How to predict fake insurance claims

How to predict fake insurance claims

How to predict fake insurance claims

The Indian insurance sector should adopt predictive analytics to handle risks surrounding premature claims, however slim the margin. Early death claims (claims received within 0-2 decades of policy issuance) are a main hazard focus for Indian life insurers. Normally, premature death claim rates vary between 0.2percent — 1.0percent of policies issued, with a high percentage of these claims being fraudulent (by way of instance, dead-man insurance, misrepresentation of health/financial data ).

Discovering early-claim risk before issuing a policy can’t just help insurers reduce operational expenses, it can also make life insurance cheaper, as the saved cost is passed on to customers. Regardless of the focus on decreasing early-claim risk, most Indian carriers still rely on instinctive, rule-based frameworks instead of predictive analytics-driven automated workflows for underwriting. This causes high false positives (rejected cases that wouldn’t have led to a claim), greater physical verification costs and more decision cycles.

Let us have a look fake insurance predictions

Reality Check: whilst superficially, it may appear that a life insurance application form captures just basic client information, once we look deeper, there’s a plethora of information which could be leveraged to identify risk patterns on affordability, sale place, seller, product, pricing etc.. Moreover, alternate data sources such as credit bureaus, social media and socio-economic indicators may be used to further augment the data.

Myth 2: Analytics solutions will demand heavy technology investments

Reality Check: With the arrival of open-source programming tools such as R and Python, technology investments necessary to build proof of concept models have really become insignificant. Additionally, the models developed on these tools can be transformed into rule-based scorecards which can be readily implemented to automate existing front-end underwriting systems.

Myth 3: Claim rates in the Indian life insurance market are rather low, so the precision of models predicting these situations is bound to be reduced

Reality Check: Given the low early claim prices, some methodologists might argue that this is too narrow an occasion rate to construct a predictive model. Some Indian Insurers have developed classification models that could identify cohorts as little as 0.5percent of total issuances leading to 50% of early claims.

Focus on building the”decision procedure” around the predictive model directly: To leverage the predictive power of analytics, a holistic approach is essential to construct a differential underwriting workflow for modelled hazard categories of clients — for example, very substantial risk (0.5percent ) are car rejected, higher risk are known for on earth verification & mandatory medicals (1%), moderate risk (5%) are known for compulsory medicals etc.. Therefore, the company cases and cut-offs developed in the design stage may not hold true in the long run, and hence, have to be calibrated and tracked continuously.

Model tracking and re-calibration: Firms and market conditions are dynamic and thus the business combination, risk patterns and risk levels keep changing with time. Therefore, regular performance monitoring and regular re-calibration is extremely vital to guarantee model accuracy with time.

Business discretion about model parameters: That is very important. Indian life insurance businesses don’t essentially affirm details about client’s income, occupation, address etc.. If any one of these variables are determinants of a risk score, and when this understanding is shared (even internally within an organisation), it may result in applicant data being manipulated.

While predictive models can help insurers include originations risk, there’s a strong case for the business as well to discuss risk data. Similar to credit bureaus for banking, including a central insurance data custodian to keep and discuss industry-wide data repositories, such as risk data on geo-locations, sourcing agents, claims and higher risk clients, can enable better underwriting decisions.

Related posts

Leave a Comment