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Data Analytics – New opportunity for fraud detection in Insurance – Infosys

Article Synopsis :

Digitisation has rendered high performance data analytics viable using data feeds from digital channels and social media. Insurers are now in a position to detect fraudulent activity much sooner and prevent it before it has a material impact on their business. These data sources were previously ignored by traditional insurers due to their sheer size and frequency of change.

Digital Thinking can Tackle Insurance Fraud

A thought provoking report by Infosys helps you gain valuable insight into the changing face of fraud detection in today’s world of digitisation. The report is supplemented with case studies of GE Consumer & Industrial Home Services Division, Infinity Insurance Co and AXA OYAK showcasing how insurers across the world are harnessing the power of big data analytics to fight fraud. The report compares traditional fraud detection techniques with modern day analytics and highlights some of the key benefits of using analytics in fraud detection.

The report discusses the following fraud detection methods which can be a good starting point for a digitisation initiative in the field of fraud detection:

1. Social Network Analysis
2. Predictive analytics for big data
3. Social customer relationship management (CRM)

The report also contains a 10 steps plan for insurers interested in using data analytics to improve fraud detection.

Link to Full Article:: click here

Digital Insurer's Comments

Fraud is hard to detect as they are not random in nature. Human intelligence involved leads to the failure of traditional fraud detection techniques.

Usage of data analytics will change the face of fraud detection and prevention in the future. Insures will have to adapt combative techniques such as predictive analytics and social intelligence at various customer touch points to identify fraudulent behaviour. Important challenges they will face in the implementation process are:

1. Building an effective analytics team
2. Defining business rules for predictive analytics
3. Intercommunication of traditional data and big data

Link to Source:: click here



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