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Confidence is at the core of AI adoption, but how confident is the Insurance industry at this point and what does our future look like?
Hiscox in collaboration with Google has developed an artificial intelligence model for the core underwriting process for specialist risks as reported in the Financial Times. This is a significant step in the adoption of AI within the insurance industry since specialist commercial insurance had been thought of as the domain of experienced teams of underwriters — not something that could be delegated to the decision making of a computer.
An Overview
Like many other industries, insurance has been undergoing a global digital transformation with insurers investing in technology to streamline operations, enhance customer experience, and stay competitive.
With a growing emphasis on adopting a customer-centric business model, insurers are focusing on personalised services, improved communication, and simplified processes in order to boost customer satisfaction. As consumers – especially over Christmas, we are using apps to book our taxis, to buy our presents, and maybe to engage with our bank – we demand our interactions with insurance services to be equally user-friendly.
Insurers have also been leveraging the power of data analytics and predictive modelling for robust risk assessment, underwriting, and claims processes which allows for more accurate pricing and risk management. These areas involving large volumes of data processing have been the first to benefit from the adoption of AI, but this only scratches the surface of the opportunities.
It is essentially the use of AI as a time saving tool to enhance relational databases freeing up resources to focus on higher value activities. Parallel trials of AI vs employees are easily constructed to audit the AI and achieve the all-important confidence.
AI Opportunities in the Insurance Industry
AI is a natural addition to the usage of data analytics and can be a powerful tool to improve the process. AI can assist in better understanding of predicting risks by analysing vast amounts of historical data and external factors.
Insurers can use predictive analytics to anticipate and migrate potential losses more effectively. By utilising machine learning algorithms, insurers can build predictive models to assess the likelihood and severity of future risks. These models can identify patterns and correlations that may not be apparent through traditional methods.
Humans are naturally adept at identifying patterns, but a machine is far better, quicker and more consistent. The challenge is in understanding how the AI has come to the decision it has, especially when you can’t just ask someone face to face.
AI can instantaneously help detect unusual patterns and anomalies that indicate fraudulent activities. It can then analyse data in real time, thereby flagging potential fraudulent claims or activities for further investigation. But explainable AI and Human in the Loop (HITL) within these processes ensures the confidence of all stakeholders.
The Risks of AI in Insurance
The use of AI also introduces new cybersecurity risks. Adversarial attacks can manipulate AI models by introducing malicious inputs, leading to incorrect predictions or decisions. Securing AI systems against such attacks is crucial and will likely represent a constant investment as cyber attacks evolve.
There are also biases in the data to consider, if the training data used to develop AI models are biased, the algorithms may perpetuate or even exacerbate existing biases. This can lead to unfair or discriminatory outcomes, particularly in risk assessments related to demographics or socioeconomic factors.
What Does The Future Hold?
Like all disruptive technologies, AI will eventually settle into normality and stop being a scary buzzword that panics the masses.
In the Insurance industry, innovators must first seek to build and maintain confidence through transparent, explainable AI to ensure that organisations are not leaving themselves vulnerable to the threats that it poses. This is required alongside an ever-evolving AI landscape where generative AI becomes more widely adopted across society.
It would therefore seem practical and functional for the insurance industry, one that is predicated on data based decision making, to adopt a hybrid model in the short term. An AI can be used for the broad strokes such as analysing large amounts of data en masse, whilst the humans retain ultimate responsibility for any decision making.
If we view AI simply as another tool to help with accuracy, much like a calculator or Excel, then it becomes easier to see how it can fit seamlessly into our lives.
