Containing claims losses because of fraud and abuse can be nightmarish and costly in terms of time and resources for insurers. Predictive analytics is about predicting unknown future events using data mining, statistics, modelling, machine learning, and artificial intelligence.
Predictive analytics can be of help to insurers, but its use has lagged applications in pricing and underwriting. There is general awareness about the importance of predictive analytics to improving the claims ratio (or loss ratio) but its actual application is low.
Predictive analytics has the capability to significantly minimise fraud costs by detecting more frauds, curtail false positives, and reduce fraud investigation expenses. In addition, predictive analytics lowers claims handling costs, quickly identifies outlier claims, and efficiently manages claims severity.
It is unavoidable for insurers to employ predictive analytics for pricing, risk selection, underwriting cost reductive, capturing lifetime consumer value, and in sales and distribution. It is equally pressing for insurers to let predictive analytics drive claims management to tackle problems of claims fraud, and claims severity and drastically cut claims management expenses.
It is imperative for insurers to become organisations that are driven by data and analytics to increase the efficiency, productivity, cost effectiveness, and service levels of their claims operations. Predictive analytics can positively impact the key areas of concern for insurers in improving claims handling.
Insurers need to possess an advanced, sophisticated method combining predictive analytics and data-driven claims workflow solutions.
Both quantity and quality of data are important to creating highly predictive models. Since missing, poor quality or inconsistent data can hinder accuracy in predictive modelling, it is important to work with an experienced analytics partner who understands the data deeply, its potential business applications, and how to clean and manipulate the data accordingly.
Predictive models gain from lots of data, as they can assess complex data patterns. The more disparate data that can be aggregated, the more accurate the decisioning would potentially be.
The more data made available to the decision-making process the better, but humans can find themselves overwhelmed. Analytics solutions have capabilities to automatically analyse any amount of data into results which are to be acted upon by claims managers.
The practical exploitation of predictive analytics is in the inclusion of more data. Apart from the data available with an insurer, predictive models integrate public records and other external data sources, some of which are still emerging in India. But the ecosystem today provides an opportunity to pull sources of data that were earlier not available for predictive modelling.
Predictive analytics modelling is a job half done with lots of data. The predictive model should be able to provide results that are actionable for managing resources in claims management.
Better application of advanced predictive analytics, increased access to robust data sources, and strong integration with workflow can transform the claims handling process. Bringing about significant reductions in claims loss is possible through qualitative influence on claims managers from predictive modelling.
The benefits of predictive analytics are three-pronged:
- Claims decisions will be data-driven
- Expenses will be lowered
- Improved profitability and business growth.
Claims handling is a dynamic process as every claim evolves and changes, and hence analytics cannot afford to be a static application. Analytics must be a continuous, dynamic process, with each claim re-scored every time data changes. Predictive analytics is meant to ensure that each individual claim is optimally processed. The low-risk claims are approved speedily to achieve reduced claim management expenses and increase consumer satisfaction, whereas the complicated claims are left to be handled by skilled claims managers.
The immediate application of modern, predictive modelling is necessary in insurance businesses where the claims ratio is high.
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