Fraud Risk Analysis

OVERVIEW

Verita Assurance Ltd. has experienced steady growth since 2003, serving over 500,000 policyholders. However, the company faced a significant challenge with fraud-related claims rising by 14% in the past two years, despite strong digital systems. Key gaps identified were the lack of real-time fraud alerts, fragmented data pipelines, and delayed detection leading to financial losses. This project aimed to address these issues by providing comprehensive insights into fraud patterns and actionable recommendations. 

OBJECTIVES

The primary objectives of this project were to:

• Build Live Dashboards: Create integrated analytics with Google Sheets for real-time monitoring of fraud metrics.

• Identify High-Risk Claims: Embed fraud indicators to flag suspicious patterns immediately.

• Enable Claim Drilldowns: Allow filtering of claims by region, type, agent, and customer for detailed analysis.

• Automate Fraud Scoring: Implement business rules to automatically calculate risk scores for claims.

• Deliver Early Warnings: Enable faster investigations through proactive alerts, minimizing financial loss.

KEY COLUMNS

The analysis for this project would have involved various data points related to claims, policies, customers, and agents. While specific column names were not provided, the report indicates analysis based on:

• Claim Data: Total claims, fraudulent claims, claim amounts, claim types (Auto, Home, Travel, Business).

• Fraud Metrics: Average fraud score, fraud rates.

• Geographical Data: Regions (Scotland, London, Midlands, South East, NW).

• Agent Data: Agent IDs, fraud rates associated with agents.

• Customer Data: Customer tenure (New Customers (0-1 yrs), Established (1-3 yrs), Loyal (5-10 yrs)).

• Time-based Data: Trends over two years, monthly increases.

TOOLS

• Data Source: Internal company data, potentially integrated with Google Sheets.

• Visualization & Dashboarding: Power BI.

• Rule-Based Systems: For automating fraud scoring and flagging high-risk claims.

APPROACH

Phase 1: Data Integration and Baseline Analysis

• Consolidated fragmented data pipelines to create a unified view of claims and policyholder information.

• Calculated baseline fraud metrics, including average fraud scores, total claim volume, and fraudulent claim percentages.

• Analyzed processing efficiency, such as average claim processing time and the number of high-value claims flagged.

Phase 2: Deep Dive Analysis by Dimensions

• Regional Analysis: Examined fraud rates and average fraud scores across different regions (e.g., Scotland, London, Midlands, South East) to identify geographical hotspots.

• Claim Type Analysis: Investigated fraud patterns across various insurance product types (Auto, Home, Travel, Business), identifying which types had the highest fraud rates and contributed most to fraudulent claims.

• Agent Analysis: Assessed agent-specific fraud rates and average fraud scores to identify high-risk agents.

• Customer Behavior Analysis: Explored the relationship between customer tenure (new, established, loyal) and fraud risk, noting that longer relationships do not necessarily imply lower risk.

• Claim Amounts Analysis: Quantified total claims value, highest regional claim amounts, and growth rates in claim amounts.

Phase 3: Insights Generation and Recommendation Development

• Synthesized findings from all analytical dimensions to identify key fraud drivers and patterns.

• Developed actionable recommendations categorized by Regional Focus, Claim Type Strategy, and Customer Approach.

Phase 4: Dashboard Development and Early Warning System 

• Designed and built live dashboards to visualize fraud metrics and insights in real-time.

KEY INSIGHTS

The Fraud Risk Intelligence Report revealed several critical insights for Verita Assurance Ltd.:

• Overall Fraud Metrics: Average fraud score was 18.0, with 8.2% (572 out of 7,000) of total claims being fraudulent, amounting to £76 million.

• Regional Hotspots: Scotland had the highest fraud rate (9.8%), London had the highest fraud volume (28.3%), and Midlands had the highest average fraud score (18.5).

• Claim Type Vulnerabilities: Auto claims showed the highest fraud rate (8.5%) and contributed significantly to fraudulent claims. Business claims had concerning fraud scores despite lower volume.

• Agent Risk: Agent 206 was identified as high-risk with a 30.8% fraud rate, followed by Agent 045 (28.2%) and Agent 116 (21.7%).

• Customer Tenure and Risk: New customers (0-1 yrs) exhibited the highest fraud rate (12.2%), while loyal customers (5-10 yrs) accounted for the most fraud claims (34.09%), indicating that tenure does not always mitigate risk.

• Claim Amount Growth: Total claim amounts increased by 313% from April '23-'25, with London having the highest regional claim amount (£23.5M).

IMPACTS

This project delivered significant impacts for Verita Assurance Ltd.:

• Proactive Fraud Detection: The implementation of fraud indicators and automated scoring enables immediate flagging of suspicious patterns, shifting from reactive to proactive fraud management.

• Reduced Financial Loss: Faster investigations and early warnings minimize financial losses due to fraudulent claims.

• Optimized Resource Allocation: Insights into regional, claim type, agent, and customer-specific risks allow for targeted allocation of investigation resources.

• Enhanced Underwriting and Verification: Recommendations for strengthening verification procedures for new customers and reviewing underwriting in specific regions and for certain claim types improve risk assessment at the point of policy issuance.

• Improved Agent Performance: Identifying high-risk agents allows for targeted training and review of protocols, enhancing overall fraud identification capabilities.

• Data-Driven Strategy: The report provides a comprehensive, data-driven foundation for developing and refining a robust fraud mitigation strategy.

DELIVERABLES

The key deliverables for this project included:

• Fraud Risk Intelligence Report: A detailed report summarizing project overview, objectives, summary insights (fraud metrics, claim volume, processing efficiency), and in-depth analysis across regions, claim types, agents, and customer behavior.

• Live Dashboards: Integrated analytics dashboards for real-time monitoring of fraud metrics.

• Actionable Recommendations: Specific strategies for regional focus, claim type management, and customer approach to mitigate fraud.