Companies collect an enormous amount of data that emanates from their business operations on a daily basis. The data is held in their databases so that it can be used to create value. It may be stored to facilitate marketing or improve certain areas of the enterprise making operations more efficient. However, even with advanced artificial intelligence and data storage solutions, some companies still find themselves drowning in data logjams.
Data backlog is common in insurance companies. More so because of the nature of the information they collect and what they intend to do with it. For example, insurance companies store data for analytical purposes. This information can be used to identify the kind of claims they process most and how it affects their business. Furthermore, structuring the data concerning processed or issued claims can form a basis on training machine learning models. With artificial intelligence applications, it is possible to use the data in ways that can help a company to create value.
Data Centralization
Centralization of data is one of the methods insurance companies use to manage their expansive data. Software developers and vendors offer data integration services with the aim of centralizing data. Various categories of data are therefore condensed to be used in Al applications or in business intelligence.
The software offered to insurance companies is meant to package the data in a well-coiffed manner to enable the users to easily access different layers of the data This is achieved through a single dashboard that allows insurance agents to access the information they require easily.
Claim processing
Insurance companies use predictive analytics data solutions to calculate the amount payable in terms of damages. The analytic software has the ability to automatically compute the changes that occur in premiums after settling a claim. The information is displayed on the dashboard where an insurance agent examines it further before signing it off.
Fraud Detection
Fraudsters in insurance have an immense interest in the claim processing departments, and that’s why they are highly targeted compared to other areas. AI applications incorporate predictive analysis and anomaly detection to identify suspicious claims. A client’s claim processing system is linked to an anomaly detection software and a machine learning model. Every new claim has to pass through this system where it is weighed against what is considered as normal. If it doesn’t much with the claims that are seen as normal, an insurance agent is notified. The agent conducts further examinations on the claim to ascertain whether it is fraudulent.
Customer engagement
Advertising campaigns carried out by insurance companies target a diverse pool of customers who respond differently. AI applications come in handy in such situations to determine the demographics of customer response to advertisements.
The use of predictive analysis helps to develop and improve marketing strategies. For example, unlike the millennials, older people are reluctant in using mobile applications which offer insurance services. Using AI intelligence, an insurance company is able to identify detailed reasons as to why older people don’t use their application.
Insurers are also slowly adapting to millennial demands, as traditional insurance products don’t seem to match millennial lifestyles. For example, millennial car insurance buyers usually prefer anonymous car insurance quotes over specific ones, since they are used to the convenience and relative anonymity of shopping online for products that match their way of life.
Customer lifetime value
This a measurement of the period of time a customer may stay insured by a particular company and the revenue that the company is likely to generate from the customer. Historical data is heavily involved in making such determinations. Big data helps to project CLVs. Statistical averages, credit scores, past insurance policies, and other paraphernalia are also examined in this process.
AI-driven analytics are increasingly becoming an important anchor for data management in insurance companies. This is due to their ability in processing and repackaging of data in a presentable manner for display on a dashboard. In addition, pulling records and accessing specific data more conveniently are some of the major benefits of these data solutions. Instead of companies having massive data backlogs which aren’t providing the value that is expected, restructuring of the data can bring more value to the company.
