Fraud Analytics and Control Mechanisms in Insurance Claims and Corporate Operations Training Course

This course equips participants with analytics techniques and control mechanisms to detect and prevent fraud in insurance claims and corporate operations. It focuses on fraud indicators, data analysis, anomaly detection, predictive models, investigations, and control design.

Target Groups

• Fraud analysts and investigators
• Insurance claims and underwriting staff
• Internal auditors and compliance officers
• Risk managers
• Finance and procurement managers
• Data analysts supporting fraud teams
• Managers and supervisors seeking to strengthen organizational practice in this field
• Consultants and professionals supporting related organizations

Course Objectives

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

• Identify common fraud schemes in insurance and corporate operations
• Assess fraud risk and prioritize high-risk areas
• Prepare and integrate data for fraud analysis
• Apply rule-based tests and red-flag analytics
• Detect anomalies using statistical methods
• Use link analysis to uncover networks
• Understand predictive and machine learning approaches to fraud
• Design preventive and detective controls
• Manage alerts, cases, and investigations
• Measure the performance of fraud analytics programs

Course Modules

Module 1: Fraud in Insurance and Corporate Operations

• Fraud typologies
• Claims fraud
• Procurement, payroll, and expense fraud
• Fraud impact
• Fraud risk drivers

Module 2: Fraud Risk Assessment

• Risk assessment framework
• Process and data mapping
• Red-flag identification
• Prioritizing analytics
• Governance

Module 3: Data Preparation for Fraud Analytics

• Data sources
• Data cleaning and integration
• Data quality checks
• Feature creation
• Privacy and ethics

Module 4: Rule-Based Tests and Red Flags

• Duplicate and sequence tests
• Threshold and timing tests
• Claims red flags
• Journal entry tests
• Building test libraries

Module 5: Anomaly Detection

• Descriptive and distribution analysis
• Benford’s law
• Outlier detection
• Clustering for anomalies
• Interpreting results

Module 6: Link Analysis and Network Detection

• Entity resolution
• Relationship mapping
• Collusion and ring detection
• Visualization tools
• Case examples

Module 7: Predictive and Machine Learning Approaches

• Supervised and unsupervised methods
• Scoring models
• Model evaluation
• Limitations and bias
• Operationalizing models

Module 8: Control Design and Fraud Prevention

• Preventive control design
• Detective controls
• Segregation of duties
• Continuous monitoring
• Anti-fraud policies

Module 9: Investigations and Program Performance

• Alert triage
• Case management
• Evidence preservation
• Reporting
• Measuring detection and recovery

Module 10: Practical Exercises and Capstone Project

• Preparing a sample claims dataset
• Running red-flag and anomaly tests
• Mapping a fraud network
• Designing controls
• Presenting a fraud analytics program proposal