The COVID-19 pandemic accelerated the use of digital distribution in life insurance. The bancassurance channel – with digitalization at the core of its operation – has the potential to build on this momentum.
In response to aging populations’ rising interest in financial protection products, insurers are turning to the banking industry for product distribution, capitalizing on banks’ large customer bases and easy access to financial information.
Historically, the most successful bancassurance programs focused almost exclusively on selling credit life and savings-linked life insurance. With the market maturing and demand increasing, insurers are now focusing more on incorporating stand-alone protection products into bancassurance portfolios.
The traditional approach to bancassurance follows the propensity-to-buy model: Potential customers likely to buy insurance products are identified by looking at the pattern of those who already purchased the products and applying the pattern to all bank customers. This approach has worked well in many instances, particularly for savings-linked products. For protection products, however, the underwriting required for these offerings has presented an additional hurdle – a time-consuming and intrusive step in the process.
Today, advances in data and technology are streamlining this step for many consumers. Using the abundant financial data collected by banks, insurers can quickly identify low-risk customers and, once targeted, offer them financial protection products with significantly reduced underwriting requirements or even with no underwriting at all. Removing this pain point in the purchasing process can speed the customer journey and increase sales.
Financial information, long used by insurers in risk assessment, opens the door for many potential data-driven solutions. Even when access to information is limited, insurers can consider offering a simple product with only a few key underwriting requirements based on available bank data. They can take it a step further by applying mortality experience to develop a model that does not require a complete dataset from the bank, but rather only some key variable distributions to re-calibrate existing datasets or models to fit the market. Leveraging existing insights can greatly reduce data requirements, while maintaining an approach that is highly predictive for mortality or morbidity risk. Even though the most effective models are based on combining financial data with risk experience, this approach can be challenging because it requires linking two highly regulated industries.
Nevertheless, shared incentives between the two industries can make the right bancassurance solution a win-win-win for insurers, banks, and the customers they serve. Consider this: A large international bank was interested in selling additional protection products in a particular market by leveraging its massive financial data with linked mortality information on approximately five million customers. Partnering with RGA for risk assessment and advanced data analytics support, the bank developed a program to identify potential customers with low risks. The bank was able to offer a life insurance product with no, or very limited, underwriting requirements to more than 10% of its customers.
Various pros and cons based on resources, capabilities, and market demands shape a variety of data-driven bancassurance solutions. For example, data availability is a major factor in deciding the optimal approach to meet business needs. Because complete financial data from a bank is not always accessible, trade-offs must be made between available data and accuracy.
Other considerations might include whether to implement on premises or in the cloud and whether to develop solutions in-house or with a partner, such as reinsurer or consulting company. Reinsurers can tap their extensive risk assessment experience, a broad industry view, and substantial modelling expertise – helping to fill information gaps when access to bank data is limited. RGA has worked with many insurer clients to develop bancassurance solutions that solve the problem of limited data access, enabling them to digitally deliver protection products to bank customers.
These solutions primarily focus on using customer banking data to identify low-risk customers for guaranteed or simplified-issue coverage and can build on existing products and processes. Incorporating insights from propensity-to-buy models, for example, can speed pre-approval and streamline the product distribution process, resulting in higher conversion rates and increased sales.
However, technical innovations alone usually do not maximize business return. Robust data-driven solutions typically require coordination with other business areas, such as product design and client engagement, for successful implementation. Quality product design can help ensure a financial protection product is suitable for bank distribution, while effective client engagement can increase the likelihood that pre-approved offers can both attract customers and reduce anti-selection risk.
In addition, since these solutions involve the banking industry, regulation and data privacy are primary considerations, especially with customer data necessary for developing and executing programs. Close collaboration with legal and data strategy teams is essential. A coordinated effort overall is key to a successful bancassurance program, and these critical contributions should be considered from the beginning.
Development of an effective data-driven bancassurance program requires technical experience in risk assessment, risk mitigation, and data modelling. In addition to technology, a holistic digital solution should include such critical components as product design, client engagement, and the contributions of subject matter experts.
The potential for data-driven bancassurance is clear. With the right approach, insurers can use banks’ proven distribution channels to attract new customers and better serve existing policyholders.