How Analytics Insures the Insurance Industry?

Insurance is something that we all need but often are reluctant to sign the dotted line.

Insurance industry is one of the least sticky industries, where customers stay with the product for a predetermined amount of time ranging from 3-10 years at the maximum. 25% of policies lapse in the first three years and 40% lapse within ten years. This result in millions of people being uninsured leaving insurer's flummoxed.

Analytics comes to rescue helping insurers with data and intelligent insights on life insurance. The insurance industry generates enormous volumes of data like for an individual who is insured via a term plan and will undergo many life changes including employment positions, personal milestones like marriage and having kids, resulting in multitude data of changing heath and income parameters. Insurance companies should analyse and use this goldmine of information as this will help them to predict their customer behaviour and subsequent demand of different insurance products.

Data and Predictive Analytics will Drive Insurance

Data coupled with Predictive analysis can explain the customer behaviours and connect the dots so that insurers can go beyond the transactional data of the number of life insurance policies under management, and their customer ownership.

Insurers have to extend their insights beyond the strictly transactional data and aggregate them with the unstructured big data including customers geographical locations, their professions, their health and ailments, life milestones like marriage, kids etc. to be able to retain them beyond the policy terms.

How Can Data transform to Revenue for the Insurance Industry?

Insurance industry collects a huge amount of data from their customers; this data is handed over to underwriters, for risk evaluation using various actuarial data, claims data and other techniques to assess the cost factor to insure the customer. After the underwriters evaluate risk and the cost, the gross and net premium is calculated and policy issued. The customer then parts with the first premium and off goes the process. This has been a traditional path followed for insurance leaders and alike that is not any more scalable in the modern times.

Humongous data sits in what is essentially a siloed network, where underwriters have limited accessibility to communicate with agents and brokers. The data collected by brokers or agents is restricted to sharing to third part agents and after it is input to networks, it sits there in a database somewhere. Thus, valuable information is lost in databases.

Thus, to turn insurance data into valuable business insights, giant insurance companies and brokers are upgrading their systems to take the advantage of data analytics and deploy it to its fullest potential. Leveraging insurance data is poised to play a larger and more important role in every aspect of the business.

Transforming the Insurance Industry

1. Customised Offerings

Industries have woken to customer centricity and customised offerings, instead of the old adage that "father knows the best," customers believe in a trusted consultant who can help them get the insurance plan suited to their needs. Data analytics algorithms can help brokers build customer loyalty by predicting customised plans suited to each individual customer. Intelligent insurance management platforms allow agents to utilize technology which offers them actionable insights based on previous insured customer data. Intelligent management platforms empowered with smart dashboards allow agents to get a 360 degree overview of each customer's portfolio. The system automatically alerts if any of the customers have a gap in coverage to enable agents to offer more value added coverage.

2. Fraud Prevention

Insurance data provides actionable intelligence to predict potential customers who are most likely to commit insurance fraud before the event occurrence. Social media platforms provide a powerful medium to analyse and study if a potential policyholder might engage in a fraud. If a policy holder reports of an accident to seek a claim but is seen holidaying through his/her social media profiles, then this is a fraud which can be prevented by smart social media analytics.

3. Premium Pricing

Traditionally, to make statistical predictions of insurable events for an insurance product, companies have relied on "The Law of Large Numbers". Through this principle, insurers are unable to predict individual accidents or incidents. This prediction may work for agents but might not be true for policyholders; an experienced driver might get stuck with a group of learning licence holders, increasing their probability of an accident and be charged the same premium for their policy. To stay in the competitive insurance market, businesses have to device actionable insights from data through predictive modelling to track individual policyholder behaviour and fix policies prices.

Data analysis algorithms allow insurers to maximize their revenues by:

•  Analysis of the profitability of policies offered

•  Fix policy premium pricing

•  Customised product offerings

•  Improve employee (agent) productivity

The need of the hour is to develop a cloud-based data-driven broker management platform aggregating, compiling and visualising all the data sources gathered from the policy holders to give businesses an access to a variety of reports to understand their customers in real-time basis.

Data Analytics will be taking the insurance industry forward, and a positive change has just begun.