How Predictive Analytics Will Help the Insurance Industry's Evolving Business Needs By anticipating needs and preferences, PA tools are enhancing customer satisfaction
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Buying an insurance policy to cover one's life or assets is always a cool thing to do. But as the insurance market gets more competitive, insurers are using technology to improve service offerings. Technology is especially effective in attracting millennial and Gen Z customers, who are more tech-savvy compared to peeps of the earlier generations.
The tech tools comprise artificial intelligence, cloud computing, blockchain, SaaS (software-as-a-service), advanced analytics and suchlike. Among these tools, advanced or predictive analytics is one of the most essential since it helps insurers forecast the result or outcome of unknown events.
As everyone is aware, the insurance industry thrives on information. This can be from both internal and external sources. Be it an applicant's KYC form, the use of smart devices, a person's social media activities, online shopping habits, interactions with agents, etc., all these are vital as potential sources of information. Yet, while such sources of information are helpful for insurers, they end up generating data in billions of megabytes.
It is here that predictive analytics (PA) comes in handy in making sense of these massive mounds of data. PA tools break down the data into insights and information that are meaningful and can be actioned. Nowadays, insurance companies are deploying PA in insurance underwriting. For instance, historical trends can be investigated based on available data sets and the outcome of future events can be predicted.
Broadly speaking, PA helps in identifying customers' needs, correct risk assessment, proper pricing, identifying the risk of fraud, pinpointing customers at risk of cancellation, transforming the claims process and anticipating trends, besides other uses.
Insurers garner data about prospective customers via multiple channels. This includes online searches for products and services. India is an underpenetrated insurance market, primarily because of limited knowledge about insurance products.
In seeking to drive greater market penetration, insurers keep proactively pitching various new and innovative insurance policies to prospective clients. Each pitch is crafted by scrutinising people's online search patterns, the sites they frequently visit and/or the products and services they are seeking to buy.
For example, a person may be keen on visiting some overseas destination in winter. As a result, s/he will keep browsing various online tour operators in checking out suitable packages and deals. When insurers gain access to such browsing data, it helps in pitching the right kind of insurance products offering relevant overseas travel insurance to the prospect and her/his family.
These customized offerings could have coverage features, including contingencies such as theft of travel documents and baggage, unforeseen cancellation of trips, health emergencies and the like. The specific offerings pitched to these travellers based on their recent browsing history are more likely to be useful than pre-existing ones that are not relevant.
Similarly, PA can help insurers in identifying customers requiring specific attention or unique offerings. These could include travellers likely to cancel or postpone their trip or even lower the coverage due to cost constraints. Advanced analytics and insights will also help insurers in anticipating people likely to be unhappy with the coverage or other aspects, based on their past behaviour.
Such precise knowledge can help insurers stay a step ahead in anticipating supposedly "unforeseen' events or behaviour and modify coverage terms as required in preventing any potential problems. But without PA, even experienced insurers may miss warning signs and fail to pre-empt any unwanted incidents.
Another critical aspect is detecting and preventing fraud as far as possible. As per Indiaforensic's research, insurers in India lose more than 8 per cent of revenue to frauds annually. Such losses are then passed on to policyholders via higher premiums. Preventing such frauds at inception can help keep premium rates lower.
Prevention is possible through pre-emptive measures. This is possible by reviewing the history of past fraudulent claims and picking up specific patterns in the approved and denied claims of individuals. Advanced data mining tools and algorithms then help in determining the probability of such frauds. When fraudulent claims are in check, it helps insurers in establishing a more efficient system and faster processing of genuine claims by minimising the time needed for due diligence.
Preventing any loss of business and addressing prospective problems before they escalate helps in providing more personalised service and prioritising claims. As a result, these measures save time and money and help in increasing customer satisfaction. Prospective customers seeking the best insurance services and products would do well to opt for companies such as BimaKaro, where digital technology is used in processing claims faster and driving greater customer satisfaction.
As insurers deploy predictive analytics, they will be better able to meet the needs of diverse customers through actionable insights. In the ultimate analysis, such intelligent tools will benefit both the insured and insurers.