Global Learning for Operational Leaders
Classroom
0 sessionsOnline / Live
0 sessionsIntroduction
Course overview
Financial fraud represents a significant and evolving threat to the banking industry. The rapid expansion of digital banking, instant payments, mobile financial services, and interconnected financial platforms has created new opportunities for fraudsters to exploit vulnerabilities in banking systems, customer interactions, and transaction processes.
Traditional fraud detection systems often rely on predefined rules, manual investigations, and historical fraud patterns. Although these approaches remain important, they can struggle to identify emerging fraud techniques, sophisticated transaction networks, and rapidly changing criminal behaviours. High volumes of false alerts can also overwhelm investigation teams, increase operational costs, and delay legitimate customer transactions.
Artificial intelligence (AI), machine learning, and advanced analytics are transforming banking fraud detection by enabling financial institutions to identify unusual transaction patterns, analyse customer behaviour, detect suspicious relationships, and prioritise potentially fraudulent activities more effectively.
However, successful AI-driven fraud detection requires more than implementing sophisticated algorithms. Banks must establish reliable data pipelines, select appropriate analytical techniques, evaluate model performance, manage false positives, protect customer information, and ensure that automated detection systems operate within effective governance and regulatory frameworks.
The AI-Driven Fraud Detection in Banking course provides banking professionals with a practical understanding of how AI and machine learning can strengthen fraud prevention, detection, investigation, and risk management.
Participants explore transaction monitoring, anomaly detection, supervised and unsupervised learning, behavioural analytics, network analysis, model validation, explainable AI, real-time fraud detection, and responsible AI governance.
Through realistic banking fraud scenarios, analytical exercises, and implementation workshops, participants develop the capabilities needed to evaluate AI-driven fraud detection solutions and integrate them into banking operations.
Professional Problems This Course Is Designed to Solve
Increasing sophistication of banking fraud: Applying AI-based analytical techniques to identify evolving fraud patterns across payments, accounts, digital channels, and customer interactions.
Excessive false-positive fraud alerts: Improving detection precision, alert prioritisation, and investigation efficiency through model evaluation and risk-based decision thresholds.
Limited visibility of complex fraud networks: Using graph analytics and relationship analysis to identify coordinated fraud, suspicious account connections, and potentially fraudulent transaction networks.
Delayed identification of suspicious activity: Understanding real-time transaction scoring and automated detection workflows that support timely fraud prevention.
Fragmented fraud-related information: Integrating transaction records, customer profiles, device signals, behavioural information, and investigation outcomes into effective analytical systems.
Weak AI model governance: Establishing controls for model explainability, validation, data protection, performance monitoring, and human oversight.
Difficulty demonstrating the value of fraud analytics investments: Measuring fraud losses prevented, detection effectiveness, operational efficiency, customer impact, and return on investment.
What you will achieve
Learning objectives
- Explain the major types of banking fraud and their evolving risk characteristics
- Understand the applications of artificial intelligence and machine learning in fraud detection
- Identify and prepare relevant banking transaction and behavioural data
- Apply supervised and unsupervised learning concepts to fraud detection scenarios
- Understand anomaly detection and behavioural analytics techniques
- Evaluate fraud detection models using appropriate performance metrics
- Develop risk-based fraud scoring and alert prioritisation approaches
- Explain the role of graph analytics in identifying coordinated fraud networks
Who should attend
Target audience
- This course is suitable for:
- Banking fraud prevention and detection managers
- Financial crime compliance professionals
- Banking risk management specialists
- Anti-money laundering and transaction monitoring analysts
- Internal auditors and banking compliance officers
- Banking data analysts and data scientists
- Digital banking and payment operations managers
Methodology
Learning approach
- The programme combines instructor-led discussions, banking fraud case studies, AI and machine learning demonstrations, analytical workshops, fraud detection simulations, and model governance exercises.
- Participants work with illustrative banking datasets containing transaction histories, customer behaviour indicators, payment information, account relationships, fraud labels, and investigation outcomes.
- Practical activities demonstrate fraud data preparation, feature engineering, anomaly detection, classification techniques, alert scoring, model performance evaluation, and investigation prioritisation.
- The programme is designed for both technical and non-technical banking professionals. Technical concepts are explained through practical applications, while advanced analytical exercises illustrate how fraud detection models can be developed, evaluated, and deployed within banking environments.
Complete course outline
Five focused days of learning and application
The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.
