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Home Machine Learning

Using Analytics to prevent bad market conduct and gaming in Insurance

April 20, 2020
in Machine Learning
Using Analytics to prevent bad market conduct and gaming in Insurance
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An analytically driven approach to detect agent gaming and mis-selling

An analytically driven approach using machine learning techniques to detect misconduct and protect your enterprise.

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An analytically driven approach using machine learning techniques to detect misconduct and protect your enterprise.

The insurance agent remains an important sales channel for many insurers, and the predominant one in many markets. But there is increased attention of insurance regulators, and the media, in poor sales practice of some of these agents.

An analytically driven approach

Pressure remains on insurers to detect mis-selling and agent gaming as quickly as possible; to stamp out this risk. In this webinar we’ll highlight an analytically driven approach to detect agent gaming and mis-selling, to complement existing warning processes and procedures that the insurer already has in place.

Machine learning techniques for detection of misconduct

This webinar highlights an analytically driven approach to detect agent gaming and mis-selling, to complement your existing warning processes and procedures. We will also show a demonstration of the interface that your teams would access to allow them to understand why an agent is a suspect, along with a case study showing both improvements in detection of ‘bad’ agents and increased efficiencies in the investigation process.

Regulators globally are tightening up on the approach that insurers need to take to ensure than neither mis-selling nor agent gaming is taking place within the new business process. Insurers are responding in different ways, an innovative few are now using machine learning techniques as one of the ways of early detection of this misconduct. The content sharing, including case studies during the webinar will demonstrate how advanced analytics can be easily deployed within Life and P&C insurance companies.

Key Takeaways:

  • That machine learning techniques can allow insurers to much quicker identify agents who may be starting to go rogue
  • That machine learning can significantly improve the efficiencies of the internal triage and investigation process
  • That insurers can move from being reactive to proactive
  • Reduce monetary loss
  • keep customer and brand reputation high
  • Improve organizational efficiencies on fraud investigation

 

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Credit: Google News

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