What Predictive Analytics Is Reshaping

Predictive analytics helps insurance companies create customer profiles, prevent fraud and offer excellent pricing options based on risk hedging.

Insurance is a business sector where predictive analytics software has some of the most straightforward applications, also with a high return on investment (ROI). Predictive analytics is already offering companies significant savings, and it is expected to grow exponentially in the next few years. Most likely, it will become the standard practice for insurance and risk management. The advantage is that the data lake used for predictive analytics can collect both internal and external data and correlate it to identify patterns and create almost real-time reports. In turn, this would prevent fraud and help to analyze behaviors. The results can be used in various areas of the business, which include risk assessment, pricing policies, claim processing, fraud management and trend analysis. Here are a few of the ways predictive analytics is reshaping the insurance sector: Pricing This is one of the first applications of predictive analytics in the insurance sector because it offers a high ROI. As sources become more diverse and precise, results will be more actionable. Although there are relevant security and privacy issues involved, insurance companies are collecting and analyzing data from sources that, for example, 10 years ago were not available or considered relevant, like social media. The good news is that now data is no longer an average of a cluster, but, after the general profile is created, the machine can measure how each person scores against the grid. Market Trends and Risk Assessment Identifying market trends is all about detecting the right patterns in data and anticipating their further development. Fortunately, AI is perfect for doing just that, regardless of the volume or complexity of the input data. Recently, both the U.S. government and the E.U. ruling organs have adopted an “open data” policy, making available lots of census data related to population statistics, education, safety and more. These new sets offer insurance companies new opportunities regarding macro risk assessment. See also: 3 Ways to Optimize Predictive Analytics   Correlating these sets of data within the right algorithm can help insurance companies to create clusters of customers grouped according to their profitability. For example, such analysis can provide the answers to questions like the probability of a person being involved in a car accident in a certain town, or the likelihood of default for a mortgage for a specific educational profile. The next step is to extrapolate the results and make predictions for the following periods to stay ahead of the market. Fraud Detection and Prevention Insurance is a very vulnerable sector for fraud. People are tempted to pay for an insurance policy and “make it look like an accident” to collect the value of the insurance. Although over the years insurance inspectors have become well aware of classic schemes, new tools are needed because the insured risks become more diverse and linked to digital activity. The Coalition of Insurance Fraud estimates that over $80 billion is lost due to fraud. The same studies show that one in 10 claims is fraudulent. Therefore, insurers are ready to go to any lengths necessary to prevent such actions. The advantage of predictive analytics is that it can signal potential fraud before it happens. The machine would identify specific patterns associated with fraud, usually by means of dots that don’t connect. Tailor-Made Services Most companies, from utilities to retail and especially e-commerce, strive to offer customers a very personalized experience. The insurance sector needs to be at the forefront of this practice, too, as products have few real differentiators apart from the price. In this business, predictive analytics can look at customers’ profiles and predict needs, create bundles of services and help these customers meet their personal goals. Depending on a customer's profile, such purposes can include increased safety, budget management, saved time or significant risk hedging. These systems also offer the opportunity to prioritize claims and serve customers not only in their arriving order but also by evaluating their lifetime value, to avoid losing important ones who need their cases sorted faster. Customer Retention Learning from the world of retail and even HR, the insurance business can benefit significantly from identifying those customers who are about to cancel their policy. Usually, by giving these some extra attention, they can be kept onboard for another year or more. In this case, data insights and customer behavior analysis can help insurance companies identify those who are already looking for solutions from competitors. Focus on the Extraordinary Not all odd claims are frauds, but unexpected and expensive claims can hurt an insurance company’s profit margins. In this case, the role of predictive analysis is to identify potential risks and warn the customer to take all necessary preventive measures. Although such outliers are harder to detect due to the lack of previous relevant data for training, the advantage of using machine learning is that it can put together several distinct pieces of information to identify potential risk. See also: 3 Key Steps for Predictive Analytics   Privacy Concerns As in all matters related to the use of personal data, some people could have three categories of concerns, as stated by the report of the Geneva Association. To wrap up this discussion of data-centric insurance, let’s look at them:
  • Privacy and data protection concerns. These are mostly related to the fear of discrimination based on profiling. The other problem in this category is intrusiveness in the right of self-determination, especially when customers can’t afford the prime for their risk class, thus restricting their lifestyle options.
  • The individualism of insurance problems. The problem of exclusion should be at the forefront of insurance companies’ internal regulations. Excluding certain high-risk categories can lead to social pressure and the need to find alternative solutions such as state funding.
  • Implications of big data and AI for competition. The fourth technological revolution is already causing disruption and changing markets. By implementing these tools, we can expect that some jobs will disappear or reorganize. This will also happen to companies that will not adopt the new standards.

Emilita Marius

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Emilita Marius

Emilia Marius is a senior business analyst/project manager. Combining eight-plus years of expertise in delivering data analytics solutions with three-plus years in project management, she has been leading both business intelligence and big data projects.

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