Day 1
Banking Fraud Landscape and Foundations of AI-Driven Detection
- Understanding the banking fraud landscape, including payment fraud, account takeover, identity fraud, card fraud, authorised push payment scams, and fraudulent account activity
- Examining the limitations of traditional rule-based fraud detection systems and the opportunities presented by AI, machine learning, and advanced analytics
- Exploring the fraud detection lifecycle, including prevention, monitoring, alert generation, investigation, case resolution, and feedback mechanisms
- Understanding the fundamentals of supervised learning, unsupervised learning, anomaly detection, and predictive analytics in banking fraud management
- Coffee break
- Understanding the fundamentals of supervised learning, unsupervised learning, anomaly detection, and predictive analytics in banking fraud management (continued)
- Identifying relevant fraud data sources, including transaction records, customer profiles, device information, digital channel activity, and historical investigation outcomes
- Practical workshop: Analysing banking fraud scenarios and identifying opportunities for AI-driven detection across customer journeys and payment processes
Day 2
Fraud Data Analytics, Machine Learning and Anomaly Detection
- Preparing banking fraud datasets through data cleaning, validation, integration, transformation, and the handling of missing or inconsistent information
- Developing fraud detection features using transaction frequency, payment amounts, account activity, behavioural deviations, geographic signals, and customer history
- Understanding supervised classification algorithms, including logistic regression, decision trees, random forests, and gradient boosting techniques
- Coffee break
- Understanding supervised classification algorithms, including logistic regression, decision trees, random forests, and gradient boosting techniques (continued)
- Exploring unsupervised learning methods, including clustering, isolation forests, and anomaly scoring for detecting previously unidentified suspicious patterns
- Addressing class imbalance, limited fraud labels, data leakage, and the challenges of training models on rare fraudulent events
- Analytical workshop: Designing and evaluating a machine learning fraud detection model using illustrative banking transaction data and risk indicators
Day 3
Advanced Fraud Analytics, Behavioural Intelligence and Network Detection
- Applying behavioural analytics to identify deviations from normal customer transaction patterns, account usage, login activity, and payment behaviour
- Understanding graph analytics and network-based detection techniques for identifying suspicious relationships between accounts, devices, beneficiaries, and transactions
- Exploring the use of sequential and temporal analysis to detect unusual transaction sequences, rapid account activity, and coordinated fraud attempts
- Examining AI applications in identity verification, digital onboarding, document fraud detection, and account takeover prevention
- Coffee break
- Examining AI applications in identity verification, digital onboarding, document fraud detection, and account takeover prevention (continued)
- Evaluating fraud risk scoring techniques that combine machine learning outputs, business rules, behavioural signals, and contextual risk information
- Case study: Investigating a simulated coordinated banking fraud network using behavioural indicators, transaction relationships, and risk-based alert prioritisation
Day 4
Real-Time Fraud Detection, Model Performance and Investigation Integration
- Understanding real-time fraud detection architectures involving transaction data streams, feature generation, model scoring, decision engines, and automated alerts
- Evaluating fraud model performance using precision, recall, F1-score, precision-recall curves, false-positive rates, and financial loss-based measures
- Establishing risk thresholds and decision strategies that balance fraud prevention, customer experience, investigation capacity, and operational costs
- Integrating AI-generated alerts with fraud investigation workflows, case management systems, analyst feedback, and escalation procedures
- Coffee break
- Integrating AI-generated alerts with fraud investigation workflows, case management systems, analyst feedback, and escalation procedures (continued)
- Monitoring model performance for data drift, changing fraud patterns, declining detection effectiveness, and the need for model recalibration or retraining
- Practical exercise: Designing a real-time fraud monitoring workflow with model performance indicators, alert thresholds, investigation priorities, and customer protection controls
Day 5
Responsible AI, Fraud Model Governance and Banking Implementation Strategy
- Establishing governance arrangements for AI-driven fraud detection covering model ownership, validation, documentation, approval, accountability, and independent oversight
- Understanding explainable AI techniques and the importance of interpreting fraud risk scores, model decisions, and contributing risk factors
- Assessing privacy, data protection, fairness, cybersecurity, and third-party technology risks associated with banking AI systems
- Evaluating relevant regulatory and supervisory expectations for financial crime controls, model risk management, operational resilience, and automated decision-making
- Coffee break
- Evaluating relevant regulatory and supervisory expectations for financial crime controls, model risk management, operational resilience, and automated decision-making (continued)
- Developing implementation roadmaps covering business requirements, data readiness, technology selection, pilot testing, integration, model monitoring, and continuous improvement
- Capstone exercise: Developing and presenting a Comprehensive AI-Driven Banking Fraud Detection Framework incorporating fraud risk assessment, data architecture, machine learning techniques, behavioural analytics, fraud scoring, real-time monitoring, investigation workflows, model governance, and implementation priorities
FAQ
Frequently asked questions
What does AI-Driven Fraud Detection in Banking (AGRC-019) cover?
This course covers Auditing and Governance Risk and Compliance through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The course schedule and fee are available on request. Contact Aractech for current availability and a quotation.
Who should attend this course?
This course is suitable for:, Banking fraud prevention and detection managers, Financial crime compliance professionals
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
The schedule and fee, including any available delivery options, are available on request. Contact Aractech for details.
Where are classroom sessions delivered?
Classroom venues are confirmed in the schedule panel for each published session.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 training hours.
Quick request
Send quick request
Send a fast enquiry about this course and the academy team will get back to you.
Get a quotation
Request a formal quotation
Choose an available date and venue, add your participants, and we will email a formal quotation for AI-Driven Fraud Detection in Banking to your business address.