AI & ML Are Playing A Pivotal Role In The Life Insurance Space

In conversation with Marksmen Daily: Evolving customer behaviour and technology call for a paradigm shift - a shift that yields better results and measurable outcomes. Analytic capabilities help insurers in classifying customers into homogenous clusters in order to better understand their behaviour and to group common attributes. By doing this, companies can work on customized solutions for a personalized experience, thereby ensuring customer delight. Such Hyper-personalization leverages big data to deliver tailor-made solutions for every customer depending on their individual financial needs.

The fact that the pandemic helped reinforce the importance of technology in our day-to-day life, the use of artificial intelligence (AI) and machine learning (ML) in the insurance sector cannot be ignored. The technology landscape underwent a 360-degree change and companies are trying to offer a personalized customer experience across prospecting, on-boarding and the customer servicing journey. Budgetary spending on innovations such as AI use cases and pilot projects is at an all-time high largely because of the outcome that these technological tools offer in a quick turnaround time and enhancing customer experience. 

Customer Data and Intelligence 

Indian life insurance sector has evolved to a level where customer data and intelligence touch the entire value chain. Smart and advanced processing of data leads to informed decision making, personalized servicing, optimized revenue and reduced risks.

AI-enabled automation enables instant issuance experience for the customers thereby reducing cycle times for completing the purchase of a life insurance policy. Traditionally, insurance underwriting was heavily employee-dependent process involving multiple levels of checks, analysing historical data with complicated systems, processes and workflows. Intelligent process automation has eventually simplified the underwriting experience by integrating machine learning algorithms that collect, read and deliver insights and predictions from the massive data pool. This automation process is also eliminating outdated rules, and managing straight-through-acceptance (STA) rates thereby minimizing application errors.

Risk Assessment Automation 

Risk assessment automation enhances operating efficiency. Integration of data with independent bodies responsible to act as custodian of industry data plays an important role in fraud detection and early warning signals. Integrated models also help in the ongoing assessment of the risk that an insurance company carries in its books and hence can plan better. In future, cross-agency data integrations will further help build an ecosystem which will help organizations across industries. 

Machine Learning

Machine learning-driven engines deliver deep insights into customer behaviour and interests. This enables the sales team to put forth a stronger recommendation with a product that is best suited to the customer. This eventually improves the competitiveness of insurers including the increased market penetration and new business. Digital technologies such as optical character recognition (OCR), artificial intelligence (AI) and its umbrella technologies such as machine learning (ML), natural language processing (NLP) and deep learning (DL) are transforming the distribution strategies across all verticals.

By integrating robotic process automation with machine learning and cognitive technologies we are able to build intelligent operations and boost productivity. With the reduction of turnaround time, companies are able to deliver a delightful customer experience.

Agile, Digitally Savvy and Customer-Centric

At Canara HSBC OBC Life Insurance, our goal is to transform the business to make it agile and customer-centric by making policy issuance journey and customer services smooth and quick driven by various new technology adoptions. Data has become a key source of competitive advantage for identifying profitable niches and future risk, increasing persistency and achieving greater operational efficiencies. Data culture is the next big thing and lies at the core of business functions while augmented intelligence is the new trend that is catching up fast among teams. We bring together the best capabilities of both - humans and technology to achieve our business goals and offer customer delight. 

One size fits all approach is no more applicable in the life insurance industry. In our Company, we started by applying smart algorithms and machine learning methods to segment our existing base to understand our customers and create a customer-centric proposition for servicing and products.

We are proud to have built robust predictive models using regression/ classification and other machine learning methods to predict the possible future outcomes to last-mile accuracy. These models develop the approach of prediction at the customer portfolio, policy, family (household) and the sourcing level. Model tuning is a continuous process. Our analytics teams are leveraging capabilities over a large customer base to find inter-relationships and correlations through behaviour analytics and customer segmentation models. 

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