How Poor Data Quality Can Spoil the Digitization Game for the Insurance Industry
Since the insurance industry in India was liberalised in 2001, it has witnessed several gradual, yet disruptive, changes. One such trend, that started around 2005 is that the customers increasingly started searching and comparing insurance policies online. The idea gained momentum in the subsequent years and a decade later, many insurers had set up fully functional and flourishing online channels, moving towards greater digitization for selling policies.
According to a Boston Consulting Group (BCG) report, the online insurance market in India is expected to grow to Rs 11,000-15,000 crores by 2020 (US$ 1.7 billion to US$ 2.4 billion). The report also found that Indian buyers are ready to purchase insurance online in increasing numbers, among other financial products.
Our LexisNexis Risk Solutions India consumer survey* of policyholders in Metros and Tier I cities** recently showed that 23% of health insurance and 34% of motor insurance is now purchased online, and there is an opportunity to shape insurance processes to harness this trend.
As insurers are looking forward to bank on this digital wave to cater to customers from diverse complexities, backgrounds and cultures, poor quality of data remains one of the fundamental challenges that needs to be addressed. Data is unorganised raw material that needs to be cleaned, cleansed, rationalised, transformed, to arrive at information. Information can then be used to make decisions.
A West Monroe Partners survey ‘Data Driven Insurance: Harness Disruption and Lead the Way’, identified poor data quality as the biggest roadblock for insurers looking to implement big data and analytics in their enterprise. Such problems need to be addressed, before the data goes further downstream for analysis.
Let’s see how poor-quality data can spoil the digitisation game for the Indian insurance industry.
Delivering a superior customer service requires relentless improvement and collaboration across business functions, right from distribution to underwriting to claims handling. Before adopting a customer-oriented policy, it’s important for insurers to identify the potential customers, their interests and requirements.
For example, by combining the right health data collected from the applicant in the question-answer format, life insurers can build an algorithm to predict which applicants can qualify for insurance without undertaking certain medical tests. This will reduce hassle and the cost of tests for certain applicants, and contribute to an improved and refined customer experience.
The overall data diagnostic capacity needs to be augmented to offer 360-degree view of the customer.
Better quality data helps insurers to develop products attuned to the needs of their customers. It also helps them to explore opportunities present in the otherwise off-the-radar, Tier II and Tier III cities (those with populations of 50,000-99,999 and 20,000-49,999 respectively).
Though agents, brokers and other offline channels remain the primary setting for buying insurance and getting advice (and especially for life insurance), an increasing number of consumers from these cities are adopting newer ways for accessing information about insurance products.
With more than 50% of mobile traffic coming from Tier II and Tier III cities and as smartphone demand from these cities is outgrowing demand from Tier I cities (those with 100,000 population and above), quality insights from this traffic can provide a huge opportunity for insurers to reach right down to the bottom of the demographic pyramid.
According to the Global Brand Simplicity Index Study, 64% of consumers are willing to pay more for simpler and more intuitive experiences. The census of India 2011 suggests that 50% of India’s population is below the age of 25 and they are seeking speed and simplicity over their personal devices. A lack of quality data can disrupt this process.
