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AI And Analytics Changing The Face Of Insurance

Saurabh Awasthi, Head of Analytics, Cigna TTK

Cigna TTK is a stand-alone health insurance company offering differentiated health insurance solutions including health and wellness programs that support customers in making lifestyle changes and managing chronic medical conditions.

Insurance has been the earliest adopter of data but the latest one of data Analytics. Historically, Insurers have collected wealth of data, but have been really slow in using it to its full potential. The optimum usage of that data-wealth along with the external information available about the individuals when assisted with advanced analytical techniques will create wonders for Insurance carriers. With more number of consumers moving to online medium for purchases and interactions, the volume of individual data is increasing exponentially. Artificial intelligence (AI) along with the wave of deep learning techniques has the potential to reason, learn and understand the human mind, an opportunity which will help insurance to shift their approach from ‘Detect and Repair’ to ‘Predict and Prevent’ and will transform every single aspect of the industry. In fact, the required technology and techniques to achieve the situation already exist and have produced the desired results across other industries.

Interestingly, some of us might argue that Actuaries using advanced stats and techniques to understand the risks have been the stalwarts in Insurance industry. However, revolutionary advances in Analytics techniques and AI technology have expanded the core discipline of insurers and will reshape the Distribution, Underwriting, Pricing and Claims. There exists a huge opportunity for Insurers to become a data – driven insurance organization and adopt the technological and analytical advancements in order to gain the competitive edge.

Advanced technologies and Machine Learning techniques are already affecting distribution and underwriting with real time pricing, dynamic underwriting and targeted customer acquisition. The emergence of IoT devices like Fitbit, Telematics etc and increased online interactions of the individuals is helping the insurers to know the right customers in a way like never before. The auto Insurer very much knows the driving habits of the policyholder; it also knows the social profile of the policyholder. On the other hand, Life and Health insurers have a closer understanding of their customer’s lifestyle, demography and financial

profile. The information thus available, complimented by advanced machine learning techniques holds the potential to bridge the gap between the insurer and customer resulting in better pricing, right risk profiling and availability of best fir product. It is very interesting to note that with more and more information available about the individuals because of open source data from other industries, external data and data from IoT devices, many insurers are already experimenting with simplified and usage based insurance products, streamlined and faster customer acquisition process, and more dynamic and real time pricing. However, most of these experiments are in their initial phase and are expected to get matured with more of such used cases.

Advanced technologies and machine learning techniques are already affecting distribution and underwriting with real time pricing, dynamic underwriting and targeted customer acquisition

Claims remains the important and critical function of any Insurance organization for two reasons: 1) Claims handling process defines the Insurance carrier’s relationship with the client and 2) Insurance carrier also needs to stop any malicious claims reported from fraudsters. The usage of wide variety of data related to hospitals and policy holders, advanced machine learning techniques and AI technology are being instrumental in handling initial claim routing, increasing efficiency and accuracy and decreasing turn-around time. In a world blessed with data and technology, even the fraudsters present a formidable challenge for Insurance companies and Machine learning and deep learning techniques have come to insurance carrier rescue. Affinity analysis to understand the possible fraud nexus, anomaly detection techniques to read scans and many more such supervised and non-supervised machine learning techniques are fast becoming instrumental in detecting fraudulent claims and hence increasing profitability. Insurers are slowly starting to understand the importance of data sharing as well along with use of machine learning techniques to solve the Fraud problem in the industry. Various public and private entities are coming together to create ecosystem in order to share data for used cases under a common regulatory and cybersecurity framework. A right solution to the Insurance fraud challenge will not only assist insurers but also to the customers; it will help recalibrate pricing and curtail claims approval time.

With the increased applications of convolutional neural network and deep learning technologies, Insurers have access to models which are constantly learning and adapting to the world around them – enabling new usage based insurance products and customer engagement techniques while responding to the underlying risks in real time. The industry today is on the verge of a paradigm, tech-driven shift and the early adopters will continue to have a competitive edge for times to come.

This rapid evolution of the industry must be fuelled by integration of data ecosystems, advanced technologies and Machine learning techniques. Data has become the most valuable asset of any organization and Insurance industry is no different. Most AI technology and Analytics techniques will give best results when there is high volume of data from variety of sources. Insurance carriers must develop a well – structured and actionable strategy for both internal and external data along with data from IoT devices. Overall data strategy will have to find a way to organize internal data so that it can be used for new analytical insights and obtain access to external data in a cost – effective way. The other most important part for the insurance carriers to gain this competitive edge is creating the right talent and technology infrastructure. The best fit people should be technologically adept, creative and willing to work at something that will not be a static process but rather a mix of automated and machine supported tasks which continually learns and evolves. It’s time for the carriers to look at these disruptive technologies as exciting opportunities to gain edge rather than a threat to their current business.

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