AI’s Influence on Vehicle Insurance Risk
The automotive insurance industry is undergoing a transformation driven by the increasing application of artificial intelligence (AI) and machine learning. Insurers are leveraging these technologies to refine risk assessment practices, potentially leading to more accurate pricing and improved customer experiences.
AI’s Advantages
Doug McElhaney, a partner in McKinsey & Company’s insurance practice, highlights the importance of both AI algorithms and the comprehensive data incorporated into them. “Advanced, non-linear algorithms can identify at a more granular level that may be missed by that rudimentary algorithm, all of AI is designed to replicate human cognition to some degree,” he explains. By utilizing a more expansive dataset along with advanced algorithms, insurance companies can better differentiate levels of risk.
The Role of Telematics
Telematics-based insurance products provide access to substantial data volumes, including driving behavior insights. These data points, captured from the vehicle itself or a mobile device, can identify driving patterns like rapid turns or hard braking. McElhaney notes that telematics offers insurers increased confidence, transitioning from static rating to usage-based insurance that captures critical driving habits.
Challenges and Opportunities
Roman Swoszowski, vice-president of AI and cloud research and development at Grape Up, points out that handling and analyzing data poses challenges and risks, particularly as automakers may leverage their data advantages to enter the insurance market or establish partnerships. He adds that even with the risks, the integration of data-driven technologies potentially lead to improved vehicle safety and efficiency.
Expanding Data Sources
Insurance companies are tapping into a growing variety of data sources. Beyond telematics data, this includes traffic patterns and accident information tied to specific locations such as intersections. Matthew Carrier, a principal at Deloitte Consulting, emphasizes the benefit of such information in refining risk assessment and policy pricing. Post-accident data, such as camera video and telematics data, can also optimize the claims process.
Detecting Fraud and Ethical Considerations
AI and machine learning are improving the detection of fraudulent insurance claims and purchases, considering factors like annual mileage estimations and vehicle storage location. “The efforts by insurance companies to reduce fraud ultimately benefits consumers by lowering the cost of insurance policies,” says Carrier.
Ethical considerations are critical in the use of consumer data. These include data confidentiality, privacy concerns, bias, and infringement related to consumer data. Ensuring that the consumer data used in risk assessments is accurate, appropriate, and collated consistently, while also observing relevant consumer protection laws and regulations is vital. Careful consideration must be given to guard against the use of data leading to biases.
