Being part of the insurance industry, where everything we do is heavily regulated, and the principles of fairness and transparency in our rating and underwriting practices are always at the forefront, which prevents us, in many instances, from moving AI initiatives forward. Concepts like the inability to show proof of work, AI hallucinations, or even security concerns make it difficult for insurance companies to adopt. However, plenty of use cases still make the insurance industry a good candidate for Artificial Intelligence initiatives implementation. On the other hand, there is currently a sense of FOMO (Fear of Missing Out) across the industry that clouds the path to understanding how this technology can be effectively utilized in insurance organizations to generate tangible value.
Incorporating AI into the operations of an insurance company, enhancing their customers’ experience, or increasing our workforce productivity are important steps toward embracing digital transformation and staying competitive in the current insurance landscape. This article will provide you with practical use cases where we have seen AI applications yield tangible results. But before we begin, we need to go over three essential guidelines we need to keep in mind when applying AI to insurance business operations:
- Have a plan: Work with your Enterprise Architecture practice to create a digital density map of your organization by area. This doesn’t have to be too elaborate. It simply entails a functional diagram illustrating key processes and how information flows through them. Chart how it currently operates, and envision how technology could alter these information flows. For instance, if reporting relies on PowerBI development expertise, envision the possibility of Big Data and AI-powered forecasts in three years. After completing your map, it will be easier to pinpoint which changes should be prioritized to solve existing organizational problems.
- Start small: AI doesn’t necessarily mean full automation. Although end-to-end Business Process Automation (BPA) is an ideal goal, the reality is that certain complex insurance use cases may only partially be automated cost-effectively. For example, using an AI tool to rapidly analyze damage severity in a car based on videos or images can work well. Still, the entire claims adjustment process and in-depth damage analysis might be more challenging. In such scenarios, hybrid solutions that combine Robotic Process Automation (RPA) and AI-supported features with human input and interactions are perfectly valid. Start by automating basic use cases and expand from there. Listen to your customers and how they want to interact with you. Not every interaction needs to be automated; in fact, customers will often prefer to have a human connection, particularly when dealing with a claim.
- Deliver value: Exercise caution with hybrid implementations to prevent overlapping tasks and duplicating costs. For instance, if you implement an AI tool to process claims, make all your customer experiences leverage the same tool. This means FNOL callers, website visitors, API integrations, etc. Consistency in how your technology is being used is critical. Ensuring that its implementation delivers value is crucial.
Now that we have gone through these guidelines let’s delve into some of the most practical use cases that your organization could be implementing right away:
- Sales
Digital journeys for agent onboarding, quick quoting, and policy issuance are essential in today’s competitive market. Regardless of the distribution approach of the carrier, enabling self-service portals and facilitating 24/7 purchasing options can yield immediate results. While it’s true that in insurance, many customers and agents still prefer traditional phone calls over self-service tools, efficiency will increase gradually as users become accustomed to the new tools. You can add an AI layer to digital journeys by recommending products based on client profiles, streamlining quoting forms, and providing conversational chats to guide customers or agents through the buying process.
- Inform Underwriting
There is a difference between deciding and informing. AI is, in our opinion, a great tool to enhance your underwriting decision-making, but it won’t replace it. At least not for now. Practical use cases include identity verification for Know Your Customer (KYC) processes, document scanning and reading, such as agent licenses, Errors & Omissions (E&O) policies, or police reports. More advanced scenarios involve analyzing building images to gather data for underwriting.
By analyzing extensive data, including customer profiles, historical claims data, and external factors like weather patterns or economic indicators, AI algorithms can assist underwriters in making more accurate risk assessments. This enables insurers to offer personalized policies tailored to individual customer needs while reducing risk.
- Claims
Automating the claims assessment process can reduce manual errors and expedite processing. Claims often need to be more diverse and structured to be standardized into a single digital flow. Multiple types of files, such as photos, paper reports, call recordings, etc., need consideration. However, AI can integrate all these artifacts to generate the forms or reports adjusters require to analyze claims. You can also automate the approval or rejection of straightforward cases, reserving human intervention for more complex cases.
In auto insurance, various AI-powered tools can assist in real-time damage assessment, severity determination, and even automatic adjustment estimates. This not only accelerates claim settlements but also enhances customer satisfaction.
- Customer Service
AI-powered chatbots are revolutionizing customer service within many industries. These virtual assistants can handle routine inquiries, provide policy information, and assist with First-Notice-of-Loss (FNOL) filing or updating personal details. By automating these processes, insurers can free up human agents’ time to tackle more complex customer issues while providing round-the-clock support.
In conclusion, implementing AI into your insurance operations demands assertive planning and integration with existing systems. Identifying specific pain points within your organization that could benefit from AI solutions and prioritizing them accordingly is crucial.
By strategically embracing AI technologies, insurance companies can unlock growth opportunities, enhance operational efficiency, improve customer experience, and ultimately thrive, becoming more competitive.
AI shouldn’t be the latest shiny thing that we want to implement because everyone else is looking into it, but instead, focus on the immediate deliverables that could provide value to the organization. There are plenty of inefficiencies in our world that could absolutely benefit from Artificial Intelligence.
Francisco Diazluna: I am the CIO at Producers National Corporation, where I leverage my extensive experience in implementing digital and transformational initiatives. With a strong background in software development and a specialization in insurtech, I have spent over 25 years delivering solutions to complex business challenges. My focus is on providing leadership and sound architectural solutions throughout the design, implementation, delivery, and maintenance of digital solutions while ensuring regulatory compliance.
I excel at aligning corporate strategy with IT tactical projects, offering guidance to C-level executives on budget and resource needs. As a quick learner, I take responsibility for all aspects of IT operations and the IT portfolio of programs, projects, and initiatives.
I enjoy assisting and mentoring my staff through challenging architectural and software development issues. I am comfortable providing recommendations, whether in a boardroom setting or a technical war room. My responsibilities include implementing insurance suites like Duck Creek, creating public-facing websites using ASP.Net, developing intranets via SharePoint, and managing data warehousing solutions with SSIS and SSRS.
As an entrepreneur at heart, I founded my own company, Diazluna Corp, in 2009. In my spare time, I provide software development services to clients throughout the Chicago area, which helps me stay current with trends and technologies in software development.
Camilo Cruz: I am the CEO and co-founder of Proxima, and I specialize in e-business with a proven track record of developing digital companies and disruptive business models. My daily challenge is to combine strategy with IT innovation to tackle unique business challenges.
After founding and successfully exiting several eCommerce startups in the USA and Latin America, I took on the exciting task of leading the construction of the first full-stack insurtech in the Dominican Republic. Currently, I am focused on developing the most innovative nearshore and IT services company for the insurance and financial sector in the U.S.
