How business intelligence can transform the insurance industry

The business world is rapidly discovering the power and potential of big data. Today’s organisations have unprecedented access to information on business performance and consumer behaviour that can help them refine their products and services in near limitless ways. 

Big data is typically categorised as structured (sales and website performance data) or unstructured (customer feedback data). Either way, data is often stored in departmental silos across an organisation. As businesses embark on their own digital transformation journeys, decisions on technological investments (including analytics) must be based on what will provide the greatest benefits to both employees and customers. For the greatest effects, information and insights must be available to users across departments and levels, empowering everyone in a business, from the sales team to the C-Suite.

The insurance industry provides a great example of this progress in action. The industry is both people-centric and data-rich, meaning that insurance companies are in an ideal position to refine their business practices by utilising sophisticated data analytics. As insurance is based largely on understanding risk, data plays the critical role in helping organisations make better, more-informed business decisions. Specifically, there are key operations within an insurance company that can be transformed using enterprise analytics.  

Shorter claims cycle through improved fraud detection

Fraud detection is a high priority for insurance companies when processing claims. The ability to spot inconsistencies can help insurers prevent large pay outs for fraudulent claims. However, fraud perpetrators are becoming more sophisticated and are able to manipulate most rules-based fraud solutions on the market today. 

By using predictive analytics, which combines artificial intelligence (AI), machine learning, data mining, and modelling to create useful forecasts, insurers can detect fraud more effectively at each stage of the claims cycle. The powerful combination of rules, modelling, database searches, and exception reporting allow organisations that embrace this type of advanced, predictive analytics to stay one step ahead of perpetrators.

Agile and responsive sales and distribution teams

Insurance sales representatives need fast, accurate access to information to make the most of on-site visits with brokers or clients. Traditional sales solutions are just not equipped to handle the complex needs of modern sales teams. These legacy systems fall short because they fail to provide in-depth insights into individual customers or prospects when they are needed most. As a result, many organisations are looking to mobile BI apps for their sales enablement needs. 

Mobile access to resources, provided by modern BI tools, empowers sales representatives with the insights they need when they are in the field — helping boost productivity and delivering a competitive edge. These applications can arm salespeople with context-aware maps, multimedia content including sales presentations and training videos, and real-time access to quote analysis, buying patterns, and demographics.

Improved customer insight and management

Given the limited opportunities for face time with customers, and the competitive nature of the insurance market, it is critical that insurers maximise every customer interaction. Insurers that fall behind in the field of customer management risk losing out on market share and seeing lower overall profitability. 

By analysing data, businesses gain greater insight into the preferences and behaviour of their customers. Aggregating data into a comprehensive customer profile provides a better understanding of customers’ preferences, lifestyles, call centre interactions, and other key characteristics. Insurers can then use this data to forge deeper connections and deliver highly relevant and personalised offers and services.

Intelligent underwriting and claims processes

The claims process is typically the single largest expense for an insurer, which is why it is important to make it as efficient and effective as possible. Processors and underwriters have to sort through an incredible amount of data on a daily basis, including: adjusters’ hand-written notes, data from fraud lists, and information stored in claims management databases. The accuracy of underwriter calculations is instrumental to the success of the insurance company; if the calculations aren’t precise, the company runs the risk of being overpriced in comparison to the market, or it could even suffer significant losses from unexpected claims payments. 

Powerful analytics reinforce the underwriters’ ability to confidently act upon data related to customer credit history, risk, and market information. It allows claims adjusters to easily assess critical data related to policy information, police reports, loss, frequency, and severity. And by mobilising applications, adjusters can access claims information from any location and directly input critical assets related to a claim, such as photos or notes from accident scenes, repair estimates, or other relevant information.

For example, social media provides a particularly rich and personalised source of data, with information that is accessible in near real time, directly from the customer. This enables insurers to create smarter marketing campaigns, quickly respond to direct consumer feedback, and create new products based on consumer preferences.

Conclusion

Analytics and big data are poised to have a big impact on the insurance industry for years to come. How new analytical methods are used can inform and supplement a company’s digital strategy and greatly aid in decision-making, particularly where predictive analytics is put into practice. Institutions that embrace data and new analytics technology will likely see returns in increased efficiency, visibility, and streamlined management processes in addition to the benefits outlined above. However, a strict data governance strategy must be implemented to ensure adherence to processes. Data can be organised, controlled, and shared with business users so they can extract their own insights without the risk of corrupting the data for other departments. With the right strategy, technology, and governance, data analytics can provide a significant competitive advantage. 

Hugh Owen, SVP, Product Marketing, MicroStrategy
Image Credit: Sergey Nivens / Shutterstock