AI-Driven Fraud Detection in Banking

As banking transactions become increasingly digital and complex, so do the methods used by fraudsters. Traditional rule-based fraud detection systems are no longer sufficient to combat sophisticated and evolving threats. Artificial Intelligence (AI) offers a smarter, faster, and more scalable approach—using real-time data analysis, pattern recognition, and anomaly detection to identify suspicious activity before it causes damage.

This course introduces participants to modern fraud detection techniques powered by AI, including supervised and unsupervised learning, deep learning, and graph-based approaches. Through hands-on exercises, real-world case studies, and ethical discussions, participants will learn how to build and deploy fraud detection models tailored to banking environments.

Section 1: Fraud in the Digital Banking Era

• Types of financial fraud: identity theft, transaction fraud, phishing, synthetic accounts
• The evolving nature of fraud in online and mobile banking
• Limitations of traditional fraud detection systems
• How AI enhances fraud detection: speed, adaptability, and automation
• Industry case studies: AI catching fraud before it spreads

Section 2: Data for Fraud Detection

• Key data sources: transactions, login behavior, geolocation, device metadata
• Feature engineering for fraud: velocity, frequency, amount deviation
• Exploratory data analysis (EDA) on fraud patterns
• Data labeling, class imbalance, and synthetic data generation
• Lab: Prepare and explore fraud data using Python and Pandas

Section 3: Machine Learning Models for Fraud Detection

• Supervised learning: decision trees, random forests, gradient boosting
• Unsupervised learning: clustering and isolation forests for unknown fraud
• Deep learning for detecting complex fraud patterns in sequences
• Evaluation metrics: precision, recall, F1, cost of false positives
• Workshop: Train and validate a fraud detection model on transactional data

Section 4: Real-Time Detection, Alerts, and Anomaly Monitoring

• Stream processing for real-time detection: Kafka, Spark, and APIs
• Building real-time alerting systems using AI triggers
• Graph analytics for network-based fraud (e.g., money mule networks)
• Addressing adversarial attacks and evolving fraud behavior
• Integration with banking platforms and automated action systems

Section 5: Governance, Ethics, and Operationalization

• Regulatory considerations (e.g., PSD2, AML laws, GDPR compliance)
• Managing model drift and ensuring explainability in high-stakes contexts
• Balancing detection accuracy with customer experience
• Transparent reporting and audit trails in AI-driven systems
• Final project: Present a fraud detection strategy and model architecture

Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.

Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.

CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.