by James Cowen – Chief Commercial Officer (CCO) at Humn.ai
Technology and insurance are not two terms that go hand-in-hand. We are seeing the emergence of AI-driven insurance in insurtech platforms, such as Humn, but there is a huge knowledge gap that exists between the insurers, who have been doing things a certain way for centuries, and those they insure, who are generating vast amounts of high-quality data that is going unused. As the CCO of an insurance technology company, I have witnessed firsthand how AI can, and should, be transforming the insurance industry for better. Data science has a huge role to play in understanding risk exposure, and how insurers approach underwriting and risk management.
Traditional insurance is broken
While the traditional insurance model has served the industry well for many years, it has become increasingly apparent that there is room for improvement. The underwriting process, which often relies on manual tasks and annual premium assessments, can be time-consuming and inefficient. This approach may lead to risk assessments that are based on historical data, claims history, and general assumptions about risk, which might not fully capture the nuances of modern risk factors. As a result, pricing may not always be as accurate as desired, resulting in increased exposure to unexpected losses.
The claims process is no better, and can be lengthy and cumbersome, as it often requires manual verification, paperwork, and communication between multiple parties. This can result in delays in claim resolution and increased administrative costs which, aside from affecting the bottom line, can be very damaging to customer satisfaction and retention. Speaking of customers, many insurers are also not utilising the full potential of customer data, which means that most traditional insurance products are generic and offer very little in terms of personalisation.
The AI opportunity
At Humn, we focus on fleet insurance, and the traditional underwriting process typically relies on historic data from claims history, fleet composition, make, and model. However, with AI, we can now price insurance dynamically by trip, taking into account the risk associated with each trip. Vehicles generate a wealth of data, which can help in this process, allowing us to adapt to new forms of mobility such as rideshare. Insurance is catching up to mobility, shifting from per-mile rates to more advanced, dynamic pricing models that account for hundreds, if not thousands, of additional risk factors.
AI can enhance risk assessment accuracy by continuously refining models using newly acquired data, allowing for a more precise evaluation of risk exposure. As more data is gathered, models become increasingly accurate, which in turn enables insurance providers to reward drivers and fleets for safer driving practices. This improved transparency and control over premiums foster a more equitable and incentivising system for policyholders, encouraging better driving habits and overall safety. AI can also assist in detecting and preventing fraudulent insurance claims. Speed is crucial in staying ahead of fraudulent claims, and AI can help reduce the cost of claims and the cost of doing business by quickly notifying insurers of potential fraud.
The cost-saving benefits also extend to predicting risk. AI allows insurers to personalise insurance products for individual customers (which could be entire fleets or single policy holders) and continually improve pricing risk. AI can also assist in predicting and managing risks in the insurance industry by continuously evaluating and learning patterns. This allows insurtechs, such as Humn, to personalise risk for fleet managers and identify potential issues before they become problematic. The holistic view is to ultimately reduce the number of overall claims, and the claim costs, meaning premiums can be lowered while ensuring margins stay intact.
The hope is that AI will continue to evolve and transform the insurance industry, and give those that use AI-driven data a competitive edge over other insurers. The dynamic pricing models that we see today are just the beginning. As AI technology becomes more sophisticated, insurers will be able to better understand risk and adapt to the ever-changing landscape of mobility; some insurers are still unsure about insuring new vehicle models as they lack the claims data and historical information to confidently price these risks.
Of course, there are challenges, and traditional insurance companies may be hesitant to adopt AI due to various reasons, such as the need for staff training and a shift in mindset. Implementing AI technology requires significant changes in the way employees approach their work, moving from traditional methods of collecting their own data (almost acting as data scientists) to being guided by data-driven insights collected by AI. Staff training can be time-consuming and costly, and employees may be resistant to changing long-standing practices. Additionally, adopting AI often requires investment in new infrastructure, such as hardware, software, and data storage solutions, which can be a substantial financial commitment, especially for smaller insurance companies with limited resources.
Integration with legacy systems can pose another challenge, as many traditional insurance companies have complex, long-established systems in place. Integrating AI technology with these systems may require considerable investments in both time and resources. Finally, and perhaps the biggest challenge, is the fear that the adoption of AI could lead to more accurate pricing and risk assessment, resulting in reduced premiums and profit margins.
There is immense potential for AI to transform the insurance industry, and create more efficient processes that are beneficial for all parties. Leveraging AI-driven data analysis and technology can allow insurers to improve risk assessment, enhance customer experiences, and maintain competitive margins by reducing risk exposure and claim payouts. While challenges do exist in adopting AI, embracing these innovative technologies is crucial for insurers to stay ahead of the curve and adapt to the rapidly changing landscape of mobility. Harnessing the power of AI, insurance companies can revolutionise their processes and create a more efficient, customer-centric industry.