Insurance companies have relied on data for as long as the insurance has existed. Insurers today use big data from many sources to accurately, price and create incentives to reduce risk. Advances in data capture and storage make it possible to gain more information about customers than ever before. From telematics that tracks driving to social media that creates a digital footprint that can provide previously unseen information, new data sources can create larger personal pictures of the Customer problem.
Insurers are increasingly using Artificial Intelligence (AI) and machine learning to drive manual, lower risk, more efficient operations. After the rise of AI-powered insurance, the ability to predict with greater accuracy the losses and the behavior of their customers. Some insurers say it will give them more time to turn around and avoid claims.
However, this new way of doing things can actually make it unfair and corrupt the business model, so that some people can’t get coverage. ? After all, AI is not an agnostic technology and therefore can be used in ways that influence the thinking of its users. As a result, insurers must be very careful to ensure that they develop and use AI and machine learning in an appropriate manner and protect their customer data with appropriate controls. water.
How useful is AI?
AI has become an integral part of daily operations in most businesses, and can be credited with condensing large amounts of data into something usable. But companies are coming under a lot of public scrutiny because of the impact of algorithms on business, the question is how to properly integrate machine learning and AI is the leader of the mind. for insurance policies.
It’s important to remember that AI isn’t original; There is no need for algorithms; they are just algorithms. So instead of asking how AI is a firm, we should ask how far the ethics are maintained by the people who design the AI, feed it with data and use it. to decide.
For privacy issues, companies must comply with the GDPR regulation, the European legal framework for the protection of personal data. At this point in time, however, there is nowhere to grapple with the raft of ethical issues presented by this rapid development of AI. The EU AI Act, originally proposed in 2021 and expected to pass in 2024, is understood to be the world’s first global regulation for AI. Therefore, even though various pieces of legislation are being prepared, there will still be gray areas with companies relying on high-level guidelines that can leave a lot of room for interpretation. So, for the time being, the onus lies with companies, organizations and society to ensure that AI is used effectively. Insurers need to consider their entire data ecosystem to implement full AI management, including the insurtech vendors they partner with.
The value of using a clean traditional method should be considered as an important factor for a successful implementation and a rich skin.
As machine learning continues to increase in value across the insurance industry, the value of using the right approach should be considered as a tool for successful implementation and profitability. . In addition to transparency, the key components in WTW’s own strategy, for example, are about responsibility and fairness – understanding, measuring and reducing the bias – of models and systems in the way they work in practice, including how they are constructed. and technical excellence to ensure reliable and secure features and systems that provide privacy and security by design.
While insurers were already on a digital journey and new products before COVID-19, the pandemic has accelerated some of these changes. In addition to the new reasons of increasing uncertainty in the global markets and high inflation, the demands of the customers are changing the great demand in the business to change the speed.
In order to respond to customers’ needs for speed and convenience, with products and services tailored to them, and experiences similar to other areas of life and internet, insurers need to adapt quickly with AI technology to become useful. in order to enhance their risk management activities. The increasing use of AI in decision-making about our daily lives also requires a level of transparency to be explained to employees and customers.
Given the vast amount and variety of data sources, the real value of AI and Machine Learning is when it comes to making intelligent decisions in a way that doesn’t require human intervention. . However, this possibility arises from the discovery of a ‘black box’ in which most business personnel do not understand why and how a certain action is performed by the predictive model. This is because the more companies use data and the more complex the analytics and models they build, the harder it is to understand. This, in turn, leads to a great demand for the ‘explainability’ of the models and an easy way to access and understand the models, including from the point of view of the editor.
The question of how to effectively integrate machine learning and AI is top of mind for insurance leaders.
Transparent AI can help organizations explain the individual decisions of their AI models to employees and customers. With the GDPR decision soon to come into effect, there is also a regulatory requirement to provide consumers with information on how their data is being used. If a bank uses an AI model to assess whether a customer can get a loan and the loan is rejected, the customer wants to know why the decision was made. This means that the bank needs to understand exactly how their AI makes decisions, and be able to explain this in clear language.
Discover the potential of AI
Opportunities for flexible pricing and immediate P&L impact have never been better. The pursuit of cost flexibility can lead to the transition to advanced analytics, automation, new data sources, and the ability to quickly respond to changing market environments.
External data can help insurers better understand the risks they are underwriting. With a complete picture of the driver and vehicle, drivers can better assess the problem and detect fraud. By feeding external data into analytical models, they can accurately predict and draw the desired risk profile at a reasonable cost. Investing in AI can enable an insurer to improve the customer experience throughout the life of the policy – from changing the time of the quote to adjusting to requirements.
The push for transparent and responsible AI is part of a broader debate about business practices. What are the supplier’s key assets, how do these relate to its technology and data, and what management systems and processes do they have in place to keep up with it? Ultimately, for AI to have the greatest impact it needs public trust.
Transparent AI can help organizations explain the individual decisions of their AI models to employees and customers.