Fraud Analytics for Insurance Training Course

Course Overview

Learning Objectives

• By the end of this course, participants will be able to:
• Analyze the role of data analytics in insurance fraud detection and prevention.
• Identify relevant insurance data sources and fraud-related data attributes.
• Detect unusual patterns, anomalies, and behavioral indicators associated with potential fraud.
• Apply statistical and analytical techniques to insurance fraud scenarios.
• Develop fraud indicators, detection rules, and risk-based scoring approaches.
• Analyze claims, policy, payment, customer, and intermediary data for suspicious relationships.
• Identify outliers and recurring patterns that may indicate fraudulent activity.
• Evaluate fraud detection results and distinguish meaningful signals from false positives.
• Prioritize suspicious cases according to fraud risk and potential financial impact.
• Design analytical dashboards and fraud monitoring indicators.
• Assess the effectiveness and limitations of fraud detection models.
• Strengthen data quality, governance, documentation, and analytical controls.
• Integrate fraud analytics with investigation, claims, underwriting, compliance, and risk functions.
• Develop actionable recommendations for fraud prevention and loss reduction.
• Establish a sustainable analytics-driven insurance fraud management framework.

Who Should Attend

Learning Outcomes

• Upon completion of the course, participants will be able to:
• Explain the principles and applications of fraud analytics in insurance.
• Identify and evaluate data sources relevant to insurance fraud detection.
• Prepare and assess insurance data for fraud analysis.
• Detect anomalies, outliers, unusual behaviors, and suspicious patterns.
• Develop meaningful fraud indicators and analytical rules.
• Apply risk-based fraud scoring and case prioritization.
• Analyze claims and policy relationships to identify suspicious behavior.
• Identify patterns involving customers, intermediaries, providers, transactions, and claims.
• Evaluate false positives and improve the quality of fraud alerts.
• Develop fraud monitoring dashboards and analytical reports.
• Assess the performance of fraud detection models and analytical approaches.
• Connect analytical findings with fraud investigation processes.
• Identify data quality and governance weaknesses affecting fraud analytics.
• Recommend preventive controls based on analytical findings.
• Communicate fraud intelligence effectively to management and investigation teams.
• Build an integrated analytics-driven framework for insurance fraud detection and prevention.

Course Outline