Learning Objectives:

  • Explain the use of AI in fraud detection.

  • Understand behavioural biometrics and anomaly detection.

  • Describe the role of transaction monitoring in fraud prevention.

7.1 AI-Based Fraud Detection

The H.L. College of Commerce syllabus covers “AI-based Fraud Detection” as a security measure in digital banking . The Amrita syllabus covers “Risk management strategies for mitigating cyber risks” including the use of technology .

Anomaly Detection:
AI-powered systems that identify unusual transactions and patterns.

Real-Time Monitoring:
AI-powered systems that monitor transactions in real-time.

Pattern Recognition:
Identifying patterns indicative of fraud.

Behavioural Analytics:
The H.L. College of Commerce syllabus covers “Behavioural biometrics & Anomaly Detection” .

7.2 Behavioural Biometrics and Anomaly Detection

The H.L. College of Commerce syllabus covers “Behavioural biometrics & Anomaly Detection” as a security measure in digital banking .

Behavioural Biometrics:
Analysing user behaviour patterns, such as typing speed, mouse movements, and device handling, for continuous authentication and fraud detection.

Anomaly Detection:
Identifying deviations from normal patterns that may indicate fraudulent activity.

7.3 Transaction Monitoring

The H.L. College of Commerce syllabus includes a case study on “NPCI’s Fraud Risk Management in UPI” .

Real-Time Monitoring:
Continuous monitoring of transactions for fraud.

Rule-Based Monitoring:
Using predefined rules to flag suspicious transactions.

AI-Powered Monitoring:
Using machine learning to detect anomalies and emerging fraud patterns.

Fraud Risk Management:
The H.L. College of Commerce syllabus includes “NPCI’s Fraud Risk Management in UPI” .

7.4 Detection and Prevention Strategies

Detection:

  • AI-powered real-time monitoring.

  • Anomaly detection.

  • Behavioural biometrics.

  • Transaction monitoring.

Prevention:

  • Multi-factor authentication.

  • Biometric authentication.

  • Security awareness training.

  • Fraud risk management frameworks.

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