The furore surrounding insurance fraud in Kenya is well justified – in fact, what sector in the country is more prone to fraud than in the underwriting market? Who is the best equipped to curb this vice – threatening 40% of underwriter’s earnings other than insurtechs?
There is no industry more data driven than the insurance sector, yet the traditional players continue to face challenges in making good use of this data to optimally make decisions. Insurers are still stuck in the past, according to recent report Reinventing Life Insurance Agency Distribution Globally from Morgan Stanley and the Boston Consulting Group, stating that the sales processes remain “old- school”, cumbersome, inefficient and inconsistent with the fast evolving customer expectations that are now being set by digital leaders.
Kenyan insurance sector growth has been stagnant over the years. Latest statistics from Insurance Regulatory Authority (IRA-K) shows that Gross Direct Premium Growth Rate was at 3.5% in 2018 compared to 6.3% in the previous year, while its penetration ratio stands at 2.43% during the period under review, a drop from 2.68% registered in 2017.
General insurance – largely motor and medical classes of business – remains the biggest casualty in fraudulent claims. Coupled with other factors, the business registered KES1.65 billion underwriting loss in 2018 compared to KES1.65 billion the previous year.
To right this decline, it is time insurance stakeholders genuinely embraced data going forward. And what better way to build a more resilient infrastructure than to partner with insurtechs?
Insurance technology firms are capable of creating an entire ecosystem where an underwriter can be able to deliver personalised offers based on deep customer insight, come up with broad set of services offered around an integrated set of customer needs and offer real-time monitoring propositions around key risk objects such as cars or homes.
According to Jean-Nicholas Hould, Co-Founder and Chief Data Officer of Breathe Life, an American Enterprise Commerce Platform targeting the Insurance Industry, as much as traditional players have wealth of existing data in their domain, partnership with insurtechs cannot be gainsaid.
“Partnering with an insurtech is typically much faster, less risky, and more successful than going it alone,” he says.
Underwriters will take advantage of clean data sets and more cutting-edge modern technology such as AI and machine learning. Insurtechs understand the mindset of a consumer, and their immediate needs.
Insurance firms still use archaic methods to analyse and manage claims, that is, by employing human intervention, using bales of paperwork and phone calls. What this means is that there is an opportunity for real threat when it comes to fraudulent claims and applications.
Through closer collaboration with insurtech, an underwriter is able to optimally utilise credible data, a necessary ingredient for product development and pricing. Also, this partnership can mitigate risks associated with claims as fraudulent applications are detected way before they can do any harm.
Insurtechs are able to achieve this through use of predictive analysis, text mining, use of new technologies to help investigate and monitor specific claims, enable more transparency throughout the claiming process, offer identity validation and timely pay the claims.
Already the industry – IRA Kenya, Association of Kenyan Insurers and respective players – have rolled out mechanisms to combat fraud, including application of technology. Still, fraudsters are constantly and tirelessly testing vulnerabilities in their systems.
Capturing fraud in the market is arduous, as it involves use of adjustors, investigators and police force. This can be simplified through data analysis and the comprehensive cross-referencing of data points across internal ones and external databases.
At WazInsure, we offer tailor-made products for insurers can be able to assess the key indicators of risk at both the insurance application stage and the claims stage.
Underwriters can benefit from our predictive framework to look at the past claims activity and behaviour of an individual and generate a relative risk score to help make an assessment at point of quote or when settling a claim.