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Three Trends for Insurance Technology Professionals
By Kevin Bingham, Principal, Deloitte Consulting and Dave Schmitz, Director, Deloitte Consulting
Core System Transformation
Insurance companies across the country are implementing new policy and claims administration systems to create a stronger data/business intelligence foundation. By improving their policy administration systems, organizations are enhancing the way they design and maintain products, underwrite, interact with producers, sell to, and ultimately service their policyholders. If done correctly, the transformation can have a positive impact on policyholder, business user, and producer satisfaction. At the same time, increasing use of third party data and analytics can improve straight-through processing and underwriting results.
By introducing design efficiencies into the claims lifecycle, from intake to adjudication, organizations are improving their ability to meet regulatory requirements, reduce expenses, improve customer service, and streamline the overall claims processing framework. Similar to policy administration, the benefits of a successful claims transformation include improved operating efficiency and better claims outcomes by leveraging big data and enhanced business rules.
With these types of transformation efforts taking multiple years, CIOs and information technology professionals are a critical component of streamlining the technology integration and achieving the desired ROI. In addition to guiding the selection of a solution that supports the business transformation objectives, IT can help guide the user community to reduce both configuration and customization by focusing on the key drivers of business value. Just as important is understanding how to best capture, manage, leverage, and provision data within the core systems to enable the analytics and decision making that are critical to profitable underwriting, efficient operations, and enhanced customer experience.
Leveraging Big Data Technologies
The convergence of big data, advanced analytics, and an evolving understanding of habits and how to change human behavior through data enhanced “nudges” is quickly changing the world we live in. For insurance, underwriting predictive models have been used successfully for almost two decades. Personal lines, followed closely by the commercial lines of business, recognized the power of leveraging internal and external data to do a better job of segmenting and pricing risks. Less than a decade ago, insurance companies began using predictive models to attack the claim side of the equation. Running models at first notice of loss, insurers can attack recognized and unrecognized severity by aligning claim complexity with the most appropriate resources as early as possible.
Today, insurance companies are attacking all types of challenges with big data and analytics: price/demand optimization, web optimization, agent segmentation, excessive opioid use, premium audit modeling, workforce intelligence, and more.
Today, insurance companies are attacking all types of challenges with big data and analytics
But as some insurance companies found out the hard way, IT involvement is a critical component of developing models, building a scoring engine, and successfully implementing the models in a production environment. Data scientists may find a way to access internal and external data, but without the help of IT, it is almost impossible to bring the models to life in the day-to-day operation of the insurance company.
Innovative Driven Competition
The world we live in is changing faster than ever before, and the insurance industry is no exception. Telematics, ride share programs, smart buildings, smart cars, driverless cars, competition from alternative capital, wearable medical technology, competition from non-insurance companies, drones, cyber risk exposure, changing workforce demographics, smart robots, water shortages, and catastrophes.
Using telematics as an example, insurance companies are capturing and analyzing information such as miles driven, acceleration, braking, cornering, excessive speed, road type, day of week, time of day, weather conditions to do a better job of setting prices.
More importantly, data driven features available on apps are allowing drivers to see how well they are performing in categories such as cornering, braking, and speeding habits. Leveraging this type of post trip feedback, insurance companies have evolved beyond pricing to providing insurance customers with real-time risk management feedback. For a parent of a 17 year-old driver, these types of statistics could help save a child’s life.
Staying with a personal automobile example, what happens to premium volume as cars become smarter and accidents drop significantly from current levels? On the flip side, what happens to premium volume if bad actors hack into vehicles and cause accidents? What happens to auto insurance agents if internet companies aggressively sell insurance products?
In the news, we see a number of articles discussing the topic of humans being replaced by robots. For some of the largest contract manufacturers, the eventual impact could equate to millions of human workers being replaced as robot installations expand across the globe. In the Wired.com article titled Better Than Human: Why Robots Will—And Must—Take ourJobs, the author notes, “It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation.” For workers’ compensation insurers, that sure represents a lot of lost premiums.
From telematics to game changing line of business trends, IT professionals will need to pay attention to the major shifts in the collection of data with high volume, variety and velocity (e.g., smart phones, GPS devices, cameras, sensors, unstructured text, photographic data).The ability to collect, store, manage and analyze data on a PDA or laptop on demand will be IT’s next big adventure.
In the Forbes article titled Data Scientists: The Definition of Sexy, Gil Press stated: “A data scientist is an engineer who employs the scientific method and applies data-discovery tools to find new insights in data. The scientific method—the formulation of a hypothesis, the testing, the careful design of experiments, the verification by others—is something they take from their knowledge of statistics and their training in scientific disciplines. The application (and tweaking) of tools comes from their engineering, or more specifically, computer science and programming background. The best data scientists are product and process innovators and sometimes, developers of new data-discovery tools.” Although the term data scientist is getting a ton of play in the media, it is the information technologists that are the backbone of addressing the three trends we have identified. In the end, without careful planning and execution by IT professionals, the vision defined by the data scientists might end up on the shelf, never being effectively deployed into core insurance operations with the desired impact on the top and bottom lines.