Risk Assessment for Major US Bank

The Challenge

A Fortune 100 bank was struggling with their legacy fraud detection system. The existing rule-based approach generated excessive false positives, frustrating legitimate customers and requiring costly manual review processes.

Additionally, their credit risk assessment relied heavily on manual document review, with loan officers spending hours analyzing financial statements, tax returns, and supporting documentation.

Our Approach

Phase 1: Data Foundation

We began by consolidating transaction data into BigQuery, creating a unified view of customer behavior across:

• Real-time transaction streams
• Historical transaction patterns
• Customer profile information
• External data enrichment

Phase 2: ML Model Development

Using BigQuery ML, we developed a sophisticated fraud detection ensemble:

1. Anomaly detection for unusual transaction patterns
2. Gradient boosted classifiers for known fraud patterns
3. Graph analytics for identifying fraud rings

The models were trained on 3 years of historical data, with careful attention to:

• Class imbalance handling
• Temporal validation (no data leakage)
• Feature engineering for velocity and behavioral patterns

Phase 3: Document Intelligence

For credit risk assessment, we implemented a Gemini-powered document analysis pipeline:

• Automated extraction of key financial metrics from statements
• Cross-document validation and consistency checking
• Risk factor identification and summarization

Technical Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Transaction    │────▶│    Pub/Sub      │────▶│    Dataflow     │
│    Sources      │     │                 │     │   Processing    │
└─────────────────┘     └─────────────────┘     └────────┬────────┘
                                                         │
                                                         ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Alert &       │◀────│   BigQuery ML   │◀────│    BigQuery     │
│   Action        │     │   Inference     │     │   Feature Store │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Results

Quantitative Outcomes

Metric	Before	After	Improvement
False Positive Rate	8.2%	4.9%	40% reduction
Detection Latency	2.3s	87ms	96% faster
Manual Review Volume	45K/month	18K/month	60% reduction
Document Processing	45 min avg	3 min avg	93% faster

Business Impact

• $12M annual savings from reduced manual review
• 23% improvement in customer satisfaction scores
• Regulatory compliance maintained with full audit trails

Key Learnings

1. Start with data quality: The model is only as good as the data. We spent significant time on data cleansing and feature engineering.

2. Human-in-the-loop: For high-stakes decisions, we maintained human oversight while dramatically reducing the volume requiring review.

3. Continuous improvement: The fraud landscape evolves constantly. We implemented automated retraining pipelines to keep models current.

Client Testimonial

“GCPFlow’s team didn’t just build a fraud detection system—they transformed how we think about risk. The combination of real-time ML and document intelligence has given us capabilities we didn’t think were possible.”

— Chief Risk Officer, Fortune 100 Bank

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Ready to transform your risk assessment capabilities? Schedule a consultation to discuss your challenges.