Category Archives: Technology

InsurTech considerations for the P&C industry

InsurTech seeks to improve upon traditional insurance processes by making use of technology like artificial intelligence (AI), mobile applications, and cloud computing. In this article, Milliman’s Tom Ryan takes a look at the InsurTech environment within the property and casualty (P&C) industry. The following excerpt highlights the dynamics stirring up interest in the industry.

The current interest in InsurTech is driven by a perfect alignment of four key elements, the “big Ts”—technology, talent, treasure, and a tempting target.

• Technology: Many of the ideas behind InsurTech startups are not new. It’s just that they were not feasible previously because of shortcomings in technology—even for the technology available as recently as four to five years ago. The improvements in faster, cheaper, smarter computing power, greater storage capability, and large blocks of external but “usable” big data have allowed many seasoned ideas to come to fruition.

• Talent: Many of the entrepreneurs behind today’s InsurTech initiatives migrated to insurance from other industries where they successfully implemented technological innovation. As these other industries get more crowded and mature, innovators are bringing their playbooks to more wide open spaces—the insurance industry. Visit the websites or read the backstories of many InsurTech startups and you will likely find references to prior successes in FinTech or at least a Stanford or MIT pedigree.

• Treasure: At the end of 2016, policyholder surplus in the U.S. property and casualty (P&C) industry stood near record highs of $700 billion. According to the Insurance Information Institute, the industry now has $1 of surplus for every 77 cents of net written premium, close to the strongest claim-paying status in its history. While this is good news from an insurer solvency perspective, the abundance of surplus relative to premium is driving a sustained soft market with low return on equity. Many insurers are responding to these conditions by merging with or acquiring competitors, buying stock back, or raising distributed dividends. It has proved difficult to put any excess capital to work directly in company operations. This had led several insurers to invest in internal technology and digital innovation initiatives as well as starting their own corporate venture capital funds to invest in InsurTech startups. More and more of the investors in InsurTech ventures are the investment arms of legacy insurers and reinsurers. Because of the lack of attractive standard alternatives, these investments may be the best options.

• Tempting target: The insurance industry is huge, with over a trillion dollars of net premiums written annually—over $500 billion in the P&C industry alone. Couple the size of the industry with a reputation for risk aversion and conservatism, and you have a tempting target for innovators, disruptors, and entrepreneurs.

Riding the data: How a transportation company used data science to improve decision-making processes

How can a company leverage customer data and turn it into actionable information? This was the challenge one transportation provider faced when its modeling system began underperforming after the company implemented it to predict revenue and passenger traffic. In this article, Milliman consultant Antoine Ly discusses how the firm created a machine-learning model that helps the company analyze various aspects of its ridership, leading to more informed financial decisions.

Here is an excerpt:

Working from a mock-up drafted by the client, the [Milliman] team reproduced the dashboard to the client’s specifications, but it is now supported by newly developed software as well as the client’s data warehouse. The dashboard allows the client’s management team to quire different aspects of passenger usage to gain insight into traffic flows and revenue. Colour-coded symbols, which when clicked on, give managers a concise picture of a train’s revenue and traffic. Managers can also quire the system based on selected features for both past usage and anticipated ridership, and are now able to make more informed decisions about pricing, the need for discounts or adjustments to marketing campaigns.

Because the model can adapt to new situations, deviations from the average error are confined to a much more narrow range. This means managers can have more confidence in the model’s predictive value and increases their ability to manage revenue.

Emerging risk analytics: Application of advanced analytics to the understanding of emerging risk

This report by Milliman’s Neil Cantle uses advanced machine learning algorithms, such as deep neural networks, to analyse social media conversations about Brexit. The purpose of the study was to examine whether useful information could be extracted from social media in what is effectively real time on a key topic in a political economy.

