The digital journey starts at the first customer touchpoint

The data-driven approach starts at the customer prospecting stage itself where digital marketing and segmentation allow for targeted communication to potential customers. For example, promotional campaigns like income tax saving opportunities aimed at taxpayers during filing season encouraged potential buyers to engage with an AI-powered chatbot that answered queries and provided guidance.

Once the customer is onboarded, a ‘suitability analysis’ takes place, where the customer is asked a set of questions. Based on the customer’s responses, prepackaged offerings are suggested to him or her. This simplifies the buying process and sets the tone for a quicker and clearer decision making on part of the customer. “The intent here is to ensure that the customer’s needs are taken care of, rather than us pushing them or taking decisions on their behalf,” says Sachin Goel, chief technology officer and head of digital for Tata AIA.

During the buying process, Tata AIA has the capability to dynamically waive off documentation requirements if the customer is eligible for such a provision. Data, however, remains at the centre of decision making to enhance customer experience.

Solving the comprehension gap

In the life insurance industry, the gap between what the agent or relationship manager has sold versus what the customer has understood, is referred to as mis-selling. The problem occurs when customers do not really understand the details of the policy, for example, product features or details such as the limitations of the cover or caveats — and go ahead with buying the policy. The insurance regulator, IRDAI, puts the onus of reducing this gap on the insurance company.

Before smartphones and video recording became commonplace, a call was often made to the customer and a telecaller asked specific questions and recorded the conversation.

Many companies continue to use similar processes or have migrated to using text-messaging. But Tata AIA pioneered a solution capability called Video Pre-Submission Check (vPSC), the pilot for it started over three years ago.

This process is set within the customer journey wherein the customer responds to a set of questions on video. Using AI techniques, the video is matched with the photograph submitted and an informed decision is taken on whether to approve the customer, have more checks in person, or request for another video, or reject the purchase.

“The recording happens as part of the customer journey itself, and sits on the point-of-sale applications directly. It helps solve a real problem in the industry and helps us at the claim stage as well,” says Mr Goel, adding, “We have been doing this for a couple of years; our solution is fairly flexible. We can even send a link for recording via WhatsApp or via an agent. This came in truly handy during the Covid-19 lockdown and testimony to its success and significance are the calls that we received from our industry peers enquiring about this capability.

“Many insurers use text or calls, but that isn’t of great help if there is a dispute during claims. Video data, however, is generally more convincing.”

Humanoids in action

Life insurance is generally a long-term contract, usually for 20 years at least. Premiums are almost always automated. “There isn’t much opportunity to do out-bound calls unless there is a problem. We use analytics to segment profiles which have a propensity to surrender, and we use personalised communications to ensure retention,” says Mr Goel. For others, there is just a reminder call unless they have missed payments.

Customers have a multitude of service options ranging from the website to WhatsApp to social media and chatbots. Intelligent automation in servicing has paid huge dividends with self-service soaring to unprecedented levels of 82% pre-Covid and 91% during Covid.

Deep learning for underwriting

Underwriting is one of the most complex processes in the life insurance business. Over 55% of overall business and 85% of non-medical business at Tata AIA is passed through an automated decision management system that follows a rules-based model to make underwriting decisions.

For the medical insurance business, the policy necessarily goes to a human underwriter, which is often time-consuming and slows down the underwriting process. “For this, we have deployed deep learning models based on the data that the company already has. These models have been developed to mimic complex underwriting decisions and tell us which policies should be approved,” says Mr Goel.

At this point, the manual process runs in parallel to the automated, deep-learning model, and helps with approval decisions with more decision-making information being available.

“Life insurance is a complex business. The value of our covers is very high, often running into several crores. Using data, AI and deep learning, we are well on the path to make underwriting, which is at the core of our business, simpler while, at the same time, enabling our customer to make smarter decisions,” says Mr Goel.

After all, insurance is about ensuring peace of mind and intelligent application of digital technologies is helping Tata AIA achieve newer goals.

—Haroon Bijli