Is it time for a shift towards pay-how-you-drive models?

The Indian motor insurance industry is experiencing a technological revolution, where data innovations are disrupting traditional, comprehensive insurance models, driven by an increasing consumer demand for personalised solutions. As digital advancement is reshaping the landscape, the Indian motor insurance market is anticipated to grow from $13.19 billion in 2025 to $21.48 billion by 2030 with a CAGR of 10.25% during the period.

At the forefront of this shift is the ‘pay-how-you-drive’ (PHYD) model, which uses telematics technology to offer premiums based on individual driving behaviour. With the global rise of usage-based insurance, the question remains: is India ready to embrace this more personalised approach to motor insurance? 

The industry is deemed to move towards personalised data-driven solutions that will care for what people want in their insurance. Global trends show a tremendous push toward usage-based insurance (UBI). The global UBI market is projected to be near $150 billion by 2027, growing by over 25% yearly, created by advancements in telematics and a myriad of consumer preferences for personal insurance solutions. India is on the cusp of early adoption of PHYD, with the top insurers bringing forth products that reward safe driving.

Limitations of traditional motor insurance

In India, motor insurance premiums have been fixed on broad parameters like a vehicle’s make, model, engine capacity, location, and driver’s demographic profile. Although this method simplifies underwriting, it lacks granularity for assessing individual risk. As such, safe drivers generally end up paying as much as high-risk drivers. Traditional insurance models also fail to incentivise safe driving behaviour as it makes no distinction in the premium according to a person’s driving style, thereby giving no direct financial incentive for an individual’s driving habits. 

Rise of PHYD Insurance

Unlike traditional motor insurance, PHYD insurance departs from a fixed charging structure to a dynamic pricing model, where premiums are decided based on real-time driving behaviour. Telematics technology, embedded within mobile apps or installed in the automobile through a plug-in device, provides insight into key driving parameters like speed, acceleration, braking patterns, cornering, and total miles. When the telematics data is analysed, a score for driving behaviour is considered, which further influences the premium charged to the insured.

Adoption of PHYD in India

Telematics-driven solutions have been incorporated into several Indian insurance solutions and recognised for their potential by some insurance providers. The safer the policyholder’s driving habits, the more premium discounts they receive. In addition to pricing, PHYD provides feedback to customers about their driving habits. Drivers can consciously improve their scores to lower premiums while contributing to road safety. Gamification is another tool adopted by insurers that rewards better driving behaviour. This has resulted in favourable outcomes in terms of changing behaviour that lowers accident chances.

Benefits

Aligning premiums to the actual risk will enable insurers to price policies much more accurately, thus reducing adverse selection and increasing system sustainability. Policyholders, especially those who drive safely, will not bear the cost of high-risk drivers. Likewise, there are significant implications for road safety. Given India’s high road accident rate, incentivising safer driving could lead to a substantial decline in casualties and fatalities.

Challenges in adoption

The adoption of PHYD in India holds tremendous potential, though it comes with a few challenges. Addressing data privacy concerns, enhancing telematics infrastructure, and alleviating consumer apprehension about continuous monitoring are key steps. By investing in consumer education about the benefits of PHYD, insurers can significantly boost their adoption and pave the way for a more innovative and efficient insurance landscape.