Guest Blog - Roman Swoszowski, VP, AI and Cloud R&D at Grape Up
By implementing Artificial Intelligence into claims adjustment software, the insurance enterprises are enabled to gain new quality in claims management and achieve impressive operational savings. To utilize AI-drive applications that accelerate and automate their processes, insurers have to use innovative yet proven patterns in delivery impactful software. While these use cases are so valuable, why are there still common challenges with using AI in the insurance industry?
The insurance enterprises have been implementing and developing numerous software products that were meant to provide customer-friendly experience and help with automating repetitive tasks. And while the world’s top insurers are the leaders of digital transformation, there is still a lot of value that AI technologies can bring to their business operations. This article showcases the main tasks that can be performed by AI-enabled applications and, on the other hand, explains the reasons triggering common struggles in implementing AI in insurance.
Claims Adjustment Process - What Can Be Improved?
As claims management teams declare, the amount of work required to be processed manually often leads to inefficiency. Furthermore, an overwhelming sum of tasks may cause mistakes that impact customers' lives. This kind of job, while done manually, cannot be scaled up. Here comes AI.
AI-driven solutions are designed to automate, simplify, and speed up the process of claims handling, which leads to increased customer satisfaction and cost savings in operations. AI-based applications are extremely effective in collecting and processing claims data, as well as verifying and analyzing them. With the help of AI, customer experience can be improved on many levels.
First, before the process even starts, many of the claims are wrongly delivered, as customers often find it difficult to get the right department responsible for the issues they face (according to the industry reports, it may be even 35% of all claims). Some of them are sent to generic, corporate, or marketing inboxes, and many more to wrong people and offices. AI algorithms are fluent in analyzing the subject and content of a message to monitor keywords and analyze context to find the proper address and redirect the email. What’s also highly valuable to the pace the claim is processed, such a solution can detect if it is a new case or a missing attachment of an open process, and in situations like that, forward the email to the correct recipient.
Incoming claims processing
As customers and insurers can communicate in various ways using numerous channels, AI-enabled tools help to organize all the important information, extract the details from different claim reports, and verify all required attachments and data. It’s a huge upgrade from manual work to automate processing with serious time savings, especially for established companies operating at scale. By implementing these solutions, enterprises not only reduce time and costs but also eliminate unappealing and repetitive tasks that often lead to employee burnouts.
When these time-consuming parts of the process are done by applications, claims management professionals can focus on more valuable duties, such as ensuring that customers are well-informed about the process.
After collecting all the information and data, we are approaching the crucial part of the process - verifying reported damage. Here may occur a few issues, and insurers have to verify if reported damage is true and happened because of a reported event. AI-powered software utilizes computer vision to process and analyze attached photos (used mainly when it comes to car insurance) and satellite pictures of properties.
Among many challenges the insurance industry faces, effective fraud detection is the key to prevent financial losses and to build a secure system. AI applications work well with huge data volumes, comparing documents, and looking for patterns. These applications verify if estimated costs are real in comparison with actual invoices and are able to easily indicate costs of services not included in an insurance policy, inflated rates, and excess medical treatment costs. With such effective support, insurers save millions of dollars yearly and improve the verification of new customers.
Well-known advantages, issues with implementing AI to production
Most of the cases described above are well-documented, and while some insurance companies thrive using AI to enhance their business operations, AI implementation in the industry looks poorly. According to Gartner CIO Report (2018 -2020), only 19% of insurance companies deployed AI-enabled applications to production. Yet there is visible progress (in comparison to 4% in 2017), the adoption of AI is not satisfying.
When diving more into details, especially taking into account that despite declaring the broader implementation of AI, insurers struggle in leveraging its technologies, we can indicate the main challenges. As the industry leaders declared in Gartner Research Circle Members Survey 2019, these issues are caused by the lack of understanding AI advantages and use (42% of surveyed leaders) and current staff skillset (56%) that need to be improved. Companies have problems with translating these high-level AI use cases to their specific challenges and, therefore, cannot clearly see how AI can enhance their businesses. People who want to implement these solutions have issues with presenting generic, costly ideas to the decision-makers.
While these obstacles seem serious, pressure to transform the industry, reduce costs, and increase customer satisfaction, along with the changes triggered by COVID-19 obligate insurers to accelerate their AI implementation plans. In practice, to overcome the issues highlighted by the reports, they have to choose between building in-house expertise or collaborating with consulting and technology companies specializing in helping enterprises embrace the latest technologies, including AI. What is crucial for the success of such collaboration, enterprises on a lookout for partners should equally value potential partners' skills in Data Science as well as their experience in delivering high-quality software. Some ineffective experiences come from working with teams excellent in Data Science but less proficient in developing critical software that has to solve real cases at scale. To design a team that delivers robust, scalable AI-powered applications, enterprises need both Data Scientists and Software Engineers fluent in developing modern software.
The most challenging part of the AI implementation comes to moving from prototyping a solution to developing it in a production environment. As Data Scientists specialize in designing AI solutions, they need to collaborate with Software Engineers proficient in running modern applications. Even the most innovative and powerful prototypes cannot work in production without proper management of the technology stack, which is needed to operationalize AI-enabled apps.
While there is no proven receipt for success as the AI technology obligates enterprises to customize solutions, some basic rules are describing the successful approach to productionizing AI-driven applications. First of all, start small – begin with a single solution rather than company-wide change and improve small case with AI. Furthermore, use a pilot project to help your team learn and adapt. But keep in mind setting realistic expectations – implement AI where it provides business value, don’t treat it as a magic formula to solving all your problems. Along with that, don’t hesitate to move to productionize your solutions. Prototyping is crucial, but the real value comes with running applications. Finally, work and progress incrementally by continuously developing products and features.
By leveraging AI to improve claims management, insurance enterprises can build a sustainable competitive advantage and embrace new business opportunities, but the process requires determination both in prototyping and in operationalizing AI-driven software.
Roman is responsible for developing the overall technology vision of the company with focus on artificial intelligence, deep learning and cloud native technologies. With almost 15 years of hands-on experience in the IT industry, he drives the company’s technology strategy and works closely with engineering teams to ensure continuous delivery of innovative software solutions.
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