Over 25% of Kenya’s insurance industry income is fraudulently claimed leading to insurers incurring heavy losses.
To counter this, Kenindia Assurance has taken steps to curb the fraud by incorporating Artificial Intelligence (AI) in its motor insurance claim processes.
The insurer is utilizing artificial intelligence to fast track its claims resolutions through the use of anomaly detection, sentiment detection, text analytics and a self-service portal.
Integrated Motor Insurance Data System
Kenindia Assurance Deputy General Manager, Joyce Mathenge, says that motor insurance is the main contributor to insurance fraud hence the need to develop mechanisms to lower their risks.
“With more Kenyans owing cars and motor insurance being mandatory, we find that having ineffective due diligence processes and a corruption culture usually leads to motor fraud. We have invested in artificial intelligence as well as continuously training our teams to enhance their abilities to detect motor fraud,” said Mathenge.
She added that Kenindia is looking at setting up a data centre where customers’ insurance history will be stored with the aim of detecting motor insurance fraudsters and their methods.
This will be in addition to the industry’s Integrated Motor Insurance Data System (IMIDS) through the Association of Kenya Insurers which has made it possible to track the insurance history and claims of all vehicles insured in the country.
Deloitte’s Insurance Outlook Report 2019/2020 East Africa showed that while motor private and medical business classes are the largest classes, they are also among the most loss-making businesses. The report urges insurers to explore other emerging business classes that have a potential for growth to diversify their business mix.
Over the years, the insurer has invested heavily in information technology to better their service delivery for their life, general and medical operations.
“Even as we look at ways to remain relevant in these competitive times while improving our customers experience, it is our vision that with the help of artificial intelligence, we will now be able to further shorten our claim processes, validate all claims and pay our customers the correct claims, faster,” concluded Mathenge.
Loss making classes
According to the PwC report, motor private and medical classes are the largest classes and among the most loss-making businesses.
The insurers should investigate other emerging business classes that have the potential for growth to diversify their business mix.
“Alternatively, insurers need to investigate means of reducing the loss ratios on the large business classes using big data and AI,” the report recommended.
A research released in April 2019 by insure tech firm Bismart noted that only 60% of motorists polled in Nairobi can confirm a full insurance cover.
This leaves the rest potentially exposed to the very risks that they assume they have taken cover against.
Following a three week period of research, Bismart found that about 3% of the policies are reportedly unpaid and premiums are not remitted to the insurance company, despite the holders having insurance stickers.
Such policies, according to the survey, were at the risk of being cancelled from the books, again leaving the insured exposed to risk.
While the survey was unable to verify the true status of 22% of the motor insurance policies, the research found that 12% of the covers do not exist in the books of the underwriters despite full payment for the cover and motorists having cover requisite certificates.
Additionally, despite clients paying for and bearing certificates for comprehensive covers, Bismart found that about 1% of the policies were instead registered as third-party covers.
According to Bismart Insurance chief executive Eunice Maina, fraud in motor insurance is perverse with many motorists driving around without protection even though they have paid for it.
