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AI-Driven Fraud Detection in Banking

Auditing and Governance Risk and Compliance 0 live data Cursustaal: EN

Cursustaal: EN Cursusbeschrijving, lesmateriaal en uitvoering blijven in het Engels.

Geen geplande sessies gevonden Contact us Tot 50% groepskorting beschikbaar.

Datum Dates on request

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Taal van de cursus en tweetalige begeleiding

Het officiële cursusmateriaal en het gedetailleerde programma zijn in het Engels. Uitleg, discussies, vragen en praktische oefeningen kunnen, afhankelijk van de beschikbaarheid van de trainer, tweetalig in het Engels en Nederlands worden verzorgd. Hierdoor behouden deelnemers toegang tot de originele Engelstalige vakterminologie, met aanvullende begeleiding in het Nederlands.

Seminarinhoud

Wat je leert

Why Attend

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.

Course Methodology

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.

Course Objectives

By the end of this course, participants will be able to:

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
Assess real-time fraud detection architectures and operational workflows
Understand model explainability, validation, and responsible AI requirements
Integrate AI-generated fraud alerts with investigation and case management processes
Develop an AI-driven fraud detection implementation and governance roadmap

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
Financial technology and banking innovation professionals
Cybersecurity and financial crime investigation specialists
Banking executives responsible for fraud risk and AI transformation

Target Competencies

Participants will develop competencies in:

Banking fraud risk identification
AI and machine learning applications in financial crime detection
Fraud data preparation and feature engineering
Transaction anomaly and behavioural analysis
Fraud classification and predictive modelling
Graph analytics and fraud network detection
Fraud model performance evaluation
Real-time fraud monitoring and alert management
Explainable AI and model risk governance
AI-driven fraud detection strategy and implementation

Seminardetails

Gedetailleerde opbouw

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
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
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
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
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
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

Data en locaties

Beschikbare seminar data

50% KORTING VOOR GROEPEN

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Cursuscertificaat

Certificaat bij afronding

Iedere deelnemer die dit seminar afrondt, ontvangt een professioneel cursuscertificaat van INFORMATECH.

Deelnemers ontvangen een INFORMATECH-certificaat voor het afronden van AI-Driven Fraud Detection in Banking.
Het certificaat bevestigt aanwezigheid en succesvolle deelname aan het seminar.
Het ondersteunt registraties voor professionele ontwikkeling binnen auditing and governance risk and compliance en aanverwante functies.

Certificaat controleren

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Veelgestelde vragen

Wanneer is mijn plaats bevestigd?

Je plaats is bevestigd zodra de volledige betaling is ontvangen.

Bieden jullie groepskortingen aan?

Ja, wij bieden de volgende kortingen voor groepsboekingen:

2 deelnemers: 20% korting
3 deelnemers: 30% korting
4 deelnemers: 35% korting
5 of meer deelnemers: 50% korting

Kunnen kortingen worden gecombineerd met andere aanbiedingen?

Nee, kortingen kunnen niet worden gecombineerd tenzij dit uitdrukkelijk is vermeld.

Welke betaalmethoden accepteren jullie?

Wij accepteren bankoverschrijvingen, credit-/debitcards en geselecteerde online betaalmethoden.

Wanneer moet ik betalen?

De volledige betaling moet vóór de startdatum van de cursus zijn voldaan om je deelname te bevestigen.

Is btw inbegrepen in de cursusprijs?

De btw-behandeling hangt af van je locatie en status:

EU-bedrijven (met geldig btw-nummer): btw kan worden verlegd (0%)
EU-particulieren (zonder btw-nummer): btw is van toepassing volgens lokale regels
Deelnemers buiten de EU: btw is doorgaans niet van toepassing (0%)

Kan ik een btw-factuur ontvangen?

Ja, alle deelnemers ontvangen een officiële factuur. EU-bedrijven moeten een geldig btw-nummer opgeven.

Kan ik mijn inschrijving annuleren?

Ja, annuleringen moeten schriftelijk worden ingediend.

Wat is jullie terugbetalingsbeleid?

Meer dan vijfenveertig (45) kalenderdagen vóór de startdatum van de cursus: de deelnemer heeft recht op volledige terugbetaling van de betaalde trainingskosten.
Tussen eenendertig (31) en vijfenveertig (45) kalenderdagen vóór de startdatum van de cursus: de deelnemer heeft recht op terugbetaling van vijftig procent (50%) van de betaalde trainingskosten.
Dertig (30) kalenderdagen of minder vóór de startdatum van de cursus: de trainingskosten worden niet terugbetaald.

Kan ik mijn plaats aan iemand anders overdragen?

Ja, vervanging is zonder extra kosten mogelijk als dit vóór de startdatum van de cursus wordt aangevraagd.

Wat gebeurt er als de cursus wordt uitgesteld of geannuleerd?

Wij behouden ons het recht voor om een cursus te verplaatsen of te annuleren door onvoorziene omstandigheden. In dat geval kun je:

overstappen naar een andere datum
een volledige terugbetaling ontvangen

Ontvang ik een certificaat?

Ja, alle deelnemers ontvangen na deelname een certificaat van afronding.

Is aanwezigheid verplicht?

Ja, volledige aanwezigheid is vereist om certificering te ontvangen.

Zijn jullie cursussen online of fysiek?

Wij bieden zowel klassikale als virtuele live online trainingen aan.

Worden cursusmaterialen verstrekt?

Ja, alle deelnemers ontvangen trainingsmaterialen in digitale vorm.

Zijn reis en accommodatie inbegrepen?

Nee, deelnemers zijn zelf verantwoordelijk voor reis en accommodatie tenzij anders vermeld.

Kunnen jullie maatwerk of incompany training verzorgen?

Ja, wij bieden trainingsprogramma’s op maat op basis van de behoeften van jouw organisatie.

Hoe kan ik contact opnemen voor ondersteuning?

Je kunt ons bereiken via e-mail op info@informatech.co.uk of via ons contactformulier. Ons team reageert snel.

In welke taal wordt de cursus gegeven?

Het officiële cursusmateriaal is in het Engels. Afhankelijk van de beschikbaarheid van de trainer kunnen uitleg, discussies en praktische oefeningen in het Engels en Nederlands worden verzorgd.

Is het cursusmateriaal in het Nederlands beschikbaar?

Het officiële cursusmateriaal en het gedetailleerde programma blijven in het Engels, zodat deelnemers met de internationaal gebruikte vakterminologie kunnen werken.

AI-Driven Fraud Detection in Banking

Locatie Venue on request

Datum Dates on request

Belangrijkste onderwerpen

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 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 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 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 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 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

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