Building Data Equity in the world of Insurance!
Increasing number of Indian Insurers are now adopting analytics to gain competitive differentiation in an industry where very little innovation has taken place.
It has been debated for long now that data is treated as an asset and that it should reflect on the balance sheet. The implications of Data are far more wider and deeper when it comes to the Insurance business, as it forms the backbone of the entire insurance value chain. This makes the consideration on how we treat data more relevant for an Insurance organisation. However, data by itself has no intrinsic value unless a process is applied to generate meaning and decisions are made using these insights. Increasing number of Indian Insurers are now adopting analytics to gain competitive differentiation in an industry where very little innovation has taken place.
Following are some of the areas where an Insurance company could get the maximum bang for the buck.
For most insurers, a lack of underwriting profitability has been the key problem. However surprisingly very few companies have adopted advanced analytics to address this fundamental issue.The current processes on portfolio monitoring and decision management lack the agility and ability to transfer insights to action in a seamless manner.
The Actuarial, Underwriting and Sales team need to work very closely to ensure that the insights to execution process is effectively and efficiently managed. An analytics framework which enables an organisation to manage data exploration, decision management, analytic modelling, deployment and monitoring on one platform would ensure that the discovery to value realization process is shortened and tightly controlled.
Analytics Diffusion for Distribution:
To growbusiness and tap into newer customer segments and geographies,Insurance players will have to employ transformational thinking. Traditionally, insurance companies adopted a more product-centric approach, where the company decided which products go to which channels. Modern setups demand a more customer-centric approach where the customers demand what, where and how they wish to purchase insurance.
While attempting to move from being product-centric to customer-centric, insurance companiesare bound to facechallenges.Predictive models to assess each channels profitability banked with claims experience and building geodemographic visualizations using internal and social media data are some of the areas where analytics can play a huge role.
Risk Based View to Customer Based View:
Most decisions on the quality of business to be underwrittenare based on the performance of the risk segment; a personalized view is rarely taken. A data exploration exercise would give away that business from a specific car model (privately owned) from a particular region is loss-making, enabling insights for a decision to discontinue business. However, within this business segment, you may have few but profitable customers who have stayed with the organisation for 5 years and never claimed.
With access to analytical backed tools Underwriters can now pre-empt premium costs and policy parameters on a more realistic view of risks,rather than generalised assumptions. Integration of highly granular and singularised characteristics into the underwriting process would help drive a more personalized consumer experience.
Fraud identification is an important lever to controlling the Claims cost. Most insurance companies have large investigation teams relying on rule-based processes which are expensive and not completely effective. With advanced analytics,organisations can now look at both identifying the event and entity based frauds as well as accurately unearth fraud rings. Advanced technology also helps explore fraud networks across all claims data and visually identify sequences and spatial associations. Analytics cannot replace the process of investigation but can definitely help teams to improve the hit rate and focus only on cases which deserve their attention.
Identify right use for IOT:
Insurance organizations with a medium term strategy in place on how to utilize data & generate value should adopt IoT. This should be prioritized basis the business mix and profitability of the line of Business (LOB). For example, if the book suggests that Health LOB needs attention then investments made in wearables could help to underwrite the preferred risks. For Fire LOB investments in connected home and factory, devices like thermostats, intruder alarms, smoke detectors and water alarms can help mitigate risks thereby improving the claims experience.
The Devil is in Deployment:
Quite often, organizations decipher the insight generation piece in the Analytics journey correctly but fail to plan on how the implementation simplifies the consumption of analytical data and more importantly how the learnings post implementation of the insights loopback . If there are many manual handoffs in this process, it is likely that the analytics initiative will either fail or not realise the potential ROI.
This will be the single factor to determine success of any analytics story. Increasing number of companies areconsidering the added value of Data Governance and perceive it as a prerequisite to all information-related initiatives. Insurance industry constantly grapples with data quality issues. A framework and culture within an organisation where data is owned, managed and nurtured will be a strong launchpad for all analytical projects.
Analytics is no longer a futuristic initiative and the early adopters will gain competitive advantage!!!