Insurance has been a part of the financial world for as long as the concepts of money and trade have existed. The idea of insurance can be traced back to the 3rd and 2nd millennia BC, when Chinese traders would divide their wares among multiple ships to reduce the loss while traversing treacherous rapids and Babylonian traders would pay their money lender a little additional sum to cancel the loan, should anything happen to their shipment at sea. The oldest known insurance contract dates back to 1347 in Genoa.
Everything in the world of trade and finance - from how goods are produced to how they are transported, the form of monetary transactions, lending, borrowing and interest rates - have been upgraded with every industrial revolution and technological advancement that the world has seen. Why then, has insurance been stuck in the same pen and parchment ink methods of centuries and millennia of yore? Much of the data collected by insurance carriers are still not digitised, and it’s time for the insurance industry to join the digital century. This post will detail how Artificial Intelligence aims to do just that.
Above and beyond the basic, administrative tasks, the most exciting scope for AI in the insurance industry is the ability to synthesise data collected from other technologies (Internet of Things), such as drone technology, digital mapping, historical data analysis, telematics, etc. in innovative ways.
The introduction of mobile solutions has simplified the process of collecting data for an insurance claim including evidence collection,data storage, customer interactions and the utilisation of machine learning to assess damages and predict the cost based on historical data, sensors and images.
One of AI’s biggest selling points is its ability to process large volumes of data at an accelerated pace. This is not just beneficial for processing the huge amount of data that is entered into the insurance industry every single day, but also for processing historical data that has been amassed over the years. You might easily understand why it’s important to digitise current data, but you might be asking yourself why it’s so integral for historical data to be looked over by the neural network as well. This is so that companies can track the trends in the archives and use the data to upgrade the way their business operates.
For example, New York Life Insurance extracted the cause of death from 10 years of paper death certificates to refine their life actuarial models. The other great thing about Artificial Intelligence and Machine Learning is its ability to speed up the process of underwriting claims, saving the company time and money, while improving customer experience.
The neural network process simultaneously digitises all the paperwork while tracking any anomalies or inaccuracies in the case files, detects risk factors and fraudulent claims earlier, easier and faster, and because self-learning plays such an integral role in AI, the system applies previous experience with fraud to newer, unseen cases automatically. AI and ML can also be used to improve the Portfolio Loss Experience of an insurance company by filtering out the high risk customers, who are charged more malus by the company and reduce the company’s own losses. It also uses the data collected from IoT (internet of things) to analyse data and predict risk factors before they occur, thus, preventing losses and reducing the need for insurance.
This does not mean that the insurance industry will become void but simply changes the way in which it functions. For example, most AI-driven insurance is specialised for only one particular job - home, life, health, car, personal belongings or robo-advisors that deliver personalised advice for every customer - and even if the same insurance carrier offers different types of insurance, every algorithm is different. There will also be a decline in the premium-pooling, which was characteristic of older insurance policies, since every policy will be tailored to the needs of each customer. The future is here now. Insure-tech is is transforming to new digital areas with automated human brain like thinking power to make such decisions in real time.