Business Challenge

To safeguard client data and combat fraudulent activities, especially social engineering fraud, the bank required a solution capable of accurately detecting and preventing unauthorized transactions while maintaining a seamless customer experience. 

The main challenge was to develop a system that could identify and stop fraudulent transactions in real time, without disrupting legitimate customer operations. Achieving the right balance between enhanced security and a smooth user experience was critical.

Solution

The Itera Research team developed a comprehensive, multi-tiered anti-fraud system that addressed the bank’s needs using cutting-edge technology, advanced algorithms, and stringent security measures.

1. Advanced Threat Detection: Machine learning (ML) and complex algorithms were used to monitor bank’s client behavior, transaction trends, and communication patterns in real-time. This allowed for the detection of anomalies, including suspicious email activity, phishing attacks, abnormal transactions, and access from unfamiliar locations.
2. Dynamic Blacklisting: A continuously updating blacklist was developed to block devices, IPs, and accounts associated with past fraudulent activities. The system automatically flagged and blocked repeat offenders, enhancing protection against recurring threats.
3. Risk-Based Traffic Light System: Transactions were categorized into three risk levels (red, yellow, green) to balance security and customer convenience:
   • Red (High Risk): Triggered additional verification such as multi-factor authentication.
   • Yellow (Medium Risk): Required milder security checks, like a one-time password (OTP).
   • Green (Low Risk): Allowed a smooth transaction flow with no additional steps.
4. Dedicated Recovery Service Desk: A secure recovery desk was created to manage flagged transactions. Contact center agents could review suspicious activity and follow up with customers to either confirm or halt transactions.
5. Biometric Verification Integration: Biometric methods, including fingerprint and facial recognition, were integrated for high-value transactions, ensuring only authorized users could complete sensitive operations.
6. Data Security and Privacy: All data was encrypted both in transit and at rest, ensuring compliance with the bank’s security policies and protecting sensitive customer information.

This system provided the bank with advanced fraud prevention capabilities while minimizing disruptions to legitimate customer activities.

Key Features

• Seamless Integration: The anti-fraud solution was integrated into the bank’s existing infrastructure without disrupting customer service or core banking operations.
• Machine Learning: Advanced machine learning models continuously evolved to identify new patterns of fraudulent behavior, improving accuracy over time.
• Real-Time Data Processing: The system processed transaction data and user behavior in real-time, enabling instant detection and prevention of fraudulent activities.
• AI-Driven Risk Scoring: The AI-based risk scoring engine evaluated transactions instantly, enabling dynamic responses to evolving threats.
• Scalable Architecture: Built on a scalable architecture, the system can handle growing transaction volumes and adapt to future security needs without performance issues.
• API-First Design: The system utilized an API-first approach, enabling easy integration with third-party services, fraud databases, and biometric tools for enhanced fraud prevention.

Tools & Technologies

• Machine Learning (ML) Frameworks
• Real-Time Data Processing Engines
• Dynamic Blacklisting Systems
• Biometric Verification Tools
• AI-Based Risk Scoring Algorithms
• API-First Integration Framework
• Encryption Protocols
• Scalable Cloud Infrastructure

Results

After implementing the anti-fraud system, the bank saw significant improvements across several key performance areas:

• Significant Fraud Reduction: Fraud cases dropped from 200 to just 2-3 per month, achieving a 95% reduction and greatly enhancing security.
• Minimized Financial Losses: The 95% reduction in fraud resulted in substantial savings by preventing unauthorized transactions.
• Enhanced Customer Trust: Improved security boosted customer confidence, leading to increased satisfaction and loyalty.
• Operational Efficiency: Streamlined fraud detection and verification processes allowed staff to focus more on customer service, improving overall workflows and efficiency.