Case Study Real-Time Fraud Detection in UK Digital Payments App

A

Case study

Real-Time Fraud Detection in UK Digital Payments App

Challenges

The client faced critical fraud management challenges as their transaction volumes grew exponentially, while sophisticated fraud attempts increased in frequency and complexity. Their existing rule-based detection system was creating security vulnerabilities and user experience problems.

Escalating Fraud Losses: Sophisticated attack patterns bypass static rule-based detection, with fraud losses increasing from 0.08% to 0.14% of transaction volume in just six months.
False Positive Disruption: Rigid rules generated excessive false alerts (28% of flagged transactions) that disrupted legitimate user experiences and created a substantial operational burden.
Detection Speed Limitations: Batch-oriented fraud analysis created detection delays of 30-45 minutes, allowing connected fraud attempts to continue before detection.

Aspagnul Solution

Aspagnul implemented a comprehensive real-time fraud intelligence system combining behavioral analytics, network analysis, and advanced machine learning to identify suspicious patterns with unprecedented accuracy and speed. The solution operated continuously across all transaction types while adapting to emerging fraud techniques.

Multi-Dimensional Behavioral Analysis: Sophisticated models analyzed hundreds of factors simultaneously, including device characteristics, transaction patterns, location data, and session behaviors to identify anomalies invisible to conventional approaches.
Real-Time Network Intelligence: Advanced graph analytics identified hidden connections between seemingly unrelated accounts, detecting coordinated fraud rings and money mule networks before losses occurred.
Adaptive Learning System: The solution continuously improved through supervised and unsupervised machine learning, automatically identifying emerging fraud patterns without requiring manual rule updates.

Timeline & Process

Week 1-2: Fraud Pattern Assessment

Comprehensive analysis of historical fraud cases, transaction patterns, and existing detection approaches to identify specific improvement opportunities.

Week 3-6: Model Development & Training

Implementation of behavioral models, network analytics, and machine learning components using anonymized historical data for initial training.

Week 7-8: Real-Time Integration

Development of streaming data connections, alert workflow integration, and performance optimization for real-time operation.

Week 9-10: Testing & Validation

Parallel operation alongside existing systems with comprehensive performance analysis and tuning based on results.

Week 11-12: Phased Deployment

Controlled implementation starting with specific transaction types and gradually expanding to full coverage with continuous performance monitoring.

Integration Approach

The fraud detection system integrated the client’s technology infrastructure through a sophisticated real-time architecture. A dedicated streaming data pipeline established millisecond-level visibility into all platform activities, including logins, navigation patterns, and transactions. The solution implemented direct API integration with the payment processing infrastructure for data ingestion and transaction intervention when necessary.

Custom connectorsare linked with the existing case management system for seamless handoff of fraud alerts to investigation teams. The implementation included a specialized database integration layer providing secure access to historical user and transaction data for model context while maintaining strict data protection.

All integrations are operated within a secure enclave with comprehensive encryption and access controls.

Compliance Considerations

The solution addressed multiple regulatory frameworks essential for payment providers operating in the UK and Europe:

Results

The implementation delivered substantial improvements across fraud prevention, operational efficiency, and user experience:

01

Fraud Reduction:

Decreased fraud losses by 72% as a percentage of transaction volume
Improved fraud detection rate from 76% to 94% of actual cases
Reduced average detection time from 38 minutes to 382 milliseconds
Prevented £4.3M in projected fraud losses within the first six months

02

Operational Enhancement:

Reduced false positive rate from 28% to 7.5% of flagged transactions
Decreased manual review requirements by 68%
Increased investigator efficiency by 340% through better case prioritization
Eliminated the need for overnight batch analysis processes

03

User Experience Improvement:

Client Impact

The real-time fraud intelligence system transformed the company’s risk management capabilities from a growing liability into a competitive advantage. The dramatic reduction in fraud losses and false positives enabled the payment platform to offer more seamless user experiences while improving security, directly enhancing their market position against larger competitors.

The solution’s ability to scale automatically with transaction volume eliminated the previous direct relationship between growth and fraud exposure, removing a critical concern from the company’s expansion plans. The enhanced detection capabilities also provided confidence to launch new product features previously delayed due to fraud considerations.

Perhaps most significantly, the improved fraud metrics substantially reduced the company’s risk profile with banking partners and regulators, enabling more favorable processing terms and accelerating regulatory approvals for expansion into additional European markets. Based on these results, the client subsequently engaged Aspagnul to implement additional intelligence capabilities across their compliance and treasury operations.
d business performance.

Quick Links

Legal

Contact

Follow:

A