Tom Chamberlain, VP Customer & Consulting at hyperexponential, takes a look at the sheer power of AI and its ability to make sense of vast amounts of data. For insurers, intelligent automation is the key to accurate – and faster – pricing at the point of quote.
AI is top of mind in 2023 thanks to ChatGPT propelling its way into public consciousness. The market is set to reach $407 billion by 2027, leaving no industry untouched. What does this mean for insurance?
In today’s dynamic business landscape, the combination of data overlay and advanced AI driven automation has revolutionised pricing strategies for early adopters. This collaboration empowers businesses to dissect large datasets, extracting valuable insights that inform pricing and underwriting decisions. It’s time for the rest of the industry to catch up.
Hampered by poor data practices
The insurance industry is notoriously behind the tech curve, which translates into bad data practices that hamper operations and customer experiences. Inaccurate, outdated, or siloed data negatively impacts risk assessment, underwriting, and claims processing. Fragmented
systems lead to poor data entry and errors, which in turn delay processes and increase costs. The industry must prioritise data integration, quality assurance, and cybersecurity measures to enhance decision-making and streamline operations. Embracing advanced analytics, AI and data overlay can revolutionise the industry’s poor data practices.
In case you’re unfamiliar, data overlay merges multiple data-sets to generate deeper data insights. This enhances decision making by providing a comprehensive view of customers, markets and risks. It’s commonly used to improve accuracy in risk assessment, targeted
marketing, and personalised services. Basically, it helps companies create more informed strategies, enabling them to adapt effectively to market conditions. Specifically in insurance, data overlay is used to combine diverse data sources to refine risk evaluation and understand exposures.
Extreme weather conditions, for instance, escalate risk, with natural catastrophes costing insurers $50 billion in the first half of 2023. By using data overlay to merge data on climate patterns, historical claims, and demographics, insurers are able to react faster to new events and adapt exposure and pricing decisions more effectively. That could mean proactive adjustments in coverage, pricing, or risk mitigation strategies, fostering resilience against the mounting impact of climate change and other evolving risks on the insurance market.
One step ahead with machine learning
Machine Learning (ML) algorithms are revolutionising the insurance industry by processing vast data volumes and discerning patterns within market data. The technology introduces operational efficiencies that can materially speed up claims processing. By using ML to automate the completion of administrative tasks and simple risk decisions, underwriters and actuaries are freed to focus on higher value endeavours, like portfolio level analysis and strategic decision-making. Machine Learning can also be used to augment human decision making, providing insight on market price levels, likelihood of fraud within claims and in many, many other spaces in Insurance. When implemented effectively, ML can power improved insights, responsivity, and decision making, positioning tech savvy insurers ahead of competitors, regardless of market conditions.
Two steps ahead with GenAI and better data processes
GenAI goes a step further by creating new data and harnessing this power to generate personalised content. Insurance companies like Markerstudy are already using the technology to power better customer service, providing an ‘in the moment’ response. A key
point of sensitivity here is knowing when to take the conversation to an agent to resolve complex issues and building that into your logic.
Another brilliant use case is how this type of AI can accelerate the industry’s ability to ingest unstructured data.
A number of tech vendors have started announcing new integrations with ChatGPT to automate the ingestion of unstructured data. In practice, this means the data contained in emails from brokers containing pdfs, word docs, and excel spreadsheets can be automatically extracted and processed into a usable form and sent into downstream systems. Cytora is a company doing exactly that, helping insurers streamline the underwriting workflow. Elsewhere, pilot initiatives in the broker space are resulting in data being structured for insurers in ways previously unseen.
It feels we’re on the precipice of a data revolution. GenAI allows absorption of unstructured data while brokers move towards structuring their data more. These two pivotal changes in the way data is ingested and shared speak to a scenario in the not too distant future where underwriters are finally no longer bogged down in menial tasks of data entry. But of course, only the insurers who are ready to adopt modern technology will thrive.
More data + reactive analysis = intelligent decisions
When underwriters are relieved of data entry duties, they redirect their attention to exploring how supplementary data and analytical methods can enhance the pricing and underwriting process, thereby strengthening their decision making. They can focus on higher order tasks such as portfolio optimisation and portfolio underwriting, adding more value to the business. Solutions like pricing decision intelligence platforms are designed to enable just that – eradicating low-value tasks, enabling the application of ML, and powering deeper analysis to drive more profitable pricing decisions at both the individual risk and portfolio level.
There’s also the possibility of automating quoting, not just in the follow market – where the majority of algorithmic underwriting seems to be focused currently – but also in the high low specialty markets. No matter how or where GenAI is used, the insurers with inhouse ML skills will be the ones to capitalise on all forms of data. Those with the technology to react quickly to the changing landscape will win out in this market.
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