How AI and intelligent document processing can help insurers to deliver better services and generate more revenue
Digital transformation is no longer just a ripple happening at the edges of the insurance industry but is now a wave picking up momentum. The use of artificial intelligence has increased exponentially across all industries in recent years and most insurance executives already understand that AI will drastically change their industry. An Accenture survey found that 79% of insurance executives believe that: “AI will revolutionize the way insurers gain information from and interact with their customers.”
AI has become proficient at performing tasks historically difficult for computers to execute, including predictive analytics. But the acceleration in AI adoption is being driven not just by improvements in technology but also by a desire to enhance the customer experience. Retail insurers want to provide hyper-personalization and shorter response times while those in commercial lines want to offer more proactive ‘value-add’ client services. Achieving these results requires access to the right information at the right time and computers can aggregate and analyze more data than a human ever could. AI-powered Intelligent document processing platforms like Eigen enable firms to tap into rich sources of data to increase their analytical capabilities and revenue-generating potential.
However, when it comes to using AI and automation in insurance, some people remain hesitant and fear the unknown that comes with using nascent technology in new and unfamiliar ways. As an industry that’s always under scrutiny, from regulators but also from clients who rely on insurers in times of crisis, it’s understandable that some think the risk of getting it wrong outweighs the benefits. And some are skeptical about the business benefits having been burnt by their previous experiences of AI-powered solutions that have failed to live up to expectations or hype.
But there is considerable potential for insurers to use AI tools that deliver vast improvements to their business and their bottom line. The key is to understand which solutions can address your specific pain points or problem areas and to have a realistic view of what’s achievable and how long it will take to get those results. By focusing on the benefits to clients and end-users throughout the discovery, development and deployment phases of your AI projects, you’ll deliver improved services as well as drive operational efficiencies, enhance risk management practices and increase revenue-generating opportunities.
For example, underwriting involves manual processes that have evolved and become trusted over the years, but which are inefficient and could be improved with AI to help firms generate more revenue. The slow transfer of data between brokers and carriers, the duplication of effort when rekeying submission information and the lack of good quality risk data for underwriters to act upon can all be addressed using AI. Policy administration and servicing can also benefit from AI and automation, alleviating the operational bottlenecks and issues around data fragmentation that often exist. Asset, capital and risk management are also areas prime for the adoption of AI solutions that can provide ready access to unique insights and usable data to inform decision-making.
How can the Eigen platform help insurance firms?
Eigen unlocks data from documents at scale, locating, extracting and organizing text into usable structured data to support a broad range of functional areas and transformation initiatives within insurance. Eigen can help unlock data to help underwriters price more effectively or to enhance policy management and servicing processes. By automating the extraction of information from submissions, underwriters can triage them faster and respond more quickly with quotes while maintaining high standards of risk assessment. The platform can also provide granular data for capital, risk and asset-liability management optimization and reduce the administrative overheads associated with regulatory reporting, compliance and internal audit.
The Eigen no-code platform is also document-agnostic meaning it can solve a multitude of challenges relating to document processing and data availability. Wherever you have large volumes of documents, inefficient or complex workflows and where decision-making would benefit from the addition of up-to-date granular data. Using Eigen, business users can quickly train machine learning models to automate the compilation of data from binders, policies, slips, engineering or safety reports, addendums and other sources on an ongoing basis to better understand your risks, exposures and opportunities across different lines.
The Eigen platform can also be used to support insurers’ due diligence investment processes to make sure they invest wisely, which is not just more efficient but has a material impact on the company’s resources and bottom line. Eigen can, for example, extract and export bond-level and note-level information with high accuracy for Solvency II matching adjustment analysis saving countless hours of actuarial time and enabling the insurer to claim capital relief.
Driving innovation in insurance
We know that the value of data can be unlocked using AI to inform pricing and decision-making for underwriters. And AI can automate and accelerate aspects of policy administration to create more efficient workflows and better-quality data for other departments. And the investment arm can use the technology and its data output to enhance security selection and potentially optimize capital allocation. So, given all the advantages of using AI for intelligent document processing, how can we move forward and overcome the hesitancy that still exists?
To realise the full potential of AI, insurers need to understand how and where this technology is best placed to help their people and deliver maximum return on investment. For many firms, the initial driver for adoption is a market event, a regulatory change or the identification of an operational problem that can’t be solved by throwing more people at it. Starting small with a discreet project, enables insurers to get familiar and comfortable with the technology and reap the benefits on a small scale before increasing the scope. However, it’s often the case that the scale of the problem and the need that drives the business case dictates a bigger project with multiple requirements and stakeholders to satisfy. It’s important to break these bigger initiatives into phased deployments with milestones and success metrics agreed in advance to make the project more manageable and to start realizing the benefits as soon as possible.
Whichever pain points, problems or processes you want to use AI and intelligent document processing to tackle, it's important to identify your goals, define the requirements and agree on the measures of success so everyone is aligned. This will inform the scope of the effort, who needs to be involved and which AI solutions and vendors are up to the job in hand. Ultimately the insurers who will thrive in this world of data will be those who choose to ride the AI wave, rather than being overcome by it.
- World Economic forum 2020
- Gartner Cool Vendor 2020
- AI 100 2021
- Lazard T100
- FT Intelligent Business 2019
- FT Intelligent Business 2020
- CogX Awards 2019
- CogX Awards 2021
- CogX Awards Best AI Product in Insurance
- Forbes The AI 50 2022
- Ai BreakThrough Award 2022
- FStech 2023 awards shortlisted