Inspect What You Expect – The Future of Insurance Machine Learning
By Michael Schwabrow
Since joining Cloverleaf Analytics two years ago, this phrase has been one of my favorites in terms of explaining the value of our machine learning (ML) technology. However, this isn’t just a catchphrase, but it defines the critical worth of ML. In an industry that delivers value in protecting the insured against risk, ML may be a greater contribution to the industry than GenAI. At least in the near term.
Why is ML more Valuable in the Near Term than GenAI
There is no argument that GenAI is exciting, and it may be a bigger catalyst for transforming our industry. Right now, ML has more real-world benefits in the areas of core insurance business functions that protect the insured and safeguard insurer profitability. Machine learning that leverages unified data insights empowers insurers to make small impacts in risk assessment, pricing, fraud detection, and claims processing. Enhancement in these areas can translate into millions of dollars in profitability improvement by preventing losses to the insurer.
Unfortunately, I believe this benefit of ML is often overlooked and even confused with AI when insurers are trying to progress toward meaningful digital transformation. There is a lot of talk from industry experts and senior insurance leaders about this year being the time when GenAI must demonstrate a path to value. With machine learning, there are decades of valuable insurance data about claims, policies, risk factors that can help any insurer modernize with new products, better pricing, and more satisfied customers.
Another reason why ML is more valuable in the near term is the costs and infrastructure requirements. There is no need to retrofit or build massive infrastructure farms to ensure the future of ML. Right now, GenAI has promise but it is more like the icing on the cake of insurer digital transformation compared with ML, NLP, and other core technologies that are the cake.
Reshining the Spotlight Not Redefining A Category
Unlike my previous articles where I focused on redefining the terms insurtech and insurance analytics, machine learning does not require redefining, I am suggesting insurers shine the spotlight on it again. With emerging risks and new depths of data insights that are available, ML can help make sense of critical risk factors that the average insurance professional may miss.
From a claims perspective, automated claims have already been highlighted within our industry as an innovation. This foundation has positioned the insurance industry to start looking at ways to use AI to make claims decisions happen in a few minutes or less.
As criminals attempt to take shortcuts using advances in AI to perform fraudulent actions, machine learning is critical to understanding past, present, or potential future fraud claims through pattern analysis.
What the Future Could Hold for ML and GenAI for Insurers
The insights that ML delivers combined with the automated intelligence of GenAI can bring a future of user-based, hyper-personalized insurance for every individual. Instead of rejecting particular regions or individuals, coverage could be adjusted in real-time with data from satellites, geographic information systems, IoT devices, transportation vehicles, and other personal identifiable information.
Like GenAI, ML Depends on Quality Data
Since ML can crunch decades of data, an error in a data lake that trickles throughout could have tremendous consequences. This is a major reason why insurers need to invest in unifying, cleaning, and preserving the integrity of historical and current data.
Instead of a snowball of good outcomes in terms of quality ML leading to less risk, better products, and happier customers, some insurers are courageously moving forward with digital transformation but with a snowball of bad data. This is like making a delicious soup only to find out that the sauce was good, but the vegetables and meat were rotten.
Cloverleaf is focused on ensuring insurers not only look good externally but using advancements like GenAI and being internally sound. So, when an insurer inspects what they expect using quality ML, there is good all around.