TransEuroBank Fraud Detection Case Study

Project Overview: Transaction Fraud Detection System

Company Profile

Organization: TransEuroBank
Size: €45B in assets
Customers: 3.2M retail customers
Problem: €18M annual fraud losses

TransEuroBank, a mid-sized European bank, faced increasing fraud losses and regulatory pressure to improve detection systems. The bank launched an enterprise-wide fraud detection initiative focusing on card and payment fraud.

Business Context & Challenges

Initial Situation

Manual review of 2,500+ daily fraud alerts, with 92% false positives
Detection lag of 12+ hours allowing fraudsters to complete multiple transactions
Fragmented systems across credit cards, debit cards, and online banking
Heavy reliance on static rules creating blind spots for new fraud patterns

Key Business Objectives

Reduce fraud losses by 30% within 12 months
Decrease false positive rate by 40% to improve operational efficiency
Detect fraud in near real-time (under 3 minutes) to enable proactive blocking
Maintain customer experience with minimal legitimate transaction disruption

Phase 1: Data Assessment & Architecture

Data Sources Inventory

Core transaction data (~3.5M daily transactions)
Customer profile/demographic data (3.2M customers)
Device/channel information (web, mobile, ATM, POS)
Historical fraud cases (72,000 confirmed cases over 3 years)
Geographic data (locations, merchant categories)
External data feeds (compromised card lists, known fraud patterns)

Sample Data Structure

TRANSACTION_DATA:
- transaction_id: unique identifier
- customer_id: customer identifier
- timestamp: transaction time
- amount: transaction amount
- merchant_id: merchant identifier
- merchant_category_code: industry classification
- channel_id: transaction channel 
- device_id: unique device identifier (for digital channels)
- ip_address: originating IP (for online transactions)
- location_coords: geolocation data
- auth_method: authentication method used

CUSTOMER_DATA:
- customer_id: customer identifier
- age: customer age
- tenure: years with bank
- product_portfolio: list of banking products
- avg_balance: average account balance
- transaction_patterns: typical transaction behavior
- risk_segment: bank's risk classification

FRAUD_CASES:
- transaction_id: fraudulent transaction identifier
- fraud_type: classification of fraud pattern
- detection_method: how fraud was discovered
- time_to_detection: time between transaction and detection
- customer_impact: financial and non-financial impact
- recovery_amount: amount recovered if any

Key Data Challenges

Data Quality Issues:

15% of transaction records had incomplete merchant information
Card and online banking systems used different customer identifiers
Timestamp inconsistencies across systems (local vs. UTC)
Batch processing delays in data availability

Privacy Constraints:

GDPR limitations on data retention and processing
PCI-DSS requirements for card data protection
Restrictions on using sensitive customer attributes

Technical Architecture Hurdles:

Legacy mainframe system processing card transactions in batch mode
Limited real-time API capabilities in core banking system
Siloed data warehouses for different products
No unified customer view across channels

Architecture Solution

Implemented Kafka-based event streaming platform for real-time transaction data
Created data virtualization layer to provide unified view without physical consolidation
Deployed secure, tokenized access to PCI-regulated data
Built feature store for model-ready data with appropriate privacy controls

Phase 2: Analytics Development

Multi-Layered Detection Approach

Rules Engine Layer

Redesigned 250+ existing rules for efficiency
Implemented velocity checks (transaction frequency, amount changes)
Geographical impossibility detection (transactions in multiple locations)
High-risk merchant category monitoring

Behavioral Analytics Layer

Customer-level behavioral profiles with 120+ features:

- Transaction amount distributions by merchant category
- Typical transaction times and days
- Common geographic locations
- Regular transaction sequences
- Device usage patterns
- Authentication method preferences

Anomaly scoring based on deviation from established patterns
Behavioral clustering to identify customer segments

Machine Learning Layer

Gradient boosting model for transaction risk scoring
Neural network for pattern detection across multiple transactions
Network analysis for related fraud identification

Example Feature Engineering

# Sample feature engineering code for transaction velocity
def calculate_velocity_features(customer_transactions):
    features = {}
    
    # Time-based velocity
    time_windows = [1, 6, 24, 72]  # hours
    for window in time_windows:
        window_txns = filter_by_timeframe(customer_transactions, window)
        features[f'txn_count_{window}h'] = len(window_txns)
        features[f'txn_amount_sum_{window}h'] = sum(t.amount for t in window_txns)
        features[f'merchant_count_{window}h'] = len(set(t.merchant_id for t in window_txns))
        features[f'channel_count_{window}h'] = len(set(t.channel_id for t in window_txns))
    
