Implementing a new initiative is a daunting process that requires a lot of thought and internal co-operation. Where data analytics are concerned it is essential to step back and review the business as a whole before implementing a solution, to ensure you can reap the benefits.

In a recent webinar Jamie Bisker, Senior Analyst at the Aite Group, mentioned that wise and agile carriers are viewing data as a strategic corporate asset, bridging gaps between silos of data. Treating data as a corporate asset, insurers can establish metadata about it and mine larger collections of data to extract value from it.

advanced analyticsAccess to vast amounts of data can assist insurers with risk management, customer analytics and financial performance, but the key is in the implementation of a process to analyze the data. Rather than allowing the IT department to solely implement an analytics solution, people and management processes need to be considered for it to be successful.

1. Before embarking on this journey, the business value that analytics can provide must be determined. Ultimately, revenue growth and increased profitability are at the forefront for any business, so determining how advanced analytics can improve profitability needs to be defined. Insurers who use advanced analytics to predict risk, select customers, control operating expenses, and calculate insurance premiums can easily increase profits.

2. The analytics teams are responsible for determining which internal and external resources are available and relevant to the specific issue. Integrating internal data with external third-party data can provide extensive analytics opportunities. Insurers managing employee leaves of absence can integrate their internal claim data with industry data provided by IBI or DMEC to improve and enhance their analytical abilities, while benchmarking their own data against industry standards. This provides insurers with insight to create goals and objectives to thrive for.

3. To effectively model the analytics platform, close collaboration between the analytics team and experienced claims adjusters is essential. The analytics team can develop a functional analytics tool, but if the internal team cannot or does not want to use the analytical software, the business will gain no value from the project. The internal claims adjusters working first hand will provide the team with valuable insights to improve functionality and usability of the analytical tools.

4. Following the successful development of a model, the next step is to determine how to integrate the model and determine the appropriate level of automation to allow. Ideally automated decisions will be based on high volumes of claim data with low risk attached to them. The more risk adverse a decision, the more likely the case will need to be reviewed by a claims supervisor. Again the analytics team should work closely with the claims administration team at this stage.

5. At each stage of the process it is essential to keep the claims team informed. Continued communications, training and collaboration can reduce the risk of the claims team rejecting the model. For the project to be successful, this is a key step and should be remembered throughout the project lifecycle. Following implementation, results should be recorded and reviewed periodically to ensure the project has been implemented successfully.

Data analytics can be extremely beneficial to insurers:

1. It can reduce the time taken to approve less risky claims, resulting in satisfied customers as well as reduced administration hours.

2. Better-predicted risk and policy premium calculations, resulting in increased profitability.

3. Improve investment opportunities by integrating internal financials with external economic information.

To learn more about the use of advanced analytics for insurers click here, and remember the advice from Jamie Bisker: Data should be considered a corporate asset to gain value from!