AI, Machine Learning and the Risk Modeling Revolution

AI and machine learning have been around since the 60s and 70s, but they only started really gaining traction at the beginning of the 2000s, with the rise of much more affordable computational power and the need to manage increasing amounts of data. Nowadays, AI and machine learning are being implemented in many different fields - from healthcare to robotics.

The financial world has been slow to adopt these new technologies, but is quickly catching up. Fintech and insurtech companies are on the rise, and are already harnessing the great potential behind AI and machine learning tools.

But what are AI & machine learning?

AI is defined as the development of computer systems that can mimic higher brain functions such as learning, decision making and problem solving. AI is a general definition that includes several different technologies such as machine learning, deep learning, voice recognition and more.

Machine learning is the ability of computer systems to learn based on past experience and improve future decisions accordingly. The systems continuously learn by being exposed to data, recognising patterns and using those patterns to make decisions. That is one of the main differences between AI and machine learning and traditional technologies used in finance.

How can fintech and insurtech benefit from AI & machine learning?

The potential benefits of AI and machine learning are enormous - especially in regards to risk modeling and pricing, which requires compilation of large amount of data, meticulous calculations and some “gut” instinct, and can often take weeks or even months. Traditional modeling processes can make inaccurate assumptions when calculating risk, but AI and machine learning can help overcome these inaccuracies.

Basis risk who?

When calculating basis risk for index-based bonds, for example, there are always differences between the index or reference populations and the actual policy holders. These can derive from varied social-economic backgrounds, demographic differences, group sizes or inaccurate longevity predictions and index calculation. This difference is referred to as basis risk, which can be quite significant in some cases.

Basis risk can be considerably minimized using AI and machine learning - sophisticated multi-population models can process large amounts of data, including granular information on every risk-exposed asset, improving the socio-economic and demographic segmentation of the insured populations and closing the gap between the index and the insured population. Furthermore, machine learning can significantly improve longevity predictions and therefore index calculation, constantly analyzing, learning and updating risk data - and in some cases reducing basis risk to negligible levels.

What about tail risk?

Tail risk is a form of basis risk that can also be reduced using AI & machine learning. Embedded tail risk derives from the gap between bond maturity and when the last policy holder passes away. Pre-defined commutation mechanisms are agreed upon in advance to cover for this risk, and are readjusted at bond maturity, but commutation calculations are not 100% accurate, leaving a significant amount of risk to take into account. By using advanced actuarial risk models and machine learning based cross-validation techniques, embedded tail risk can also be practically eliminated.

Objectivity, capacity and automation are key

When processing large amounts of data, humans are bound to make mistakes. The more brain power needed, the more potential there is for mistakes. AI and machine learning systems have an immense capacity to process data without the risk of human error. Decisions that might have been made in the past based on “gut instinct” can be automated, data-driven and objective, by combining the human brain power to define clear methodologies, and letting the machine work according to these methodologies in a systematic manner. This reduces the need for expert opinions, which are also prone to human error and subjectivity.

Time, in this case, really is money

As the saying goes, time is money. Risk modeling is time consuming, so the speed and capacity at which a business can process and model risks is crucial. The ability to automate repetitive parts of risk modeling and process very large amounts of data efficiently in relatively short periods of time can lower costs, boost returns and release capital.

Embracing the tech

AI and machine learning have only recently been adopted by Insurtech and are already revolutionizing risk modeling. Who knows what future advances will bring? Here at Vesttoo we have fully embraced these technologies, and they constitute the very core of our digital solution. The Vesttoo platform uses advanced algorithms for instant, automated and vetted asset evaluation and risk pricing. Risks are modeled while minimizing basis risk using our proprietary AI and machine learning algorithms. An index is calculated to fit each risk, ensuring optimal risk transfer to the capital markets. Insurer data and risk layers are structured with parametric financial structures, optimized for capital capacity placement and risk transfer to the capital markets - providing insurers and pension funds with immediate solvency relief.

Vesttoo, has developed advanced technologies for data-driven risk management, transferring actuarial risk to financial risk through the capital markets. Vesttoo specializes in risk modeling and alternative risk transfer for the Life and P&C insurance markets, providing insurers with a low-cost strategic risk management solution for immediate capital relief, value enhancement and liability hedging