Artificial intelligence (AI) is already beginning to transform the insurance industry. Once the preserve of science fiction, AI today has a growing number of practical applications within insurance.

One area where it is already finding practical application is that of commercial insurance underwriting.

As in all industries, there is a natural fear of job losses as a result of the wide-scale introduction of artificial intelligence. These fears are misplaced.

Instead, AI is likely to remove some of the more low-value and onerous tasks currently undertaken by employees, freeing them for more interesting work using skills that AI is unlikely to ever replicate.

Employees will then have more time for high-value tasks as more and more routine work is undertaken by AI.

With any disruptive technological change, advantages are always accrued by companies that are quickest to embrace the new technology. When the benefits are so immediately tangible for insurance companies it makes sense to be ahead of the curve.

Let's take a look at how AI in insurance underwriting is already making a practical difference.

AI is reducing the potential for human error and bias in underwriting

As experienced and rational as human beings are there is always the potential for human error. No one is perfect, and we all allow biases to creep into our decisions. These are often unconscious and deeply held. Although all underwriters attempt to recognise and overcome their biases there are times when they still impact the decision-making process.

AI applications are able to aggregate large sets of data in different formats. They're capable of applying pre-set identifiers and models and the results can be viewed by a human underwriter who can then make an informed decision based on the data.

Going forward AI can become self-reliant by learning from previous tasks. The human element can then be removed and the technology can make its own decisions.

It's this ability to actively learn from experience that separates AI from older technologies that help with data management.

AI can model risk exposure

A number of applications are being developed that assist in the underwriting assessment of risk. The aim is to create greater consistency in the process making rapid sense of a wide range of variables.

Advanced technologies like predictive analytics, big data, machine learning and Geographic Information Systems (GIS) are already delivering promising results for insurers.

Currently, predictive analytics are focusing mainly on pricing but this is expanding to include a greater range of business insights. Big data is moving from being just about location and geographical data to include customer behavioural data such as social media use and public records.

GIS is used for geophysical data mapping but in future is likely to be able to make geospatial assessments about the risk of natural and climatic catastrophes.

Visual recognition, natural language processing (NLP) and data mining can be used to understand public sentiment across unstructured public domain sources like social media.

All of these tools can help insurers augment the underwriting process informing how risks are appraised and premiums priced.

Tackling the growing cyber threat with AI

Cyber threats to business proliferate by the day. Staying abreast of them is a perpetual challenge for insurers. The costs to the economy run into the billions.

As more companies move into cloud-based infrastructure, the risks associated with cybersecurity and the potential for hackers to cause mischief increases. Businesses are looking to cover themselves against losses incurred from cyber attacks.

The historic data available for underwriters to assess risk in relation to cybercrime is limited. As cyber threats are continually evolving it's tricky for insurers to make a considered up to date judgement. Risk exposure to cyber threats is almost impossible for human beings to fathom.

A self-learning AI system powered by machine learning algorithms will be able to keep pace with developing threats. Eventually, it should be able to predict new cybersecurity threats before they emerge. This will allow for better security and more sensitive insurance coverage features.

Using AI and Big Data Analytics to deepen understanding of risk

AI can help expand and deepen the range of data sources available to underwriters. This should lead to better evaluations of risk. This has positive consequences for profitability overall by selecting more attractive risk profiles within broader segments. With the use of big data analytics, company risk data becomes more transparent.

As AI can continually collect new data and feed it into machine learning algorithms, risk management is optimised. The need for time-consuming due diligence processes can be reduced.

AI can leverage non-traditional data to help understand the non-traditional risk

Every company has a unique risk profile. These arise out of issues like geopolitical uncertainty, cybersecurity, equipment failure and public liability. The particular risks and challenges are never the same even for competitor companies within the same industry.

AI can make use of unusual, tailored data on a large enough scale to assess individual risk profiles. By using AI, underwriters will remove the need for speculation and guesswork when arriving at a realistic premium level.

A more intelligent, consistent and tailored approach

Overall, AI will make life easier for underwriters and improve the quality of cover and service received by clients. By utilising data on a scale beyond practical human management and in a way that allows for continual ongoing input it will revolutionise the underwriting process.

This highly tailored approach based on a deep understanding of the data represents a real breakthrough for the industry.

Artificial is at the forefront of this seismic change

When it comes to radical technological change in any industry, it helps to have the assistance of knowledgeable professionals.

Artificial have been helping insurers move beyond the time-consuming paperwork and manual processes of the traditional insurance life cycle. Using the most up to date cloud-based tools and Machine Learning techniques, we are helping insurers to make efficiency gains while improving the overall customer experience.

For further advice about how we can help please don't hesitate to get in touch.