near-miss Telematics in auto insurance

Near-miss Telematics in Motor Insurance: A New Approach to Safer Driving and Fairer Premiums

Motor insurance has long been based on assessing risks through claims frequency and severity. However, the advent of telematics technology offers a game-changing opportunity to refine this process. This article explores the integration of telematics data into motor insurance, focusing on near-miss events and their implications for dynamic insurance rating.

The Emergence of Near-miss Telematics

Near-miss events, often overlooked in traditional insurance models, are incidents that could have led to accidents but didn’t, like sudden braking or rapid acceleration. With telematics, these events can now be tracked and analyzed, offering a real-time insight into a driver’s behavior and risk profile.

A study titled “Near-miss telematics in motor insurance” (2021) by Guillén, Nielsen, and Pérez-Marín delves into this new approach. They propose a method that integrates these near-miss events into a pay-how-you-drive (PHYD) insurance pricing scheme. This method penalizes some near-miss events that indicate risky driving and adds charges to the insurance premium accordingly. Conversely, the absence of such events can lead to discounts, incentivizing safer driving habits.

Data Analysis and Methodology

The core of this approach lies in the analysis of telematics data, including speed, acceleration, braking, and cornering. Sun et al. (2021) in their study “Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression” have applied Poisson and negative binomial regression to telematics data to assess driving risk. This method helps in categorizing drivers into different risk levels and tailoring premiums accordingly.

Guillén et al. (2019) in their paper “Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?” analyzed a pilot sample of real usage-based insurance data. They found that factors like nighttime driving, urban driving, and speeding significantly influence the occurrence of near-miss events, which can be crucial for dynamic risk monitoring.

Real-World Implications and Challenges

The transition from traditional to near-miss telematics-based insurance presents several challenges. One of the primary concerns is consumer acceptance. How policyholders react to this new form of rating, where their premiums are directly influenced by their driving behavior, is yet to be seen in practice.

Moreover, the effectiveness of this model in actually reducing the frequency and severity of accidents is another area that requires empirical evidence. Studies like Dillon, Tinsley, and Cronin’s (2011) “Why Near‐Miss Events Can Decrease an Individual’s Protective Response to Hurricanes” provide insights into how near-miss events can alter risk perception, which could be extrapolated to driving behavior.

A significant case study in this domain was conducted by Meyers and Van Hoyweghen (2020), where they attempted to track the driving behavior of 5000 participants through smartphone sensors, offering a 20% discount on their premium. However, the study faced challenges in recruitment and could not establish a clear correlation between driving style and loss ratios. This highlights the practical difficulties in implementing such systems and the need for more comprehensive studies to validate the effectiveness of telematics-based insurance models.

Impact on Insurance Premiums and Driving Behavior

The application of near-miss telematics in determining insurance premiums has shown promise. For instance, Guillen et al. (2020) have explored how telematics can predict the risk of near-miss events. Their findings indicate that certain driving behaviors, such as hard braking and smartphone usage while driving, could increase the cost of insurance. This form of dynamic ratemaking, which includes penalizations for risky behaviors, provides an incentive for safer driving.

Moreover, Geyer et al. (2020) analyzed a dataset with detailed information on driving behavior, including speed, distance driven, and road type, in pay-as-you-drive (PAYD) contracts. Their study emphasizes the potential of telematics data in reducing information asymmetries and improving the accuracy of risk pooling. However, they also underscore the need for further research on loss frequency and severity in the context of telematics data.

Limitations and Future Directions

Despite the promising aspects of telematics in motor insurance, there are limitations to consider. As Guillen et al. (2020) point out, the prediction of claims would be more precise if they were recorded using only high-quality telematics data. Additionally, there is a lack of comprehensive data on the distance driven during the period when claims were made, which could impact the analysis.

The future of telematics in motor insurance is poised for growth, with an emphasis on further empirical research to understand the long-term impact of near-miss telematics ratemaking on driving behavior and claim frequencies. As telematics technology continues to evolve, it is likely to play a more significant role in motor insurance, offering more personalized and behavior-based insurance premiums. The transition from classical insurance rating to near-miss telematics ratemaking presents an opportunity to promote safer driving habits and more equitable insurance premiums, but it requires a careful and data-driven approach to realize its full potential.

As the insurance industry evolves with technology, the integration of telematics and near-miss data into rating models promises a more personalized, fair, and transparent approach to premium calculation. It also holds the potential for significant societal benefits by encouraging safer driving behaviors.

However, this transition requires careful consideration of various factors, including consumer behavior, data privacy, and regulatory challenges. Ongoing research and real-world trials will be essential in refining these models and realizing their full potential.  

References

1. Sun, S., Bi, J., Guillén, M., & Pérez-Marín, A. M. (2021). [Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression](https://www.semanticscholar.org/paper/a076d2df3ff5463a1f7d1e41a4ea9aced0251c1e).

2. Guillén, M., Nielsen, J., Pérez-Marín, A. M., & Elpidorou, V. (2019). [Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?](https://www.semanticscholar.org/paper/e1d8d9e48b3a758efd5d840a10685d742ac3973a).

3. Guillén, M., Nielsen, J., & Pérez-Marín, A. M. (2021). [Near‐miss telematics in motor insurance](https://www.semanticscholar.org/paper/e7585194d0c21297548deec27bd6f5191dba7306).

4. Dillon, R., Tinsley, C., & Cronin, M. (2011). [Why Near‐Miss Events Can Decrease an Individual’s Protective Response to Hurricanes](https://www.semanticscholar.org/paper/d96ecc35245f5fbfe059801c0b5adeff93bf7d71).