By Marty Ellingsworth
The use of predictive analytics is truly enhancing speed and customer experience at the service delivery points in the value chain. Understanding behavioral patterns enables insurers to optimize their risk evaluation, enrollment, and claims operations, as well as service feedback.
It is difficult to overstate the strides in efficiency being made by the insurance industry as a result of the effectiveness of predictive modeling tools and solutions. Vast quantities of data about people, places, and property have enabled increasingly better risk assessment, more accurate marketing efforts, better risk evaluation strategies, more efficient claim handling, and streamlined transaction processing across organizations. The next wave of advancements in predictive analytics will continue on this trajectory. Insurance professionals — from IT staff and data analysts to carrier executives — must also be more agile in their recognition, intuition, and application of novel information features.
Every segment of the insurance value chain is being affected by the use of predictive analytics. Many good ideas are coming from related industries.
For example, product development in the health insurance market has seen advances that help insurers identify patient risk patterns more accurately and provide incentives to consumers to take preventive measures.
In the workplace, insurers can now design policies based on statistics that predict which job functions present a greater risk of injury.
In the auto insurance market, predictive models are being used to develop products tailored to hundreds of customer types based on thousands of variables.
Basic medical bill review data is now permitting epidemiological studies for forecasting medical costs and inflation factors in a rolling time frame. This is far superior to standard historical trend development. Medical cost containment, utilization benchmarking, and large-loss case management processes help consumers maximize their insurance benefits. The benefit to insurers clearly has been increased efficiency and productivity.
The mutual benefits to both insureds and policyholders are that costs are more accurately defined and service is more focused. Customers who are enrolled for multiple products are generally more loyal and provide more return on marketing investments. Insurers, therefore, can use analytics to target or adjust their services to gratify and keep loyal customers. And sophisticated analytics are making precision underwriting a reality. In property/casualty product development, risk-based underwriting is flourishing with new tools that allow for segmentation in hundreds of ways, rather than in large class blocks.
In addition to efficiency, objectivity is also being improved with the deployment of analytics. That’s because decisions are being made in an empirically consistent framework as opposed to solely on human judgment. Given the variability in training, experience, workloads, and day-to-day individual judgments, predictive decision technologies provide the ability to measure complex combinations of variables, as well as to improve focus on the factors that create measurable value or positive change.
Predictive personal lines
In the marketing function, predictive analytics enable insurers to perform in-depth segmentation to determine customers more likely to respond to up-sell/cross-sell efforts, as well as those most likely to defect or those favored for a “win back” campaign. Such information lets insurers target marketing budgets toward customers that are more in line with their market objectives and risk portfolio. In addition, more customer communications are being directed at specific subgroups. These customer segments may receive educational information cards, preferred optional services, and other perks. Techniques are constantly evolving to make advertising and direct marketing more relevant and personalized.
Companies that have reinvented the traditional insurance model by marketing directly to customers exemplify the best practices in this area. Predictive models help these insurers get better returns from intimate customer relationships. Policyholders receive an optimal amount of information and offers that are more likely to meet their needs, as well as more appropriate incentives in exchange for their fidelity. As insureds transact and interact more often with a company, new behavioral data is added to refine the models and enable improvements to describe an insured’s true exposure.
A case in point is the value of personal property, where the actual replacement costs of an insured’s home can be substantially higher than the estimated replacement costs when the policy was initially secured. This gap in true and timely replacement value can be a significant risk to homeowners. Long-term owners are reducing their risks by making sure additions and improvements to their homes are included in the Coverage A policy information. By applying underwriting analytics to replacement costing, insurers are better able to mitigate the time-value effect of inflation for labor and materials. The latest breakthrough in meeting this challenge is address-specific value estimates that can pinpoint the policyholders likely needing attention, as opposed to the traditionally inefficient and costly method of contacting all insureds.
Enhancing speed, customer service
The use of predictive analytics is truly enhancing speed and customer experience at the service delivery points in the value chain. Understanding behavioral patterns enables insurers to optimize their risk evaluation, enrollment, and claims operations, as well as service feedback. Indeed, several transactional stages of the enrollment, policyholder, and renewal processes are now automated because insurers can more accurately assess the specific services that customers require; and they can create new self-service channels for insureds to fulfill their needs at their own pace.
Such advancements partly depend on insurers’ willingness to listen to customers and partly on the amount and quality of available data. Predictive analytics offers practical solutions because — as a science and an art — it applies empirical methods to vast quantities of data. While the statistics are complex, the models are based on the simple premise that more information about customers and their environment improves the approximation of that customer’s risk in an insurer’s book of business.
The sophistication of current tools enables companies to acquire, scrub, sort, and mine data with far greater efficiency than ever before. But today’s tools are also being fortified with emerging technology for the next wave of data scrutiny, building the foundation for predictive analytics 2.0.
Predictive analytics 2.0
In the near future, predictive analytics will incorporate rich media and multidimensional data. The insurance industry must adapt to increasingly sophisticated data and analysis to gain and hold a competitive advantage.
First, insurers must expand their IT capabilities to handle what will essentially be an explosion in the size of data stores required to house multidimensional data. Significant infrastructure upgrades may be required to enable data to be retrieved, coded, mined, and accessed by software applications that business analysts deploy. New toolsets will be required to handle and process the data, and predictive analytic models must be adjusted to account for real-time data and on-demand semantic and visual contexts.
Second, insurers will need to engage more closely with their distribution outlets and partners to align and share new, sophisticated information. Benefits of predictive analytics can be lost along the value chain if insurers do not effectively communicate market trends and structure their costs and compensation plans accordingly.
Producer compensation may be driven by elements of risk or other customer attributes. With a better understanding of the carriers’ policies and operating procedures, distributors will be able to optimize marketing and service efforts. This is critical to long-term distribution relationships, particularly given that point-of-service costs are roughly fixed in terms of the amount of time necessary to process applications, claims, and queries using today’s means. Soon, auto-fill, software-as-a-service, and single-entry data strategies will contribute to removing this burden from frontline administrators.
Third, and most important, insurers must invest in training and guidance for managers across all business functions to cope with this new information — both in structure and application. The “people” part of the “people, process, and technology” triad of enterprise productivity must be emphasized as well as underscored for successful implementation and execution to occur.
At the IT level, interpretation of changing business requirements may entail unforeseen processes for categorizing and querying compound data. “Up-skilling” is mandatory for technologists to attain the next level of sophistication. For those who analyze data to develop products, such multidimensional input presents great opportunities to further the understanding of the business environment and helps advance the gains in predicting outcomes.
Significant strides are being made. Analytics software can now comb through thousands of customer service calls in search of speech patterns that indicate dissatisfaction. Carriers can home in on that data to determine what is driving customers to call. Policies and procedures can then be put in place to correct issues and increase customer satisfaction. These types of initiatives present a tremendous opportunity for brand improvement and customer retention.
On the horizon
The potential benefits of predictive analytics 2.0 for insurers, distributors, and consumers are significant. Better analytics that incorporate new data points should continue to drive both lift and efficiency, translating into more proficient policy administration, improved cost estimation, smarter portfolio management, and better service delivery.
There is no doubt that predictive analytics 2.0 is on the horizon. Many of the leading practitioners in predictive analytics have been shepherding this trend into fruition over the past decade. The question is which insurers will best harness new data, modeling, and analytic solutions — at the technical, decision-making, and implementation levels — to provide superlative value to shareholders, employees, distributors, and policyholders.