Banking Data Science and Fraud Analytics Training Course

Course overview

Banks hold rich transaction, customer, channel and behavioural data, yet fraud teams often face delayed alerts, high false-positive rates and model outputs that are difficult to explain to investigators, auditors and regulators. This course addresses the practical challenge of turning banking data into controlled fraud decisions: identifying suspicious payment activity, prioritising cases by risk, measuring model performance against financial loss, and maintaining traceable analytical evidence. Participants learn to work across card, digital-banking, account-transfer and customer-profile data while accounting for class imbalance, concept drift and operational investigation constraints.

The course covers the fraud analytics lifecycle from data extraction and feature engineering through supervised machine learning, anomaly detection, alert calibration and model monitoring. Participants use SQL and Python to prepare transaction-level data, construct behavioural and velocity features, train and compare fraud models, and evaluate results using precision, recall, PR-AUC, lift, confusion matrices and expected-value measures. They also learn explainability techniques using SHAP values, threshold-setting methods, investigator feedback loops, and documentation practices aligned to model risk management expectations.

Teaching combines instructor-led technical sessions with guided labs based on a realistic banking fraud dataset. Each participant builds a reproducible fraud-risk scoring workflow, including a data dictionary, feature set, model comparison, alert threshold recommendation, performance dashboard specification and model-governance summary. The final day concludes with a fraud analytics implementation plan that can be adapted for the participant's own bank, business line or fraud use case.

The course is designed for banking professionals who already work with data, fraud operations, risk or digital channels and need stronger analytical capability without losing sight of controls, customer impact and investigation workflows.

Course objectives

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

• Prepare banking transaction data using SQL joins, data-quality checks and reproducible Python pipelines
• Engineer velocity, behavioural, merchant, device and customer-risk features for fraud detection
• Train and compare logistic regression, random forest and gradient-boosting fraud models
• Evaluate imbalanced fraud models using precision, recall, PR-AUC, lift and expected financial value
• Calibrate fraud-alert thresholds against investigation capacity, customer friction and fraud-loss tolerance
• Apply anomaly-detection methods to identify emerging patterns where labelled fraud data is limited
• Explain individual fraud scores using SHAP values and produce investigator-ready reason codes
• Create a model monitoring and governance pack covering drift, performance decay, validation and escalation

Benefits of attending

Target competencies

Who should attend

• Fraud Analysts — who need to improve alert quality and investigate transaction patterns with evidence
• Banking Data Analysts — who prepare customer and transaction data for fraud, risk and digital-channel teams
• Data Scientists — who need to build fraud models within banking controls and operational constraints
• Financial Crime Analytics Managers — who must prioritise detection investment and defend model performance
• Digital Banking Product Managers — who balance fraud controls, customer friction and payment-conversion outcomes
• Model Risk and Validation Professionals — who review fraud models for explainability, monitoring and governance

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand basic descriptive statistics, including averages, distributions, correlation and sampling. Prior experience writing simple SQL SELECT, WHERE, GROUP BY and JOIN queries is expected, as is basic Python familiarity with variables, data frames and running notebook cells. Experience with pandas or scikit-learn is useful but not essential; key workflows are demonstrated and practised in class. Participants do not need prior fraud-investigation experience, advanced mathematics, deep learning knowledge or production machine-learning engineering experience. A working laptop with permission to install or access the course environment is required.

Training methodology

The week alternates focused instructor-led briefings with hands-on analysis in Python notebooks and SQL exercises. Participants work through a simulated banking fraud case containing transactions, customers, merchants, devices and investigator outcomes. Labs progress from profiling and feature creation to model training, alert calibration and explainability. Small-group reviews test decisions against fraud-loss, customer-experience and operational-capacity constraints. The final workshop converts each participant's analysis into a model-monitoring checklist and a 90-day implementation plan for a live banking use case.

Course outline

Day 1: Banking fraud data foundations

• Banking fraud typologies across cards, transfers, digital channels and account opening
• Transaction-data schemas and relationships between customer, account, merchant and device records
• SQL joins for assembling transaction-level analytical datasets
• Data-quality profiling for missing values, duplicate transactions and inconsistent timestamps
• Fraud labels, chargeback timing and delayed outcome challenges
• Exploratory analysis of transaction amount, geography, channel and time-of-day patterns
• Privacy, access control and data-minimisation considerations for fraud analytics

Workshop: Participants profile a banking transaction dataset and produce a data-quality findings log with priority remediation actions.

Day 2: Feature engineering and fraud signals

• Velocity features for transaction count, value and beneficiary activity
• Behavioural baselines using customer spending, channel and geographic history
• Merchant, device and IP-address risk aggregation methods
• Rolling windows and leakage-safe feature construction
• Categorical encoding and treatment of high-cardinality banking fields
• Missing-data strategies for customer and transaction attributes
• Feature selection using business relevance, stability and predictive contribution

Workshop: Participants build a documented feature set for payment fraud detection, including velocity and behavioural-risk variables.

Day 3: Fraud model development and evaluation

• Time-based train, validation and test splits for transaction data
• Logistic regression as an interpretable fraud-score baseline
• Random forest and gradient-boosting model comparison
• Class weighting, undersampling and oversampling for rare fraud events
• Confusion matrices and the operational meaning of false positives and false negatives
• Precision-recall curves, PR-AUC, lift charts and top-decile capture
• Expected-value modelling using fraud loss, investigation cost and customer-contact cost

Workshop: Participants train and compare three fraud models and present a performance scorecard recommending one candidate model.

Day 4: Alert decisioning and explainable analytics

• Risk-score calibration and probability reliability checks
• Threshold selection based on investigator capacity and fraud-loss appetite
• Queue prioritisation and segmentation by product, channel and customer risk
• Unsupervised anomaly detection with isolation forest techniques
• SHAP values for global model interpretation and individual alert explanations
• Reason-code design for fraud investigators and customer-service teams
• Concept drift, fraud-pattern change and feedback-loop design

Workshop: Participants calibrate an alert threshold and create an investigator-facing alert explanation for high-risk transactions.

Day 5: Model governance and operational deployment

• Fraud-model documentation: purpose, data, features, assumptions and limitations
• Model validation tests for stability, bias, robustness and challenger comparison
• Performance monitoring metrics for detection rate, false positives and financial value
• Population stability index and drift-monitoring triggers
• Human-in-the-loop review and investigator feedback capture
• Incident escalation procedures for material model deterioration
• Implementation roadmaps for batch scoring, real-time decisioning and controlled pilots

Workshop: Participants assemble a fraud analytics governance pack and a 90-day implementation plan for their selected banking use case.

Tools & standards covered

Python, SQL, scikit-learn, SHAP

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions