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How Machine Vision can Revolutionize Motor Insurance

Himanshu Saraf
3 minutes, 49 seconds read

The motor insurance market in India is approximately Rs 70,000 crore in terms of Gross Written Premiums. With newer and stricter regulations more and more people are buying motor insurance. However, while motor insurance, in general, has grown by 16% over the last year, the new digital insurers in the marketplace have seen their premiums increase by 4X-6X. 

This underlines a shift in the way customers choose to buy motor insurance – from the convenience of their smartphone or computer, instantly. There is no reason to think that they would not want to settle an insurance claim in the same convenient manner. Fortunately, machine vision technology solves claims settlement challenges to a great extent.

Current Claims Process

Let us have a quick look at the current claim settlement process for motor insurance. Once the accident occurs, the insured has to follow the following steps:

  1. The insured informs the insurance company about the accident. Subsequently, the insured files a physical claim along with the required documents such as RC, DL, insurance policy, bills, receipts, etc.
  2. A surveyor gets assigned by the insurance company to examine the damaged vehicle. 
  3. The surveyor ascertains the reason and the extent of the loss. After this, the insurer sends an approval/rejection of the claim/amount.

The above process is not only time consuming and stressful for the insured but also expensive for the insurer due to physical inspection and other manual checks and balances. The higher cost of processing the claim makes business less profitable to the insurer. The inconvenience and long wait make the product less desirable to the customer.

As more and more people buy motor insurance online, the customer expectation from the claim settlement process is changing as well. Customers now expect a seamless digital claim settlement process preferably in a matter of hours if not minutes, instead of the present industry standard of several days.

A Machine Vision Solution to Instant Claims Processing: FlowMagic

We at FlowMagic set out to solve this problem both for the insured and insurer using the power of artificial intelligence. We have used machine vision to eliminate the need for the surveyor in all but the most complex cases. 

Using machine vision, we can process a car image and identify not only the damaged parts but also the severity of damage to those parts and whether it requires repair or a replacement. We have further analyzed repair cost data and images from tens of thousands of accident cases to build an Artificial Intelligence Costing Model that can estimate the cost of repairing any part just by looking at its photograph. All this means that the insurer doesn’t need the surveyor and other manual checks in most cases and the customer can submit a claim from the convenience of his smartphone and get an approval decision within minutes.

New Claims Settlement Process with FlowMagic

  1. After the accident, the customer clicks photographs of damaged parts of the car and uploads them on the app along with a photo of DL/RC.
  2. The AI model verifies the DL/RC information and estimates the extent of damage to the car and whether the damaged parts need to be replaced or repaired. The model further calculates the cost of repair and/or replacement and informs the customer/insurance company.
  3. Based on the outcome of the DL/RC verification and the repair estimate the claim is either auto-approved in minutes or forwarded to a claims adjuster for review.

All the stakeholders in the insurance value chain can use our solution and benefit from it.

Insurance Company: By integrating this solution with mobile applications, Insurance companies can get quick claims intimations and a reasonable estimate of the repair cost. The damage severity analysis also helps the insurance company negotiate with the garage on whether a part needs repair or replacement.

Service Center or Garage: Multi-brand garages or service centers can quickly assess the level of damage to any car brought to them through machine vision-based FlowMagic. Accordingly, they can send a quick quotation to the insurance companies. The insurance companies can trust this quotation as it is generated by a robust AI model.

End Customer: An end customer can also use our free mobile application to get a repair estimate. This can be a starting point for an informed negotiation with a garage.

To learn more about how FlowMagic can transform the way you settle your motor insurance claims or discuss your broader AI goals, please get in touch with us at hello@mantralabsglobal.com 

Also read – How AI can settle insurance claims in less than 5 minutes!

About author: Himanshu Saraf is a Capital Markets Director at Mantra Labs. He also leads Artificial Intelligence (AI) and Machine Learning initiatives in the company.

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Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. 

92% of the C-Level Executives are increasing their pace of investment in big data and AI.

NewVantage Partners Executive Survey 2019 

Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.

AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.

However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.

5 Challenges for AI implementation for Insurers

5 AI Implementation Challenges in Insurance

Lack of Quality training data

AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

Clean vision, Process, and Support from Executive Leadership

AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.

Data in-silos

Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals. 

Technology & Vendor selection

AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.

People, Expertise and Technical competency

‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.

Still playing catch-up to the US, China, and Japan — India has doubled its AI  workforce over the past few years to nearly 72,000 skilled professionals in 2019. 

Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?

What is the right approach?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.

Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs). 

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Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability. 

Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)

The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.  

Actuaries work wherever risk is present

Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios. 

Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.

Risk-Uncertainty Continuum

Source: Sam Gutterman, IAA Risk Book

Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office. 

Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.

Also read – Are Predictive Journeys moving beyond the hype?

Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.

New Product Development in Insurance

Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.

Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.

There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.

Dynamic Factors Influencing New Product Development in Insurance

Source: Deloitte Insights

To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data. 

Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring. 

This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.

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