Businesses that ensure protection from financial losses and mitigate risk are as old as human civilization. The Code of Hammurabi, King of Babylon, written in 1750-1755 BC, specified the first provision of what we now know as marine insurance. In the wake of the Great Fire of London in 1666, Sir Christopher Wren included the provision of an “insurance office” in his new plan for the city of London. Today, the global insurance market is estimated to be a $ 5,550,310 million industry consisting of major commercial life insurance, non-life insurance, health insurance and health insurance companies.

Against this backdrop, a new wave of “InsurTech” solution companies is exploring transformation of the insurance business through the introduction of big data, machine learning and AI capabilities. In a recent Harvard Business Review article, Legacy companies need to be more data-driven — fast, My co-author Ash Gupta and I said: lemonade And Traffick employs machine learning analytics and leverages thousands of data elements to provide personalized analytics and drive insurance purchases. “

Insurers can take advantage of the vast repository of big data available and combine this data with machine learning and AI capabilities to develop new policies that can reach new audiences. A April 2021 report published by GlobalData predicts that insured AI platform revenues will increase 23% between 2019 and 2024 to $ 3.4 billion. In this context, I recently spoke with Paul Ford, co-founder and CEO of. traffic— One of Insurtech’s emerging leaders in helping transform the insurance industry with big data, machine learning and AI. Traffk’s original goal is to call itself a “data-driven underwriting and delivery platform,” by leveraging the latest data and analytics tools and technologies to understand risk and modernize the underwriting process. did.

Paul Ford knew the insurance industry directly and moved to Etna as a financial underwriter after starting his career at Farmers Insurance. When Etna introduced the first Consumer Direct Health Insurance (HSA), Ford’s first glimpse into the future of insurance was when he was working in Etna. From there, Ford moved to Mercer, a unit of Marsh & McCrennan, where he used big data to transform practices and improve health and wellness plan pricing. Ford then worked on Safeway Health’s solution to manage the rising costs of premiums, all of which led to the launch of Traffic in August 2015.

Ford said: “Most insurers don’t use a lot of data to create their products. They rely on demographics over the age of 40. They struggle to price policies correctly. And this will cause many to miss huge financial opportunities. ”A March 2021 report from McKinsey,“Insurance 2030 – The Impact of AI on the Future of Insurance“AI and related technologies will impact every aspect of the insurance industry, from distribution to underwriting, pricing to billing,” he said. Advanced technology and data are already influencing delivery and underwriting, with policy pricing, purchases and bindings happening in near real time. “

At its core, Traffk is a data and analytics company trying to reinvent the insurance industry by applying big data, machine learning and AI. As Ford clearly states, “We are turning insurance into a large-scale data science exercise.” It uses Traffick’s underwriting and delivery platform to leverage vast amounts of longitudinal data and 4,000 data capabilities to optimize consumer behavior, billing behavior, buying trend signals, and product recommendations. It will be realized by doing. As a result, you’ll receive offers that match what the buyer wants to see. Ford continues. “Insurers have a large amount of data. Our goal is to use this data to work with insurer partners to create and design new and innovative insurance products.”

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A new report from the Insurance Institute of Canada (IIC) has found that technologies involving artificial intelligence (AI) and big data analytics will play a big role in reshaping the insurance industry in the next 10 years – and insurers will have to prepare accordingly for these anticipated changes.

The report, entitled “AI and Big Data: Implications for the Insurance Industry in Canada,” noted that both AI and big data are expected to transform the industry in very big ways because of the multiple opportunities they present – particularly when it comes to supporting underwriting decisions, claims management, investment management, hiring practices, and even marketing.

According to the IIC, “AI has the potential to fully harness the power of data to better anticipate and serve the needs of consumers.” It can also be used to automate the most tedious of insurance tasks, freeing up valuable time for the insurance workforce.

Meanwhile, big data analytics refers to utilizing advanced tools to analyze complex mass storage of data. In the past, insurers could only conduct actuarial analysis of recent loss experience to anticipate future claims on a quarterly or annual basis because they were limited by their analytics capabilities. However, the IIC said that current technology allows insurers to use real-time analytics to better understand the needs of their consumers, track industry trends, and make better informed decisions on claims, pricing and operations.

To best anticipate this tech revolution, IIC stated that “a focus on insurance consumers and their perspective will be critical to facilitating wide acceptance of new tools, systems and technologies.”

