How to innovate in life insurance using Analytics and Machine Learning

It is about speed and brevity

Life Insurance industry is losing customers year after year with many Gen X, Millenials, and Gen Yers not interested in buying life insurance policies. In the context of declining interest, life insurance companies need to innovate in product development, marketing, servicing, customer experience management, and financial management. Advanced Analytics and Machine Learning techniques provide a way for Life Insurance companies to innovate across these areas. In this article, we cover how life insurance companies can use such techniques to create market differentiating innovations.

The challenge for life insurers is that while they are in an ice berg that is melting, up on top, things are looking good. After the 2008, variable annuity driven financial crisis, the life insurance industry has stabilized its books, reduced or eliminated financial risks, and has stabilized its book of business. However, while it has been doing this, the attrition rate on annuities and in life insurance has been increasing with many life insurance firms finding it hard to recoup lost customers. Furthermore, as projected, if the insurance rates go up and inflation also goes up, life insurance industry will continue to have more challenges in retaining its existing customers and grow new pools of customers.

In this context, we believe that life insurers can use machine learning and advanced analytics to create new innovations that are differentiated in the market.

So, what is machine learning

Simply put, machine learning is the ability of a computer to learn a model that could be used to predict various outputs. Things that could be predicted in life insurance can include when a customer will attrite (surrender an annuity contract or stop paying life insurance premium), when a variable annuity holder would increase their withdrawal patterns, life events that drive a consumer’s premium paying behavior (e.g., move), and other factors important to the life insurance business.

Machine learning techniques (there is a plethora of them) can be broadly classified into two buckets, based on the number of variables they can use as input. Techniques such as decision trees, non-linear regression models, etc. are typically used where the number of variables used to train (i.e., create) the system are around 25 to 50. The second bucket is made up of techniques that can take several hundreds to thousands of input variables and are usually used in cases where we need to find a “signature”. Examples of these techniques include Deep Learning used for facial recognition, components of speech identification. These buckets of techniques are often used in phenomenological data (i.e., real world signals). This classification is somewhat simplistic as new ground in the machine learning world is using the second bucket of techniques to solve problems that have been solved using the first bucket of techniques.

Advanced analytics is a broader and older term where statistical techniques such as linear regression, logistic regression and machine learning techniques are used to create predictive models. Advanced analytics is often used to distinguish predictive analytics application from actuarial and so called descriptive analytics where historical is analyzed to identify patterns based on human intuition.

So, how do all these techniques help an insurer create market differentiating capabilities? The following paragraphs drill down into each functional areas of an insurer.

Actuarial analysis

Life insurance companies and Annuity firms today use assumptions to set aside reserves and to hedge on the market. These assumptions are typically created with actuarial models that create cells of customers based on a few factors (usually less than five) that segment the book of business into equally distributed groups so that actuaries can compute aggregate ratios on attrition, withdrawals, and other factors. Typically, these values are computed for the previous year and compared against historical variations to see if the trend is changing its direction. A more in-depth analysis can step through these changes year to year moving forward or backward by using values from the current year to derive dependencies for the next or previous year.

This process creates several problems in the modern market place. Since the number of variables are small emerging trends or trends that are specific to specific customer populations are hard if not impossible to create. So, policies must be priced based on broad characteristics and the burden is shifted to underwriting in the life insurance industry to cover for risks. Annuity contracts must be priced with a significant fee since the risk of withdrawals cannot be estimate precisely and market hedging must be done with large pools of money reducing the flexibility in investment.

These factors limit the ability of life insurance companies to react to changing market conditions quickly and to create products that can be better suited for targeted populations of customers.

Machine learning techniques are changing the way this process is working in leading life insurers. First, instead of using about five variables to create actuarial assumptions, life insurance companies are adding more variables with a focus on variables that capture the change in behavior of policy and contract holders. Policy holders’ payment, withdrawal, surrender, change in payment mechanisms, and other variables provide fine grain understanding of who, when, and what are driving attritions, withdrawals, and acquisition of related products. Inputs from these machine learning models are also being integrated into the actuarial process to estimate reserves to be established and Monte-Carlo and similar simulation methods for estimating market performance of assets. Furthermore, by incorporating variables that were traditionally considered to be underwriting variables into the actuarial processes, leading edge insurers are reducing the complexity for policy underwriting and supporting the ability to create insurance products that are experience focused and target specific populations (e.g., millennials who are adventure travelers).

Underwriting

The holy grail in life insurance underwriting has been to pre-qualify clients similar to credit card pre-qualification for marketing so that the marketing and writing of life insurance policies is as smooth as friction free as issuing a credit card. Insurers have taken intermediate steps in this direction by installing rule based execution systems for straight through processing and other process optimizations. However, these steps have not made it possible to underwrite a customer without performing medical tests to determine their risk profile. Furthermore, due to the generality of life insurance actuarial models, it has been harder to reduce the number of questions that needed to be answered by the customer.

