View the source here. By: John Drzik
Managing risk in real time offers the potential to both reduce risk and transfer it more effectively. It means businesses can have an up-to-the-minute view of their changing risk exposures—and take actions to mitigate them—and that the insurance industry no longer needs to rely only on historical data to price risk.
What makes real-time risk management possible? Three concurrent developments are starting to reshape the risk landscape:
New real-time data streams. From telematics to satellite imagery to wearable technology to property sensors, there are a growing number of emerging technologies generating new data streams that provide dynamic signals with risk content. Mobile phones are also a growing source of risk signals, especially as more and more of them are run on high-speed wireless networks. By 2025, the world will have 1.2 billion 5G connections, and 4G will reach 5 billion connections, according to the GSM Association, a global trade association of mobile telecommunication operators. This means the majority of the global population will have access to the advanced wireless networks that can power real-time data streaming.
Analytics driven by artificial intelligence. Advances in AI and machine learning now enable the processing of large-scale data streams at a speed significantly faster than previously possible. AI-powered analytics can distill the expanding set of real-time data signals into a dynamic view of risk that can be used to trigger mitigating actions or consideration of risk transfer alternatives.
New insurance products. New policies that adjust price or coverage in relation to changing risk signals are creating incentives to manage risk more actively. The most developed area is personal auto insurance, where some policies now provide premium credits in relation to telematics-based information on driving behavior. While still embryonic, innovative insurers are exploring the potential to create next-generation policies in other property and casualty areas that use new data streams to adjust price or coverage dynamically and that also use real-time streams to process claims more rapidly.
Enabling Better Risk Assessment
Advances that would have seemed like science fiction barely a decade ago are reality today. Consider just a few examples of emerging technology that are contributing to real-time or near real-time assessment of risk.
Telematics. From passenger cars to trucks to cargo ships, telematics are being deployed to improve transportation safety by actively identifying risky driving behaviors and conditions. Accident rates could be reduced further if insurance products provided price incentives for individuals and businesses to use the telematics feeds to manage their risk more actively. Commercial insurance policies can be developed to reprice motor or marine cargo insurance in real time based on the behavior of the operator, the roads or seas on which the cargo is traveling, the value of the cargo, weather conditions, and other dynamic variables. Fully autonomous vehicles have the potential to create a step-change decrease in risk—and the use of the autonomous features can be encouraged through an insurance policy that shifts in price based on a real-time feed signaling whether the autonomous capabilities are on or off.
The Internet of Things. Current projections are that 25 to 30 billion connected devices will be deployed by 2020 (up from more than 7 billion today). From embedded sensors that enable “smart” buildings or “smart” homes, to wearables used on construction sites or manufacturing operations, connected devices generate alerts that can warn users of unsafe conditions and trigger them to change their behavior, perform maintenance or take other actions that help to prevent accidents. Until 1986, canaries alerted coal miners to the presence of deadly fumes. Environmental sensors perform that task today in real time, improving safety above and below ground. Connected devices are often installed for reasons other than risk management—for example, property sensors might be implemented to improve energy efficiency, and wearables might be deployed to improve productivity. However, the same sensors often carry risk content that can be used to improve risk mitigation or transfer.
Cybersecurity technology. Cyber risk continues to escalate, and an expanded set of technologies is being deployed within businesses to help their information security professionals prevent or respond more effectively to cyberattacks. Many of these new technologies generate data streams that can also be processed with advanced analytics into a moving view of cyber exposure. Risk professionals can then use these views to quantify potential loss scenarios more actively and consider the economics of risk transfer with greater precision.
Visual intelligence tools. Satellites, aircraft and drones are capable of deploying high-resolution cameras and sensors that provide additional real-time data streams. More powerful machine learning techniques can now process these images into relevant real-time risk information. For example, the combination of property images and high-frequency weather feeds can provide a rapid and accurate view of property damage, which can be used for high-speed claims processing. The same visual intelligence technology, combined with property IoT data and AI-based weather forecasts, can provide a forward-looking view of property risk for a home or building that can be used in insurance pricing.
The Road Ahead
Real-time risk management technologies will not eliminate risk, but can increasingly provide businesses and individuals with actionable intelligence to manage and reduce their risk—significantly, in some cases. The advent of new data streams and powerful analytics also opens the door to innovation in risk financing, whether that takes the form of innovative coverage from insurance providers, an expanded and creative use of a captive, or tapping new products from alternative capital markets.
Insurance is likely to remain the primary risk transfer vehicle for these risks. Traditionally, insurance premiums are determined based on historical data. Underwriters and actuaries compile and use past data sets to look for loss patterns and make projections about future outcomes. Emerging data sources can now be leveraged to provide a continuously updated view of the underlying risk. Insurance providers can use this real-time data and evolving data science to make more dynamic projections about future outcomes and develop risk-based premiums that are calculated based on the new approach.
To date, risks traditionally covered by insurance could not be managed this dynamically. However, the prospect for real-time risk management is now on the horizon. By harnessing real-time data streams and analytics driven by artificial intelligence, businesses and the insurance industry can dramatically shift how we manage risk and think about insurance. We’ve barely scratched the surface, but the potential benefits for identifying, assessing and managing risk in real time are already coming into view.