Insurers should also look to other industries that have undergone rapid change in response to emerging technologies, the institute said, and adopt the lessons they learned on managing risks. They should also expect insurance regulators to take interest in how the industry utilizes AI and big data, so insurers should champion these new approaches and demonstrate how they benefit customers in order to build confidence in the insurance industry, IIC advised.

The IIC has provided recommendations for the industry to address risks in AI and big data over the next 10 years:

1. Inform consumers;
2. Embrace innovation;
3. Be prepared for uncertain regulation;
4. Create new insurance programs;
5. Take the time to do it right (and don’t be scared to fail!);
6. Respond to changing consumers;
7. Invest in new technology; and
8. Accept different views of fairness.

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Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.

Insurance is about risk. The insurance industry sets rates based on expected payouts so that, hopefully, they end up with positive revenue. Setting rates and understanding payout in order to maintain profitability is complex, and the industry hope is that ML can help in achieving that goal. Note, here, I’m focusing more on ML than artificial intelligence (AI), because many of the complex statistical tools that are now considered ML can more efficiently accomplish some of the tasks than would neural networks, expert systems, or other purely AI tools.

There are multiple ways machine learning can help in the insurance industry. Let us take a look at three.

Insurance Underwriting

Health and life insurance are complex. There are multiple factors that go into understanding an individual’s risk factors for disease, illness, and mortality. Insurance underwriters have historically used a core set of factors such as male/female, age, and smoker/non-smoker. When other factors have been used, such as zip code, the problem of red-lining has appeared in insurance as well as the more well-known area of financial red-lining. Therefore, there are regulations about how some demographic information must be used.

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Insurance has become an extremely competitive market! While there are several players, customers are increasingly becoming aware about what they need. They are now seeking a more individualized experience and if you cannot provide it, rest assured they will find another company that does.

So, how do companies find ways to address the ever-increasing customer needs?
While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation.
Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent!

Information overload
New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information. Machine learning is helping insurers create opportunities by computing this vast stream of data for hidden insights.
Using complex algorithms, insurers can find patterns in the available data to establish predictive and descriptive data models. However, the most important point to note here is that machine learning can make sense of dissimilar datasets – whether partly structured, unstructured or structured.
An uninterrupted flow of information from multiple sources – click stream, social media interaction, audio clips and more – is processed real time, enabling the systems to incrementally adapt and self-create a continuous learning curve.

Customer is king!
Given that insurance vastly requires customer interface, machine learning has helped companies to stay on their toes. They can quickly and effectively recognise dynamic customer needs to provide them with simple and intuitive experiences. The analytics can predict the future trends and behaviour patterns of the customers.
Using advanced machine learning techniques to analyze the behavioural and socioeconomic activities of a prospective customer, some insurance companies are successfully deriving precise and individualized product recommendations.
In fact, many companies are working towards creating chat bots to enrich customer interaction and looking at automating the issuance process.

Merits of machine learning
While many machine learning algorithms are not completely novel, applying complex mathematical calculations to big data – over and over, faster and faster – automatically is a fairly new. As discussed above in detail, new technologies will only better the way insurers conduct business and serve their customers.
So, here are the key merits of machine learning to show why machine learning is crucial:

a) Superiority in advisory
Virtual advisors based on machine learning algorithms could potentially change insurance advisory by eliminating human bias and improve trust, thereby boosting customers’ confidence. While India may not yet be completely ready for the machines to take over, insurers can certainly use these virtual advisors for tackling routine tasks and allow their consulting team to handle the more constructive and socially interactive challenges.
b) Enhanced operational efficiency
Machine learning can help insurers become more efficient operationally by reducing turnaround time and improving productivity.
c) Digital Marketing automation:
Insurers can automate and optimize digital marketing expenses by implementing machine learning algorithms in multiple areas including identification of optimum bidding price for each keyword, re-targeting as per propensity to purchase, and nurturing leads through right content at the right time using the right medium.
d) Dynamic pricing:
Given the enormous data at one’s disposal, insurers can create a dynamic pricing model as per the socio-economic factors of a prospective customer. For instance, by analyzing data from wearable devices that monitor physical activity and heart rate, policies can be recommended with discounts.
e) Claims management and underwriting
Considering machine learning depends heavily on a repository of data, it will help create a deeper understanding of a customer’s risk profile. This will make the underwriting process much more effective and accurate. It will also significantly reduce the turnaround time for processing of claims, and help identify any fraudulent incidences on a case to case basis. Both these factors are crucial is ensuring loss prevention.

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