Validating machine-learning models

While machine-learning techniques can improve business processes, predict future outcomes, and save money, they also increase modeling risk because of their complex and opaque features. In this article, Milliman’s Jonathan Glowacki and Martin Reichhoff discuss how model validation techniques can mitigate the potential pitfalls of machine-learning algorithms.

Here is an excerpt:

An independent model validation carried out by knowledgeable professionals can mitigate the risks associated with new modeling techniques. In spite of the novelty of machine-learning techniques, there are several methods to safeguard against overfitting and other modeling flaws. The most important requirement for model validation is for the team performing the model validation to understand the algorithm. If the validator does not understand the theory and assumptions behind the model, then they are likely to not perform an effective model validation on the process. After demonstrating an understanding on the model theory, the following procedures are helpful in performing the validation.

Outcomes analysis refers to comparing modeled results to actual data. For advanced modeling techniques, outcomes analysis becomes a very simple yet useful approach to understanding model interactions and pitfalls. One way to understand model results is to simply plot the range of the independent variable against both the actual and predicted outcome along with the number of observations. This allows the user to visualize the univariate relationship within the model and understand if the model is overfitting to sparse data. To evaluate possible interactions, cross plots can also be created looking at results in two dimensions as opposed to a single dimension. Dimensionality beyond two dimensions becomes difficult to evaluate, but looking at simple interactions does provide an initial useful understanding of how the model behaves with independent variables….

…Cross-validation is a common strategy to help ensure that a model isn’t overfitting the sample data it’s being developed with. Cross-validation has been used to help ensure the integrity of other statistical methods in the past, and with the rising popularity of machine-learning techniques, it has become even more important. In cross-validation, a model is fitted using only a portion of the sample data. The model is then applied to the other portion of the data to test performance. Ideally, a model will perform equally well on both portions of the data. If it doesn’t, it’s likely that the model has been over fit.

Predictive analytics for the mortgage industry

How can predictive analytics help Government National Mortgage Association (GNMA/Ginnie Mae) issuers decide whether they want to buy out a nonperforming loan or not? In their article “Enhanced vision,” Milliman’s Jonathan Glowacki and Makho Mashoba provide perspective on an algorithm used to analyze loans that are likely to bounce back in order to reissue them as a mortgage-backed security.

Here is an excerpt:

The XGBoost model, like similar algorithms, is easy to implement. Once the mechanics of the technique are understood, and the parameters are tuned correctly, the model can be turned on a data set to produce accompanying predictions. The model can be updated continuously each month based on new data feeds. Pointing an XGBoost program toward a new data set and running it again is virtually all that is needed to refresh the results. It is also possible to retune the parameters for the update to further enhance the effects.

A use case of this type of model would be to pursue early buyouts for mortgages that have a high probability of re-performing and potentially not pursue early buyouts for mortgages that have a low probability of re-performing, as long as this policy is consistent with GNMA servicing guidelines.

This same technique can be used on a variety of data for alternative purposes. Predictive analytics can capture predictive power from internal data, whether that involves established and go-to data sets or whether that involves bringing together data from across an organization to make predictions. Predictive analytics can also help a firm leverage industry data and other outside sources to forecast trends or improve decisions. This case is a concrete example of how using the tool should result in higher return on investment on GNMA early buyouts.

Considering the growing amounts of data available, the mortgage industry should pay attention to predictive analytics tools. Investing in the technology has proven to generate significant returns. GNMA issuers is just one group to which predictive analytics can be applied. Predictive analytics can be applied to many other techniques and tools to increase efficiencies within the mortgage industry. The future depends on it.

Telematics and UBI: How to gauge ROI?

How can insurers understand the return on investment (ROI) of telematics and usage-based insurance (UBI) programs? The right program design can reduce costs and positively impact revenue. In Europe, some UBI programs market add-on services aligned to the needs of insurers’ customers which can generate revenue. In a blog post entitled “Making the business case: Telematics investment for UBI,” Milliman’s James Dodge offers an approach for insurers to consider.