Optimize Insurance Underwriting for customer retention using mobile wallets for an insurance provider

When it comes to insurance, the tech-savvy customer of today is no longer encumbered by lengthy, manual, and time-consuming processes where insurance agents must authenticate identity, underwrite risk, set a premium, and then draw up a policy. Traditional insurers are being outpaced by digital-first insurance start-ups that promise mobile-first experience, faster disbursals, self-service capabilities, and process transparency.

In such a landscape, competition is tough, riding on price. The rise of insurance aggregators allows customers to compare different insurance providers based on price, coverage, claim settlement ratios, benefits, and more. With price being a purchase driver, insurers need to re-examine pricing effectiveness to ensure they are attractive to customers while ensuring that the enterprise remains profitable.

Why is pricing so important?

Traditionally Insurance companies have used a cost-oriented pricing model based on claims experience and are calculated from internal data sources. An expected profit margin is added to arrive at the final cost. The issue with this approach is that it doesn’t factor in changing market dynamics like competition, price sensitivity of customers, and economic conditions of the geography that they are operating in.

Consumers today are also more informed than before. Internet is empowering customers with data to compare different insurance products by price, value, and benefits. Also, with the emergence of direct players and aggregators, prices have been further pushed down.

With all these changes happening, the approach towards Insurance pricing must be a data-driven approach which means that insurance players should make a significant investment towards their digital infrastructure. This could include:

Accurate Data Collection: This includes not just internal sources but external sources as well.

Data Processing Capabilities: Data collected needs to be processed faster, which can help derive consumer insights. These insights then can help in either validating or changing our approach.

Data Security: Storing the collected data on secure servers and having strict policies on its usage.

Technological Innovation is rapidly changing the pricing structures across industries, and therefore Insurance companies must adapt and make pricing consumer-centric rather than cost-centric to retain competitive advantage.

Pricing premiums correctly is critical because it hedges against risk or losses that the policyholder may incur. Moreover, this risk must be diversified across their product portfolio to stay profitable. Underwriting risk involves complex statistical models that ingest different variables, make assumptions about customer behavior, and provide certain outputs that inform the decision on premium costs. However, there are many cases where insurers end up charging customers either too little or too much for the perceived risk. Both of these have a negative impact: Charging the customer too little exposes the insurer to cost liability, impacting profitability. Charging the customer too much leads to customer dissatisfaction and negative brand reputation, impacting long-term profitability. Further, customers are privy to pricing discrepancies, which are easily exposed through insurance aggregators that allow feature-based policy and premium comparisons.

Given this, insurers must devise more innovative ways to assume and calculate risk to optimize their pricing strategies. A data-driven pricing strategy can significantly enhance pricing efficiencies by enabling more accurate predictability. It will also allow insurers to perform better customer segmentation and risk underwriting, making price a competitive differentiator.

Case study: Modelling mobile wallet data for customer-centric pricing

A client in the insurance industry wanted to introduce a new policy plan lower than their existing entry-level plan. It was essential that the premium of the new policy was affordable to cater to customers in the low-income category. The aim was to position this new policy competitively to grow their customer base, and hence the insurer wanted to get the new price point just right.

Subex designed an innovative approach using mobile wallet data to give the insurer the right insights to make the best decision on pricing. First, data on mobile wallet usage was obtained securely from a third party and used to uncover behavioral patterns. Then, an analysis including medians and percentiles was conducted to arrive at a revenue gain calculator. Assuming various conversion rates, these inputs helped ascertain the ideal pricing for the new policy.

The primary customer behavior investigated was the maintenance of sufficient monthly balances. The premise was that those able to sustain their balance (after paying monthly bills) would be better positioned to afford a premium for insurance. Based on the outstanding monthly balances, subscribers were categorized into different percentiles groups – 25, 50, 75, 70, 80, and so on. For instance, a balance of $10 within the 25th percentile group meant that 75% of subscribers maintained a balance above $10 for more than 15 days.

Data from a single month was extracted and grouped into five parameters, i.e., minimum balance, maximum balance, 25th percentile, 50th percentile, and 75th percentile. These five parameters made up the five-point table that was used to design the revenue gain calculator. The revenue gain calculator provided estimations of the monthly revenue gain for the insurer based on different price points. It also provided insights into the expected adoption by the corresponding subscriber groups.

The analysis yielded an ideal price point of $32. This price point was arrived at using the 70th percentile of wallet balances. Based on the model, it is predicted that 50% of the subscribers would maintain a $32 balance in their wallets for over 30% of the time (9 days).

The price point analysis conducted by Subex yielded an affordable product for the insurer’s customers and promised some exciting ripple effects. For one, the model has given the organization a reliable manner to underwrite risk by using a new data source, i.e., mobile wallets. It may create some degree of cannibalization as customers from high-value plans downgrade to the lower ones. Finally, it will prevent customer churn and improve retention by allowing customers to switch to a new plan instead of changing their insurance provider. Ultimately, the price point analysis is helping the insurer effectively price newer policies for greater profitability.

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