Four Ways to Shape a Digital-Forward Auto Insurance Ecosystem

The changing landscape of auto insurance.

In the last few years, the automobile industry has witnessed major shifts: customers preferring vehicle renting and sharing over ownership, autonomous vehicles prompting us to rethink road safety, and OEMs leveraging telematics to enable connected, data-driven vehicles. As much as this digital transformation has redefined the automotive experience, it has also changed automotive insurance as we know.

The rise of InsureTech – an industry estimated to grow at a CAGR of 51.7% from 2022 to 2030 ^[1] – has already shown that the traditionally manual, difficult-to-automate insurance sector can support the coverage needs of the healthcare, real estate, and automotive sectors by introducing innovation to its long-drawn, paperwork-intensive workflows. Now, as insurers leverage intelligent capabilities to shift to a proactive approach, the future of automotive insurance is set to see better risk assessment, loss prevention, and more.

Here are key capabilities that AI can help unlock in automotive insurance space:

1. Personalized Auto Coverages
2. Precision Risk Scoring
3. Faster Claims Processing
4. Improved Fraud Detection

1. Personalized auto coverages:

To ensure consistency in policy documents with standardized clauses, the idea of offering personalized auto insurance has been long in the works. Now, with the emergence of new business models such as pay-as-you-drive and pay-as-how-you-drive ^[2], usage-based insurance (UBI) could soon become the touchstone for this sector. Enabling this change is AI-enabled telematics, which is capturing real-time data from sensors on cars’ on-board diagnostics (OBD) systems, along with driver behaviour, and traffic conditions.

Here, the biggest benefit for auto insurers is that they get to offer customized policies that suit the unique needs of different customers. With alternative vehicle ownership/use models gaining prominence and customers looking for alternatives to flat rate insurance covers, UBI enabled by telematics can allow insurers to frame contracts based on different driving parameters such as vehicle speed, acceleration, braking, miles covered, and driving hour, for each customer.

Along these lines, a global American automaker has tied up with a prominent insurance provider to offer its customers UBI plans with premiums adjusted based on their vehicle mileage and driving behaviour, leveraging data tapped through connected automobiles ^[3]. Overall, UBI ensures that policyholders pay in accord with their driving habits and travel requirements, while auto insurers get to improve customer experience, lower risk, and achieve increased profit margins.

2. Precision risk scoring:

Risk scoring or underwriting is where auto insurers quantify the risk related to potential vehicle damage. However, underwriting complex insurance scenarios – such as in UBI – can’t be trusted to traditional risk engines and formula-based assessments. To this end, IoT and analytics-driven Geographic Information System (GIS) can help insurers collect and analyse varied data, such as value of the insured vehicle, driver’s driving record, and driving behaviour, to assess potential damage with increased accuracy.

What’s more, automobile risk assessments could be further improved by adding automation to the equation. RPA (Robotic Process Automation) enables insurers to continuously update a customer’s risk score based on real-time vehicle and driving data collected by telematic devices. For instance, a major American car insurance company leverages an automated underwriting system, which is helping ‘good drivers’ save up to 52% on their automobile insurance as their driving becomes ‘safer’ ^[4]. As vehicles get more connected and insurers become digitally mature, automobile underwriters will soon be able to benchmark risk levels against client coverages with pinpoint precision.

3. Faster claims processing:

Auto claims processing consists of several stages, including claim evaluation, vehicle investigation, adjustment, and finally, remittance or denial. Considering that this process is quite paper-intensive, insurers need to go through numerous documents for each stage mentioned above. Here, NLP-powered document automation can enable insurers to scan through volumes of insurance contracts to extract key data and check clauses and claims for each policy document.

Another sticking point in faster claims processing is timely, error-free damage assessment. For auto insurers, mobile-friendly computer vision apps can ensure real-time damage reporting and evaluation by scanning images of accidents, helping create repair estimates, and enabling faster claims processing. Machine vision-powered apps enable customers to scan photos of damaged vehicles from their mobiles, documenting the breadth of information required, followed by the AI performing a real-time damage assessment that helps insurers quickly decide whether a car can be fixed and what repairs are required. This was seen in the case of a major US-based insurer which has integrated computer vision in its claims processing workflow to speed up its vehicular damage appraisal, a move that can save adjusters potentially up to 3,60,000 hours an year ^[5]. Helping settle claims quickly, these technologies will not only help boost customer confidence but also reduce adjuster costs for auto insurers, which often make up a major part of claims expenditure ^[6].

4. Improved fraud detection:

Along with insurers, mobility providers such as auto rental firms also see cases of fraud as customers often conceal vehicle damage(s) during returns. In the US alone, insurers lose nearly $40 billion per year against fraudulent claims ^[7]. These include anything from staged accidents and inflated damage costs to claiming for forged parts and even vehicle dumping. Auto insurance, just like other insurance segments, is vulnerable to claim offenders who are hard to detect when employing traditional, rule-based methods. But all this is changing thanks to predictive analytics and AI-powered Natural Language Processing (NLP).

With quality training data, ML algorithms can detect, manage, and report fraud by recognizing behavioural patterns among auto claimants, based on common parameters such as claim history, criminal record, bankruptcies, and automobile records involving data on vehicles sold, purchased, and transaction sources. Add NLP to the mix, and auto insurers can take a proactive approach to dealing with fraud. By applying text and voice analytics, NLP can helps analyze statements made by previous fraudsters and allows for this data to be utilized by predictive analytics systems, pre-emptively raising alerts on potentially fraudulent auto claims.

Additionally, all of this could be further augmented by utilizing document automation to interpret fraudulent case studies, and thereby build self-service knowledge bases for assessors to better gauge the possibility of a claim being a case of fraud.

What does the future cover?

The global AI in auto insurance market was worth USD 221.94 million in 2020 and is expected to grow to USD 582.41 million by 2028, growing with CAGR of 14.06% ^[8]. With both the automotive and insurance industries adopting AI and related technologies at a massive scale, auto insurance is set to become more personalized, proactive, and predictive in terms of coverage, contracts, and claims processing. With the quantum of behind-the-wheels data that auto insurers have within reach, the way ahead involves investing in product innovation, partnering with telematics solution providers, and employing the right set of analytics and storage tools to tap into this new wealth of data. This, in turn, will enable auto insurers to minimize costs, optimize workflows and their efficiency, and build resilience to meet highly individualized customer expectations, while also increasing profitability.