10 ways Insurance Industry uses Data Analytics

Insurance industry has been going through a sea change in the way it handles data. Data has always been at the forefront of the insurance industry from the time it started (see Insurance History). Now, insurance companies have a heightened understanding of how data needs to be perceived as a distinct asset.

Here are a few ways we have helped insurance companies use their data :

Triaging claims and improving the claims process

Claims processing is the most cumbersome part of any insurance provider’s operations. Some providers have information systems that are able to pull the data from vendors, process the data and push back relevant data. However, most providers are not able to do this end – to – end in an automated, “no-touch” way. Most insurance providers have disconnected systems that lead to significant human interventions to upload claims, download claims and for payments. These disconnected systems or islands of processing are coalescing into large end-to-end systems and will reach a stage where claims processing will require no people.

One key aspect for claims processing is the triage to ensure compliance, completeness and other requirements. Significant use of data analytics can reduce the triaging time by understanding the different types of claims and their characteristics.

Detecting Fraud

A classic use of data analytics in insurance is detecting fraud. Though this is easier said than done because a top down approach is like “finding a needle in a haystack”. To comb through massive amounts of data and look for specific patterns is a time consuming process. Significant changes to the IT infrastructure are needed to ensure data processing is capable of achieving high levels of throughput.

An alternative is to look for proxies in the data, which can be used for quick identification of fraud patterns. Data analytics is used to ensure that these proxies are reliable and valid. Using proxy parameters reduces processing time and can be used for near-real time by customer service agents.

Detecting epidemics and outbreaks for health insurance

Insurance companies dealing with health and medical are at a vantage point of understanding diseases from a population. This advantage provides the ability for an insurance company to detect epidemics and disease outbreaks faster and help in containing the situation by sharing this information.
Drug interactions and drug efficacy for the pharma industry
Most insurance firms collect data about the patient’s diagnosis, treatment and prescriptions. There are openly available standards, such as ICD and other copyright standards such as CPT. Using a combination of diagnosis, treatment and prescriptions, an insurance company can provide drug interaction and drug efficacy based on a large population. This information would be valuable to the pharmaceutical industry to increase the pace of drug development.

Reduce risk for underwriting

Insurance underwriting is a demanding process whether actuarial experts set the policy premiums. There is significant data that is required to estimate these values. Even though actuarial sciences are a matured field, the foundation is based on analysing data. Using new data analytic techniques, the risk and tolerance for underwriting can be reduced and to a large extent the process of reviewing can be automated.

Countries where there is no unique person or citizen identifier

When insurance companies operate in countries which do not have national identifiers such as Aadhar (India) or Social Security Number (US), the insurance company is left to create its own identifier. Data mashing of different types of identifiers from each person, for example, driving license, municipal card, passport, etc would be required to ensure unique identification. Data mashing at the scale of a country would require a lot of data cleansing to obtain reliable data of a person’s identity.

Identifying Customers at the risk of cancellation

As part of customer service and customer retention processes, insurance companies can leverage data analytics to understand when customers will churn. Using historical data of customers who have left or asked for reduction of insurance premiums, a data model can be built to predict if and when customers may cancel or approach the risk of cancellation. The next step would be to bring this model close to the sales and customer service personnel so that preemptive changes to the customer approach is made.

Generation of Leads

Along the same vein as identifying customers at the risk of cancellation, the generation of leads also requires a predictive model that would point to which leads are most optimal to follow up. Since it would not be possible to follow through with all the leads that are present, a predictive data analytic model would reduce the sales fatigue and increase the chance of converting a lead to a customer.

Customized Policy Offerings

With increased competition, insurance companies are jostling to find their space in the minds of a customer. One possible approach is to customise the policy for each customer so that all the needs are met at a viable price point. For this approach, data analytics is required to understand the different clusters of customers and the costs and profit margins that are possible with each cluster. Also, multiple data sources such as social media, may be required to understand certain customer behaviours to fine tune a customised policy.

Improving Customer Satisfaction

Customer satisfaction is closely related to customer service. Getting the right data of a customer, in front of a customer service agent is critical to ensure that the agent can respond appropriately and offer alternate solutions. Using data analytics, it is possible to recommend services that similar customers have opted for. Using text analytics, it is possible to understand customer sentiments on a social media channel and armed with this information, it would be possible to initiate a new customer campaign or create a new product that alleviates this issue.

There are many more ways that are possible to help insurance companies use their data and help look for opportunities!