To reduce the need for the medical test, insurance companies are now experimenting with deep learning techniques that use commercially available medical data on radiology images, clinical trial data including blood samples and correlation of blood samples with diseases, text understanding of physician’s notes, etc. taking thousands of variables and predicting occurrence of certain diseases in the customer to be insured and the acute or chronic nature of the disease. While this brave new frontier needs to be carefully managed to avoid regulatory and customer backlash, green lighting policies using deep learning approaches can provide a way to overcome such negative reactions in the market place.

The key benefit provided by deep learning is the ability to include the thousands of heterogeneous variables in creating new predictive models that can be used to classify applicants into life insurance pools without performing medical tests. Consider the number of variables that blood samples measure. Add to it variables that radiology images and features that can be extracted from those images that point to specific medical conditions. Combine this with key information about chronic conditions and treatment plan information from doctor notes using text understanding. Enhance this information using life style variables about risky life styles that are usually ascertained by questions asked but now determined using external data sources, the number of variables expand far beyond the ability of conventional advanced analytics models. Deep machine learning techniques allow insurance companies permits life insurance companies to take these multi thousand variable set and predict the applicant’s risk class.

Product development

One of the key new trends in product development in life insurance and annuity is to create products for Gen Xers, Millennials, and Gen Y. From several projects, we have performed across our life insurance customers, we have seen that products that appeal to the younger generation are temporally relevant. While past generations have bought life insurance products to protect their family or to protect income by paying premiums for a long time (typically, not less than 10 years), younger generations are interested in insuring their life styles over a narrower period. For example, a millennial may be interested in buying life insurance as they rent a zipcar to drive around Boston or may be interested in buying life insurance for a few years after birth of a child.

While life insurance policies are available for specific travel (e.g., life insurance for air travel), the idea of life insurance products that protect for a shorter duration, ranging from days to not more than a few years is something that is new to the life insurance industry.

How can advanced analytics and deep learning help create these types of products? As mentioned earlier, the actuarial practices work with limited variables but very long history that is meant to predict and price mortality during normal course of life with significant risk in payments spread over many policy holders. However, products that address life styles must deal with shorter time frames and events that are numerous and historical data on their risk is not available for long periods of time.

However, with new sources of information that are more real time such as data about life styles collected from fitness devices, health information collected as described above, with deep machine learning’s ability to predict important events that could occur, life insurance companies can create new class of products suited to the life styles of new class of customers opening new avenues of revenue.

Marketing

Life insurers and annuity companies have significant opportunities in improving their marketing. Most of the companies today sell their product through distribution channels (agents, wirehouses, etc.) and perform brand marketing to establish their firm as a solid company that will not go defunct in the next few years. However, given the nature of these products, customers are very infrequently in touch with the insurance companies except when they would like to surrender their policy or contract or when they wish to claim against the policy. More targeted marketing to retain customers to increase profitability have typically resulted in increased mortality risk carried at the book level by the firms. If effectiveness of marketing is measured in terms of new customers recruited and customers retained, then life insurance company marketing has not been very effective.

Advanced analytics and machine learning can be used to address this problem. A general industry pattern is that up to 20% of term life customers attrit in the first 3 years not even paying back the marketing expenses spent on recruiting them. Another 10% to 20% attrite one or two years before term end and premium jumps and up to 50% attrite during so called shock period one year before and one year after the term end (e.g, 10th year for a term-1o policy). It should be clear that retaining customer during the early period and selectively retaining customers who don’t have significant mortality around the shock value period can improve the profitability of an insurer. By creating predictions on which policy holder will attrit when, prospectively identifying the signals and drivers for that attrition, and by better estimating total life time value for those customers, advanced analytics and machine learning techniques can help an insurer better target their existing customers.

In addition, this type of targeted marketing combined with machine learning refined underwriting and actuarial practices can create “offers” that are appropriate for the customer. Better estimation of mortality through machine learning driven underwriting and shorter duration life insurance offers (extensions on current policies) can allow insurance companies to target and retain late stage customers whose policy maturity may be imminent. Similarly, for annuities, a better prediction of withdrawal probabilities can allow annuity companies to target contract extensions that will help the customer keep more of their investments with the firm.

Summary

Advanced analytics and machine learning techniques can have fundamental impacts on all aspects of a life insurance and annuity firm. Above, implications of these approaches for most functions of an insurance company was provided. For further questions, reach out to svirdhagriswaran@us.ibm.com