The insurance industry is growing with an enormous amount of data that is being gathered from various sources like smartphones, government statistics, social media, banks, etc. Analysis of data collected from these sources will enable Insurers to understand customer behavior and preferences.
Much of this data is being stored, utilized, and analyzed by advanced technologies like Data Science because of its ability to draw decisions from quantifiable and data-driven evidence. Applying Data Science in the insurance sector has many benefits. The most common are
- Data Science enables us to assess risks, track and analyze them, and develop effective strategies to attract customers and retain them.
- It directs the actions based on customer behaviors and preferences and defines specific goals.
- It enables the identification of new opportunities and creates plans for the target audiences.
Applications of Data Science In Insurance
Insurance companies have always done quantitative research but now they are broadly leveraging more data and models, and successfully performing activities like :
Fraud detection and investigation – Insurance companies suffer huge financial losses because of fraudulent activities. The user can detect fraud by identifying undetected suspicious activities through Data Science and its algorithms. Using multiple techniques, data science makes it possible to discover the fraudulent activities at the right time and save the insurer from significant losses.
Personalized Product Development – Customer retention is important in any industry. The insurance industry is no exception. It obviously becomes a necessity to strategize a plan according to the customer’s requirements. Data Science and its platforms gather all the possible data pertaining to the customer requirements, analyze it and develop a plan most suitable to the customer. By leveraging Data Science on quantitative and qualitative data sets, the insurance companies and the actuaries can develop customer-specific products to attract them.
Customer segmentation – The Data Science algorithms perform customer segmentation based on their financial status, age, location, behavior, preferences, etc. Based on this segmentation, solutions are derived from and provided to the relevant customers. It is also possible to cross-sell policies and tailor personal services relevant to a particular customer segment.
Risk assessment – Risk assessment is the quantification of risk and identification of the reasons for risk. Implementing the risk assessment tools will assure the predictions of risks as well as limit them to the minimum to avoid huge losses. The algorithms detect the risk and combine the data concerning individuals’ risks that differ by nature and effect. This enables the insurance company to forecast segmented/group risks and thereby the overall company’s risk.
Data Science in Insurance- Challenges and concerns
While Data Science offers new opportunities and numerous benefits to insurance companies, there are a few challenges and concerns to address-
Data management and ownership – With a huge amount of data collected from various sources, it becomes the biggest challenge for the companies to manage this data. Clean, enriched, and centralized data always provide valuable results. Another obstacle that, usually, the policyholder and the insurer face is regarding the ownership of the data – will it be owned by the policyholder or the insurer. If the ownership lies with the policyholder then will he/she be able to use the pre-existing data with another insurer when they change the provider? Or if the data is owned by the insurer, then it will certainly restrict the consumer’s right to avail of a better deal with another insurer.
Cyber Security – With large amounts of personal and financial data of the customers being accumulated, cybersecurity always poses a threat to the industry. The risks of data being lost, stolen, or corrupted have to be addressed by the field experts. A solution or process has to be provided to prevent hacking and access by unauthorized users.
Lack of Human Judgment – The analytics models rely on various methods and algorithms for various financial aspects of insurance like pricing the premiums, claims, etc. Despite the fact that these methods and algorithms are being adopted by many insurance companies, there always will be an absence of human involvement. No algorithm can replace human knowledge and judgment.
Make Insurance Expensive For Some Segments – A certain market segment may find that insurance is getting adversely affected by Data Science. A clear understanding of the customer’s risks may lead to an increased premium and expensive insurance, thereby making a particular segment avoid insurance.
The application of statistics by the insurance companies has begun long back. Hence, it becomes imperative for this industry to adopt Data Science and leverage the same across business processes and benefit from them.
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