Insurance underwriting may have to rely on proxy measures

Insurers may have to rely on what are called proxies that are very personal and individually identifiable but at the same time strongly correlated to the risk which they are underwriting, says Mr Yashish Dahiya, CEO and co-founder of insurance aggregator Policybazaar.com.

In an article in Moneycontrol, he notes that while telematics devices are closest to what needs to be measured (the risk of one's driving and the propensity of claim outcome), they are not practical in a market like India where insurance rates are too low and device costs comparatively are very high.

Digital behaviour

Mr Dahiya says that two new parameters that have caught the insurer's attention in the last couple of years are voice analytics and digital behaviour.

Just like offline behaviour, a person's online behaviour is very closely correlated to a person's beliefs and habits. If one were to observe and understand this person and behaviour, one may be able to decipher the chances that such a person puts himself/herself in difficult situations leading to claims/accidents. The important aspect here would be to link the personas built to measurable differences in claim outcomes so they can be priced effectively. 

Voice analytics

Similarly, voice analytics is gaining good momentum with insurance companies. What one says and with what tone present a good window of behaviour, habit, and nature of the person. By suitably understanding specific patterns of word usage, tone and tenor, voice personas of people can again be drawn. Further correlating such personas to claims outcomes, one can substitute voice personas as the basis for assessing the risk and consequently dynamically price the customer.

Both voice (language and tone) and digital behaviours are found to be very personal in nature and it is this 'nature' that is being explored by the insurers for individual risk assessment.

Mr Dahiya warned, “One must, however, be careful that while building such algorithms, the principle of GIGO (Garbage In Garbage Out) can act against the model if one has not taken enough steps to understand the underlying data and its correlations.”

Source: Asia Insurance Review