Bolstering Staff Expertise
Unbiased machines also offer benefits to streamline very complicated work. Take underwriting mines as an example. To underwrite a mine, you need mine engineering expertise – a unique skillset. Even if a business wanted to underwrite 10 times the mining business it currently has, it might not be able to find enough underwriters with the necessary skillset.
However, machine learning offers businesses the chance to encode the best practices of their underwriters, allowing them to assess more risks. Each underwriter performs differently, with a list of preferred resources and special skills. That means an insurer can take certain aspects of what its underwriters do, build those aspects into a machine learning algorithm and scale them to create an effective model. Although algorithms and artificial intelligence do not replace human workers, they make employees much more efficient and less error-prone, especially when there’s a huge number of specialty risks that need to be assessed.
People vs. Computer Skills
Traditional actuarial models have always been about pattern matching; machine learning simply augments those existing tools to create more accurate underwriting risk evaluations. Humans are good at negotiating, sales and decision-making. Machine learning and algorithms are good at taking huge quantities of data, analyzing that data to make inferences and detecting patterns.
What machine learning does is let you apply data to more complex problems. A traditional auto insurance model might have five variables, for instance. But in specialty insurance, there may be hundreds or thousands of variables involved, which is where machine learning offers great benefits.
Techniques such as NLU also widely expand the sort of datasets available to underwriters. For example, NLU lets you search through textual information to automatically find key concepts and phrases, which can then be brought to a claims adjuster’s attention.
Combined with data mining and text analysis techniques, NLU can:
- Streamline the flow of data to the correct departments or agents.
- Improve an insurer’s decision-making by providing timely and accurate data.
- Boost service-level agreement response times.
- Help detect problematic claims and activity.
Using Quality Data Is Essential
Accurate risk evaluations require good data. Without using quality data in training algorithms, you will not get good outputs. An important step is to test your models against known outcomes, so you can see the quality before deploying a forward-looking predictor.
This also requires large volumes of data to train your algorithms and create more accurate risk evaluations. While there are efforts underway to use “small data” to train models, it’s still in the early days. Machine learning requires large amounts of cleaned, validated data; this data can either be imported from your organization’s existing databases or acquired from a third party.
Although the data revolution is transforming the insurance industry, we’re still a long way from artificial general intelligence* or any sort of science fiction-type singularity scenario. But in terms of a marketplace where machine learning is transforming the way business is processed, we are already there.
*Editor’s Note: Wikipedia defines “artificial general intelligence” as the intelligence of a machine that could successfully perform any intellectual task that a human being can.
In a report titled, “The State of Artificial Intelligence,” CB Insights provides the following definition: “Artificial General Intelligence or General AI is the concept of an AI system with human-level intelligence and cognitive ability that can perform a broad range of tasks and apply that knowledge to solve unfamiliar problems without being trained to do so.” Emphasis added.