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Analytics in Depth

Predictive Analytics and Insurers

Every business depends on forecasting to some degree. Accurately predicting future market conditions, product demand, expenses and revenue is an important endeavor for any company, particularly so for insurance companies. The ability for an insurance company to accurately predict the cost of its policies, and avoid adverse selection (total number of insured’s weighted towards higher risk policies) dramatically impacts it's profit or loss. Subsequently, the insurance industry is a perfect fit for Predictive Analytics.

Insurers have always relied on some form of forecasting for setting policy costs. Originally, they simply made educated guesses as to the cost of premiums. As the industry matured, they began to measure single factors such as age, condition, or past history (univariate analysis). As insurance forecasting became more sophisticated, they began to use multiple factors (multivariate analysis). Currently, Predictive Analytics is considered an industry best practice - using data mining techniques, advanced statistical models, and sophisticated algorithms - and combining additional information like credit scores and relevant economic conditions to yield more accurate predictions for potential insurance outcomes.

Customer behavior has become more complex, with vast amounts of information, price comparisons and purchasing options literally at their fingertips. Predictive Analytics can help insurers determine more accurate pricing, identify more fraudulent claims, create better product features, and help them target potential new clients.

Use Of Predictive Analytic Techniques

Data Deficiencies

The quality of statistical models are entirely dependent on the quality of the data used in their development. Insurers with inadequate data will often recruit third-party data providers (data vendors, rating bureaus, etc.) to augment their data, ensuring the output from their predictive models is both accurate and relevant.

Automation and Relationships

Studies have shown that the most profitable relationships for insurers are their longest term clients. However, advances in technology and improvements in insurance transaction automation has lowered the costs of switching providers, making it easier for insureds to shop around, and ultimately switch providers. This lessens the value of these long term relationships.

On the other hand, automation can help insurers shed their reputation for being indiscriminate when lumping all policyholders into the same basket, or "risk-pool". Simple premium models based on zip codes, income levels, or other seemingly non-relevant data (as viewed by the customer) can be replaced by more accurate, sophisticated risk analysis metrics. This will help regain the trust and loyalty of the consumer by more accurate and fair premium pricing based on the actual the risk he or she represents.

Risk of Commoditization

Historically, insurers have differentiated themselves from their competitors based on factors such as quality customer service, personal attention, as well as price. But now the internet has ushered in the age of instant price comparisons, and recent economic turbulence has resulted in more price-sensitive consumers. This has lead to many insurers trying to compete solely on price, and predictive models are helping insurers do just that. This may lead to a lack of differentiation among the quality of services being offered, resulting in the potential commoditization of the market.

Risk Pooling vs Risk Identification: Size Matters

One of the main tenets of insurers minimizing exposure to losses is "risk pooling" - creating homogenous groups of significant size of insured so that losses by a subset of the group is offset by non-losses of the remaining group members. It stands to reason then that the larger and varied the group, the smaller the risk.  But as referenced above, this pooling of insured also contributes to the image of insurers being indiscriminate to their policy holders - and puts pressure on competitive pricing. Predictive Analytics can enhance the process of pooling by helping insurers create smaller, more refined homogenous groups through better risk identification - resulting in more competitive, risk commensurate premium pricing for each risk pool - while mitigating exposure to loss.

Predictive Analytics: Evolutionary or Revolutionary

With respect to the three core functions of insurers; underwriting, marketing and claims - predictive analytics is evolutionary AND revolutionary. For underwriting, predictive analytics is evolutionary. For marketing and claims, it truly is revolutionary.

For underwriting - advances in hardware, software, and predictive modeling algorithms has brought operating efficiency and more accurate risk analysis and pricing - yet is just another evolutionary advancement in the toolset used in the job of predicting the future.

For marketing and claims - these technology tools are revolutionizing the insurance industry:

  • Claims Management: Identifying claims that will benefit the most from active case management - attorney retention, liability claims, subrogation analysis, severity analysis, etc.
  • Fraud Detection: Identifying claims that are more likely to be fraudulent so that resources can be directed on the highest-risk claims.
  • Supply Chain Analytics: inventory levels, distribution center placement, routing.
  • Finance: identification of financial performance drivers.
  • H.R: prospective employees, workforce compensation, benefits selection.
  • R&D: which product features are most desired by customers?
  • Marketing Programs: Identifying the most profitable future prospects for new policies, churn prediction and retention modeling, loyalty management and customer lifetime values (LTV) analysis, cross-sell and up-sell analytics, and renewal of lapse policies, website promotion analytics.

FluentAnalytics Empowers Insurers

We Define and use Analytics

There is no shortage of evidence to show that, in nearly every decision point in life, analytical decision-making is more accurate and produces better decision outcomes. Conversely hunches, intuition, guesstimates, and conjecture based decision making provide for below average performance, and remove the possibility of improved decision making.

Performing analytics on a large data set starts with understanding and formulating a hypothesis. We then move on to gathering and analyzing relevant data. This then leads us to interpreting and communicating these analytical results. Developing quantitative thinking adds to a data scientist’s ability to deliver precise results. In full spectrum summarizing data, finding the meaning in it and extracting the value is a complete solution to an analytic data project deliverable.

We Understand the Science of Data Scientist and Prediction

A data scientist is "a better software engineer than any statistician and a better statistician than any software engineer." As for skills and experience, I think coding ability and an understanding of how numbers behave are both vital, as is curiosity. "Insane curiosity" is the most important trait of data scientists who decides to find out what happens.  All true data scientists have started playing with some data at 8 p.m. and suddenly find it is 3 a.m. and they're still at it. But equally important is the ability to communicate with people. If you uncover a vitally important piece of information in a set of data but are incapable of imparting it to others or convincing them of its import, it will have no impact -- and therefore, it will be as if the information were never discovered.

If the predictive analytics problem is well defined like predicting churn on a customer or default on a credit account, data mining tools allow for savvy data professionals (data architects, data analysts, BI architects) to easily build and test models and keep trying with adding more data and variables until they like the model's economic performance. On the other hand if the problem is not clearly defined and the selection of variables is all over the place, a more formal training in data sciences would certainly be required. On the other hand savvy business users who are used to carrying out rocket science in tool, armed with the knowledge and understanding of what predictive analytics can do, should be able to conceive problems in innovative ways that can be solved using predictive analytics. The role of a data scientist as typically defined has to be broken up in 3-4 different job roles and they will easily get the job done.

Prediction is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results. In marketing, for example, a customer's gender, age, and purchase history might predict the likelihood of a future sale.

In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software. Predictive modeling is used widely in information technology (IT). In spam filtering systems, for example, predictive modeling is sometimes used to identify the probability that a given message is spam.

FluentAnalytics has determined where predictive analytics can benefit the insurers’ business model most effectively. We invite you to discuss with us our consulting experience in the field of predictive analytics, and how we empower insurers with better outcomes.