Insurance is a risk-transferring mechanism that ensures full or partial financial compensation for the loss or damage caused by event(s) beyond the control of the insured party. And in turn, risk management is very important for the insurance industry. Insurers consider every available quantifiable factor to develop profiles of high and low insurance risk, which determines the insurance premiums. And this leads to higher premiums for higher the risk.
Today, data availability has greatly increased, and with this plethora of information at hand, insurers can evaluate risk of insurance policies at a much higher accuracy. Insurers collect a great deal of information about policy holders and insured objects, on which statistical methods and tools based can be used to analyze data or to determine insurance policy risk levels.
Risk management has many facets, and each of these have an avenue where data analytics and intelligence can solve problems and open new avenues for business success.
Image Source: http://www.ramco.com/downloads/WPR_Insurance_Analytics.pdf
Customer centricity is critical in insurance. To know your customer’s activity across the organization, their purchasing habits and preferences can enable your staff to provide the differentiated experience that will attract new customers and build loyalty of existing.
Microsoft Power BI has the ability to analyze your customer base, enabling you to understand the value that each customer brings. Understanding the financial impact of each customer, you can identify opportunities for increased revenue and lower cost scenarios.
An extremely potent example here would be mapping real-time information and calamities to control exposure.
For example, we all hope we won’t see another storm like Superstorm Sandy – but if we did, and you could know the exact path of the storm and the assets that you insure within that path, you can decide whether to move those assets to a safe place in order to better manage your exposure.
Using Power Map in BI, you can know weather data with catastrophic risk modelling data and geospatial modelling relating to that risk. So if you were looking at a high net worth individual who had a lot of art in their home, and you recognized that your exposure was a couple of million dollars, that insight can help in your decision on whether or not to spend a few thousand on moving the art to a secure location.
Claims handlers can optimise the claims process, minimising losses and increasing customer satisfaction by speeding up the entire process.
Sales managers can better monitor the performance of their sales agents, highlighting areas of underperformance and uncovering potential opportunities for upselling.
Profitability of insurance business depends on the actualization of the assumptions relating to expenses, income and mortality. A very close constant monitoring of each of these factors is an absolute essentiality like Premium Analysis, Financial Analysis, Product Profitability Analysis and Underwriting Loss Analysis
Managing risk is the most important goal for insurance companies. But the amount of data and variables that go into insurance analytics is huge and needs tools and approaches that help insurance professionals to quickly identify trends and act on them.
Microsoft Power BI helps you analyze the health of your business, collaborate, and explore silos of data to build robust, reusable models.
Having worked extensively on SQL Programming, ETL Development and Database design for the application, I am very excited about the big data advent in today's world. Apart from work I love to travel and explore old historical monuments and temples.