I am more than glad to invite you to join me in a series of posts related to a practical guide for tackling auto insurance fraud in the new era of data science and advanced analytics.

Insurers are used to face a constant threat, a powerful enemy that never rests. And this is no other than claims fraud. Especially for the auto insurance industry, this open wound has significant finance impact, not only to insurers but also to the public, with increased premiums for which all of us have to pay.

This series consists of 7 articles which aim to provide a structured approach and guidance for tackling insurance fraud with advanced analytics principals, as many insurers have already embraced and succeeded significant tangible results and ROI.

A structured implementation methodology for auto insurance fraud

The practical guide to tackle auto insurance fraud is not just for series of analytics techniques. It is a structured methodology and implementation guide that embraces analytics for developing a claims fraud prevention system. This will act as second line of defense and will boost the efficiency and effectiveness of claim handlers and fraud investigators. Corporate KPIs, like suspicious claims fraud rates and detection rates, will increase and provide significant fraud savings, giving a boost to the insurer’s profitability.

Design, plan and preparation is key for tackling insurance fraud. This is an open invitation to join me in this 7-post blog series as we drill down to fraud detection and prevention analytics techniques. In each article we will analyze a different analytics approach, providing the high lights and the key messages that an insurer has to focus on.

Sneak peek

Here’s a sneak peek at what will follow:

• Part 1: Data Management & Data Quality
• Part 2: Business Rules and Watch lists
• Part 3: Advanced Analytics
• Part 4: Social Network Analysis
• Part 5: Hybrid Scoring
• Part 6: Investigator’s Interface
• Part 7: Operational Processes and Automated Controls

Regardless of your maturity in analytics or fraud prevention tactics and techniques, this practical guide will give you the fundamentals in analytics methodology. And even beyond, it will allow you to enhance your existing processes or design and plan new ones.

Stay with us in our next stop “Part 1: Data Management & Data Quality”, where we'll analyze the pillars of a fraud prevention system, the data importance and what you have to do in matter of data cleansing, data matching and data integration. Also, there is still time to register now to our free webinar “Analytical Fraud - Preparing for a successful Project” that will be held on September 29^th