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Author: Dr. Swati Srivastava is a seasoned underwriter with ICICI Lombard General Insurance.

Historically, insurance industry is proven to be resistant to change but the new age technologies have not spared any industry. With changing times, insurance industry is also undergoing a digital transformation. Artificial intelligence and Machine Learning has the potential to act on various insurer pain points while at the same  time give a customer centric simple and easy solutions.

Right targeting for Business growth-

Traditionally insurance has been seen as a sales driven industry where it is mostly push factor from insurance companies through their agents and brokers channels for selling the products. As a result the insurance penetration is less than 4%. But with the increased awareness and catastrophic impacts on individuals post Covid-19, cyclone Amphan etc., the pull factor is also getting stronger.  So with the right targeting and selling products which are best suited for a customer, demand for insurance will also increase.

A big challenge faced by the insurance sales team is in getting the timely quotes to the customers. Insurance being a very price sensitive industry, the sellers have to constantly prove as why the customer should choose them over the other. This requires innumerable iteration in quotes with policy covers and price. But the traditional way of underwriting does not allow this kind of flexibility in a time bound manner.

So with the help of AI and ML, insurance companies are adopting chat box where in sales team can ask for multiple iteration in quotes shared by the underwriter. Unlike the manual underwriting, Artificial Intelligence will help in analyzing the data and customize the insurance plan as requested on one click of a button. The sales team will then approach the client with much higher confidence as they know they are having their underwriter along with them in all such meetings. Thus there will be a higher chance of customer acquisition.

Assessing risk and underwriting

Since ages, for risk assessment, underwriters rely on information shared by clients. The problem here is that there is asymmetry in sharing of information and the customer could withhold some important information which are crucial for the underwriting purpose resulting in inaccurate assessments. Underwriting is the core function of insurance industry. But it has become even more complex with huge amount of customer data scattered across different papers in an unstructured way.

Most of the time of the underwriter is gone in structuring this data which a very tedious, time consuming and expensive process.  This has a scope for human error as well. Poor underwriting can result in losing the prospective business opportunities as well as underwriting losses.

Machine Learning technology for eg. Natural Language Understating (NLU) enables insurers to deep dive into more abstract data spread across different pages and formats, there by pulling such pertinent information together and better assessing the potential risks of the customers. More accurate risk assessments results in more optimal pricings thereby bringing in competitive advantage in an industry which is very price sensitive. With AI and ML, as we consume and analyze more data, we can design such products which are more suited to the customers and this will act as a positive reinforcement in customer retention.

Artificial Intelligence will definitely help in saving crucial times of the underwriters in structuring the data and highlighting the insightful information for the underwriter to take the timely call and action. Thus AI not only helps in better assessing the risk with deep data analysis but also gives more competitive pricing options in a time bound manner, saving both time and energy of the underwriter to focus on more on the high risk groups and manage portfolio level loss ratio.

Some are of the view that with the coming of AI and Machine learning, manual underwriting could be a thing of the past. This has significantly disrupted the retail business, where in customers can choose their health or motor insurance plans based on few details requested on the app. But Group business requires a lot more analysis and we are still very far from that goal.

Fraud Detection

Insurance industry works on the principle of “Utmost Good Faith”, where in it is believed both the insurer and insured will share all the information in a clear and transparent manner. But with human interface, there can a breach in this principle. Fraud can happen at multiple levels.

At the time of policy booking, a customer might hide past medical or claim history, resulting in adverse risk selection.

Claim Processing- There has been an increase in fraudulent claims in the all the vertical lines of insurance be it health, motor or property. Since insurance companies tries to adjudicate claims within a TAT of less than 2-4 hrs in case of health insurance, this leaves very little time for the insurers to deep dive into the claim file and study hospital bill. With the coming of Government’s health insurance schemes like Ayushman Bharat, insurance companies have seen a rampant increase in fraudulent health claims.

There have been many instances when a claim is registered from a hospital, which is only present on papers in reality. Similarly, a claim is registered by an individual, without him even getting admitted in a hospital.  In a country like India, where  the literacy level is less than 70%, a large chuck of population does not understand the terms of insurance and the chances of encountering fraudulent claim is even higher.

With the coming of AI and ML, insurers can assess the claim file in more detailed manner and can identify and raise red flags in case of any errant billing done by the hospital without impacting the TAT for claim processing.

Similarly, in case of motor claims, AI enabled sensors installed in the app of the company can assess the damage on the spot, thereby help in reducing the chances of fraud.

Claim Processing

The biggest pain point of the insured comes when one encounters claim. People get nightmares thinking of the lengthy forms they need to fill before filing for claim. Even after all that hassle, they still worry if the claim will be admissible or not and how much time the claim processing will take. Even for insurance companies, they need to analyze the claim file in detail for policy terms and conditions and then determine how much amount is admissible for the claim and to be paid to the customer. All this needs to be done accurately within a TAT of 2-4 hrs without any scope of human error.

AI enabled chatbots can help in gathering the relevant information from the customer and help filling in lengthy claim files. AI technology can streamline the claim processing through process automation.  Thus it will be benefitting both insurer and the insured.

Conclusion

Artificial Intelligence in insurance industry is still in very nascent stage and insurance companies still experimenting the scope of AI in various dimensions to incorporate them in everyday practice. It is expected with the increased process automation using AI and ML technologies, a lot of work, if not all, currently done by humans will become obsolete. Role of human will be to focus on high value and tricky cases with the enhanced information available to them.

Also with the coming of new insurance companies based on insurtech, there is a huge pressure on the traditional insurance companies to adopt the AI and ML technologies in almost of lines of their operations. Hence there are significant push and pull factor for the insurance companies to revamp themselves and adopt the new technologies. It will be really interesting to see as how the insurtech start-ups will give competition to traditional insurance companies, which already enjoy the customer trust and business, adopts and integrates features of AI and ML in their day to day activity.

 

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