Fraud Analytics and Intelligent Detection in Financial Institutions Training Seminar/ Workshop

Course overview

Fraud Analytics and Intelligent Detection in Financial Institutions is a practical professional training course designed to equip participants with the skills to identify suspicious financial activities, analyze transactional data, investigate fraud indicators, and develop data-driven fraud monitoring and reporting solutions. The course combines financial fraud risk management, data analytics, statistical analysis, business intelligence, and introductory machine learning. Participants will work with simulated financial datasets to identify unusual transactions, detect duplicate payments, analyze suspicious transaction patterns, develop fraud risk indicators, and create interactive dashboards for fraud monitoring.

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Course objectives

• Understand common fraud risks affecting banks and other financial institutions.
• Apply data privacy, information security, and human oversight principles when conducting fraud analytics.
• Prepare analytical reports that support investigations, internal audit, compliance, and management decisions.
• Understand machine learning applications in fraud detection. Evaluate detection effectiveness using appropriate performance metrics.
• Use Power BI to create fraud monitoring dashboards. Apply introductory Python techniques for fraud analytics.
• Apply SQL queries to identify anomalies, duplicate transactions, and unusual payment patterns. Develop fraud indicators and transaction-monitoring rules.
• Identify suspicious transactions using rule-based and statistical techniques. Clean, validate, and analyze financial transaction datasets.

Who should attend

Course content

• Module 1: Foundations of Financial Fraud and Fraud Analytics - Overview of financial fraud and its institutional impact. - Common types of banking and financial fraud. - Internal fraud and external fraud. - Digital payment and electronic transaction risks
• Module 2: Financial Data Sources and Data Preparation - Understanding financial transaction datasets. - Transaction records, customer records, payment records, and account information. - Data collection and integration. - Data quality assessment and valid
• Module 3: Microsoft Excel for Fraud Detection - Importing and preparing transaction data. - Sorting, filtering, and conditional formatting. - Identifying duplicate payments and transaction references. - Using IF, COUNTIF, SUMIF, XLOOKUP, and related funct
• Module 4: SQL for Financial Fraud Analytics - Querying financial databases. - Filtering transactions using conditions and thresholds. - Identifying duplicate transactions. - Detecting repeated payments to the same beneficiary. - Using GROUP BY and aggrega
• Module 5: Statistical Analysis and Fraud Indicators - Descriptive statistics for financial transaction data. - Transaction frequency and value distributions. - Mean, median, standard deviation, and percentiles. - Outlier identification and interpretation.
• Module 6: Transaction Monitoring and Rule-Based Detection - Principles of transaction monitoring. - Developing fraud typologies and detection rules. - Identifying unusual transaction frequency and velocity. - Detecting repeated payments and duplicate reco
• Module 7: Power BI for Fraud Monitoring and Reporting - Importing financial datasets into Power BI. - Data transformation using Power Query. - Building data models and relationships. - Developing measures using DAX. - Calculating fraud indicators and exce
• Module 8: Python for Fraud Analytics - Introduction to Python for financial data analysis. - Working with Jupyter Notebook. - Loading CSV and Excel datasets using pandas. - Cleaning and transforming financial records. - Detecting duplicates and missing va
• Module 9: Machine Learning and Anomaly Detection - Introduction to machine learning in fraud detection. - Supervised versus unsupervised learning. - Labeled and unlabeled transaction data. - Preparing features for fraud models. - Introduction to logistic
• Module 10: Fraud Investigation and Forensic Analytics - Translating fraud indicators into investigation questions. - Reviewing transaction histories and account relationships. - Linking transactions across related accounts. - Examining payment sequences a
• Module 11: Anti-Money Laundering Analytics and Compliance Monitoring - Introduction to AML and countering the financing of terrorism. - Customer and transaction risk profiling. - Risk-based transaction monitoring. - Identifying unusual transaction pattern
• Module 12: Fraud Analytics Governance and Final Project - Developing a fraud analytics strategy. - Defining fraud analytics responsibilities. - Documenting detection rules and model assumptions. - Establishing data access and retention controls. - Monitor

General notes

• Expected Deliverables Participants will develop a fraud analytics workbook, a library of SQL detection queries, a fraud indicators register, a Power BI monitoring dashboard, a basic fraud risk scoring model, and a final analytical report based on simulated financial data.
• Tools and Technologies — Comma-Separated Microsoft Excel, Microsoft Power Query, Microsoft Power BI, SQL Server Management Studio (SSMS), Microsoft SQL Server, PostgreSQL, pgAdmin, Python, Jupyter Notebook, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Isolation Forest, Logistic Regression, Decision Trees, Random Forest, SQL Window Functions, PivotTables, DAX, Power BI Report Builder, CSV Datasets, Excel Workbooks, and simulated financial transaction datasets.
• Importing and Cleaning Financial Transaction Data, Identifying Duplicate Payments in Excel, Detecting Repeated Transaction References, Identifying Split Transactions, Flagging Unusual Transaction Values, Analyzing High-Frequency Transactions, Detecting Dormant Accounts with Unusual Activity, Identifying Multiple Payments to the Same Beneficiary, Comparing Customer Transaction Patterns, Detecting Transactions Outside Normal Operating Hours, Developing SQL Queries for Suspicious Transaction Identification, Joining Customer and Transaction Records, Calculating Transaction Velocity Indicators, Applying Statistical Outlier Detection, Conducting Benford's Law Analysis and Interpreting Its Limitations, Developing Rule-Based Fraud Detection Tests, Creating a Fraud Indicators Register, Building a Transaction Monitoring Dashboard in Power BI, Developing Fraud Risk KPIs Using DAX, Creating an Investigation Exception Report, Cleaning Financial Datasets Using Python and pandas, Identifying Anomalies Using Isolation Forest, Building a Basic Fraud Classification Model, Evaluating Model Precision and Recall, Analyzing False Positives and False Negatives, Developing a Fraud Alert Prioritization Matrix, Reconstructing Transaction Timelines, Preparing a Fraud Investigation Report, Documenting Analytical Evidence, Developing an AML Monitoring Dashboard, and Presenting a Final Fraud Analytics Project.

Frequently asked questions

Schedule & fees