Why predictive analytics is the secret weapon for P&C insurance

The insurance industry thrives on the measurability of potential outcomes, yet uncertainty remains a core aspect of this field. No one could have guessed, for example, that Hurricane Florence would have resulted in an enormous $45 billion worth of property damage. By the nature of the business, many variables affecting the volume and variety of claims seem to lie outside an insurer’s control.

But this assumption is rapidly changing as we enter into a golden age of insurance, an age that leverages emerging technologies to mitigate unpredictability.

When it comes to evaluating risk, determining quotes and managing claims, property and casualty (P&C) insurers have made major strides over the years, turning what was once partially a guessing game into a fully digital industry of meticulously calculated probabilities.

And more innovation is happening than ever now that P&C insurers have been armed with a new secret weapon: predictive analytics. This technology, coupled with automation and advanced data-mining techniques, will be at the core of every successful insurance business, where knowing what is coming next, or digital foresight, means customer security, safety and trust.

Here’s what insurers need to know when diving into predictive analytics, and reaching the full potential of the industry’s modern abilities:

Not all data is created equal

Data is the bread and butter of insurance portfolios. Quantitative and qualitative alike, data sets provide the basis for calculating risk and determining price when it comes to policies and claims. Information on how much we consume, how fast we drive, the neighborhood where we live, and average weather conditions are all important in identifying variables of risk and ultimately ensuring business success. A large quantity of data, however, does not necessarily reflect the caliber of that information.

Before integrating predictive analytics into an organization, it is essential to determine exactly which data sets drive decisions when it comes to portfolio management and apply modern tools to extract that knowledge as efficiently and accurately as possible.

The Internet of Things (IoT) is just one of the technologies that has upended the property and casualty space by collecting hyper-accurate, real-time data via sensors in the field. This method of gathering data requires little user interaction, streamlining research processes across insurance companies and allowing them to move more nimbly than ever. By creating a live feed of data points, IoT sensors can even catch anomalies and prevent claims before they happen. The enhanced quality of this information acts as the foundation for the successful application of predictive analytics, leading to more preventative and dependable decisions down the road.

Predictive equals proactive and personalized

Once the right data has been collected, insurers must seek to understand what that information means for operations today, tomorrow, and even five years down the line. The goal of predictive analytics is to transform insurance from an industry that’s defined by reactive action — such as responses to incoming claims and natural disasters — to one that has proactivity at its core. This is achieved by applying machine learning and AI to data sets which distill and interpret the information, spitting out actionable insights on what the future holds in terms of incoming claims, and the best way to meet those challenges for the insurer, and the customer.

Comprehensive, predictive models that combine both historical and incoming sensor data predict claims based on factors like geography and can even narrow predictions down to individual customers. This tailored forecasting approach creates the opportunity for highly personalized service and pricing. In this way, predictive analytics powered by machine learning technology increase the accuracy of individual projections and help establish a more precise understanding at not only the customer level, but also bigger trends by extracting patterns from the data on a larger scale.

Insurance should feel like assurance

Data and predictive analytics do not matter unless they are used to achieve real world results. Whether in property and casualty, life and annuities, reinsurance or large commercial sub verticals of insurance, greater efficiency, savings, and higher customer satisfaction is the end goal of all innovation. The onset of predictive technologies coupled with automation has proven to achieve these goals, providing a strong sense of assurance and trust for insurers and their customers alike.

Automating processes based on the actionable insights from predictive analytics establishes stable business practices for insurance providers. Tasks like policy endorsements, renewal, issuance, premium rejection, underwriting support, compliance checks, and claims bulk payment processing can be completed by automation, cutting down on time and directly impacting on the bottom line. Automation can also harness predictive insights to take on complex decisions, accessing more than one system to complete a process — resulting increased revenues and confidence.

Predictive analytics is the future of the insurance industry. This technology, fueled by the right data, gives insurance companies the power to cope with volume fluctuations, transition from reactive to proactive action, personalize service, and remain competitive in an increasingly cutthroat environment. With storms like Harvey and Florence resulting in record amounts of destruction, insurers must lean on emerging technologies to bring in process efficiencies and ensure business growth.

Now is the time for change.

Anurag Chauhan is executive vice president and global head of Insurance at NIIT Technologies, a global IT solutions provider based in Princeton, N.J.. He can be reached via LinkedIn.

These opinions are the author’s own.

See also: 5 ways AI and data are transforming the insurance space