Underwriters are at the heart of any insurance business. Their ability to assess risk accurately based on a wide variety of information is critical to product innovation, effective pricing, and ultimately whether a business can make a profit in an increasingly competitive market. Consequently, insurance companies are always striving to optimise their underwriting processes, in a bid to get ahead.
Underwriting has advanced enormously since the early days of insurance, when companies would assess risk based on paper-based applications and binary logic. As technology and modelling have developed, the process has become increasingly sophisticated and digitised, with underwriters now sifting through rafts of proprietary and third-party information. The challenge is to process and analyse this ‘big data’ as accurately and quickly as possible, to keep up with customer expectations regarding service and convenience.
Artificial intelligence in underwriting has advanced rapidly in the last few years and promises to revolutionise how insurers assess and price risk. But does the reality live up to the hype?
How is artificial intelligence being used in insurance underwriting?
ChatGPT might be the latest version of AI to capture the imagination, but AI has been gradually altering the way insurance underwriting is carried out for several years. AI and machine learning systems can analyse large datasets much more quickly than manual processes, particularly in retail lines, which have a higher volume of relatively straightforward policies. AI-powered underwriting systems are being used to evaluate a person’s risk profile by scanning huge amounts of structured and unstructured data sources, such as social media, financial, and public records, with the ability to make decisions in a matter of seconds in some cases.
To give a few examples, the Spanish insurtech Bdeo recently announced that it is now automating more than 50% of underwriting in Spain, thanks to its AI solutions. Another leader in the space, the reinsurer Munich Re is recently launched its Predictor platform, which it says “enables insurers to rapidly adopt predictive underwriting with modules spanning data preparation, model deployment, integration and performance monitoring”. Lexis Nexus has also introduced an AI solution to speed up underwriting in home insurance, while the professional services giant, PwC, recently announced a partnership with Cognisure to launch an AI powered digital underwriting solution for the London market.
What are the benefits of artificial intelligence in insurance underwriting?
The potential benefits of AI in insurance underwriting are huge. According to a McKinsey report digitising underwriting can help insurers “see loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent.” Here’s a few reasons why:
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Increased efficiency and accuracy:
By automating key underwriting tasks, such as collecting, processing, and analysing structured and unstructured data, AI can significantly speed up the underwriting process. This in turn helps to boost customer satisfaction, increase sales, and reduce administration costs.
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Risk assessment and management:
AI algorithms can analyse historical data and identify patterns that human underwriters may overlook. This enables companies to develop more accurate and sophisticated risk assessment models, to price policies more accurately, and reduce loss rations, improving overall risk management strategies.
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Fraud detection and prevention:
Detecting and preventing fraud is a significant challenge for underwriting, with research showing that insurance fraud has risen 25% due to the cost of living crisis. By analysing vast amounts of data, AI can identify suspicious patterns or activities indicative of fraudulent behaviour. This helps insurance companies to combat fraud more effectively, ultimately reducing losses and improving the industry’s overall integrity.
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Free up underwriters:
By automating much of the most time-consuming and mundane tasks, AI can also free up human underwriters to focus on higher value, strategic tasks, such as more complex risk assessments, strategic decision-making and developing innovative new products.
Is artificial intelligence a panacea for insurance underwriting?
Introducing AI into insurance underwriting isn’t without its challenges. Insurance companies must be careful not to place too much trust in AI programmes, which are only as good as the data they digest. If they are trained inadequately or fed inaccurate or incomplete data then there is the danger of ending up with a biased or discriminatory system, which is doing the opposite of what it is designed for.
As AI is still a relatively new concept, companies must be careful that they don’t lose customer trust, particularly in terms of data privacy and security and complying with their regulatory responsibilities. Consumers overall still like to deal with human beings and know that there is human oversight of their information, so firms must be wary of moving too quickly. Human underwriters will still be crucial to oversee both the inputs and outputs of an AI system, ensure that decisions are fair and transparent, and to retain the human touch in understanding the needs and behaviours of customers.
The future of artificial intelligence in insurance underwriting
There is little doubt that AI is already changing the face of insurance underwriting and will continue to do so in the coming years, as the technology gradually becomes more sophisticated and capable. While the potential benefits are enormous, implementing AI and machine learning systems will mean a significant transition for insurance companies, who need to rethink tried and tested processes and adopt completely new ways of working. It can’t happen overnight, and firms must first put in place the right digital foundations in terms of collecting and organising their data and upskilling the workforce to work with innovative tools. For those who get it right, the sky is the limit.
How to start implementing AI in insurance underwriting
If you’re ready to get started, here are a few tips on what to think about when implementing AI into your underwriting process:
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Start small:
Rather than trying to run before you can walk, start by applying AI to a small underwriting problem so you can begin to understand the technology and how it can help improve how you work.
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Set your objectives:
What are you trying to achieve from using AI on this specific problem? For example, do you want to drive faster decisions, or perhaps reduce your loss ratio for a particular product? This will help focus your efforts and investment.
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Risk management:
Also consider the risks involved, and how you can start mitigating those early on in the process.
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Digitise the relevant data and processes:
AI relies on having the right data to work with, so assess what data and infrastructure is required to deliver that. This could involve implementing software like Insly to fully digitise your underwriting function and other areas of your workflow and to support a more holistic data strategy.
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Invest in specialist skills, or external expertise:
If you plan to make AI a core part of your underwriting strategy going forward, then you will benefit enormously from hiring individuals with the right expertise, upskilling your workforce, or bringing in external specialists.
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Test, and test again:
As with any new technology, implementing AI isn’t an exact science, so trial and error will be key to working out how it can best help you reach your objectives.
If you’re taking the first steps in your digital journey, then Insly makes it simple, with no-code modular insurance software covering the whole insurance lifecycle, from distribution and underwriting, right through to claims. Laying the foundations for a digital, automated future.