    # Location-based velocity
    for window in time_windows:
        window_txns = filter_by_timeframe(customer_transactions, window)
        locations = [t.location_coords for t in window_txns if t.location_coords]
        features[f'location_count_{window}h'] = len(set(locations))
        features[f'max_distance_{window}h'] = calculate_max_distance(locations)
        features[f'location_entropy_{window}h'] = calculate_entropy(locations)
    
    return features

Machine Learning Challenges

Class Imbalance:

Only 0.02% of transactions were fraudulent
Solution: Combination of SMOTE oversampling and cost-sensitive learning

Concept Drift:

Fraud patterns evolving rapidly (30% change in patterns quarterly)
Solution: Online learning with partial retraining and champion/challenger model framework

Explainability Requirements:

Compliance mandated explainable decisions for customer impact
Solution: SHAP values for feature importance and prediction explanations

Model Performance Metrics

Baseline Rules Engine

Precision: 8% (92% false positive rate)
Recall: 65% (35% missed fraud)
Average detection time: 12 hours

New ML-Powered System

Precision: 42% (58% false positive rate)
Recall: 83% (17% missed fraud)
Average detection time: 2.5 minutes

Phase 3: Implementation & Operations

Deployment Architecture

Streaming analytics processing pipeline with sub-second latency
Real-time scoring API for authorization integration
Tiered alert system with risk-based routing
Feedback capture system for continuous improvement

Operational Integration

Alert Management System

Risk-based triage routing (high/medium/low)
Context-rich investigation interface
One-click feedback mechanism for investigators

Intervention Framework

Automated blocking for highest-risk transactions
Step-up authentication for medium-risk transactions
Post-transaction verification for lower-risk anomalies
Custom intervention strategies by customer segment

Monitoring & Governance

Real-time performance dashboards:

- Fraud detection rate by channel/product
- False positive rates by alert type
- Average time to resolution
- Model drift indicators
- Rule contribution analysis

Daily model performance review
Weekly model retraining based on confirmed cases
Monthly governance committee review

Major Implementation Challenges

System Integration:

Legacy authorization system couldn't support real-time API calls
Solution: Created intermediate decision cache with 100ms response time guarantee

Change Management:

Fraud analysts resistant to ML-based recommendations
Solution: Side-by-side comparison period and "hybrid" review mode during transition

Alert Volume Scaling:

Initial deployment generated 300% more alerts than capacity
Solution: Progressive risk threshold tuning and capability-based alert routing

Business Outcomes & Lessons Learned

Quantitative Results (After 12 Months)

37%

Reduction in fraud losses

€6.7M annual savings

58%

Decrease in false positive rate

82%

Reduction in time-to-detection

99.7%

Legitimate transaction acceptance rate

Qualitative Impacts

Improved customer experience with fewer unnecessary declines
Enhanced regulatory standing with documented fraud controls
Fraud team transformation from data entry to analytical investigation
Organizational shift toward data-driven decision making

Key Success Factors

Cross-Functional Leadership

Joint business-technology steering committee
Fraud operations deeply involved throughout development
Clear executive sponsorship from Chief Risk Officer

Incremental Implementation

Phased deployment by product and channel
Side-by-side operation with legacy system
Graduated risk thresholds during initial period

Feedback Integration

Analyst feedback captured on every alert
Weekly model refinement cycles
Regular calibration sessions with fraud teams

Critical Lessons

Technical Perspectives

Real-time detection requires fundamental architecture changes
Privacy-preserving feature engineering is essential
Model explainability cannot be an afterthought

Organizational Insights

Fraud analysts need to become model partners, not just users
Executive sponsorship must extend through operational transition
Success metrics should balance fraud reduction with customer experience

Process Realizations

Detection is only half the solution; intervention strategies are equally critical
Continuous feedback loops are essential for model effectiveness
Governance frameworks must adapt to algorithmic decision-making

Educational Discussion Points for MBA Students

Strategic Considerations

How does fraud detection balance between customer experience and security?
What organizational structure best supports advanced analytics initiatives?
How should ROI be calculated for fraud prevention systems?

Technical Discussion Topics

When is real-time processing truly necessary versus batch processing?
How can financial institutions balance regulatory requirements with advanced AI adoption?
What data governance frameworks support both innovation and compliance?

Implementation Questions

What change management approaches work for analytical transformations?
How should banks handle the transition period between legacy and new systems?
What skills transformation is required for existing fraud teams?

Future Considerations

How will evolving privacy regulations impact fraud detection capabilities?
What opportunities exist for cross-bank collaboration on fraud prevention?
How might emerging technologies (blockchain, digital identity) transform fraud detection?

Comprehensive Fraud Detection Project: TransEuroBank Case Study