When it comes to processing insurance claims, speed is important. In surveys conducted by Sollers and Ipsos, 52% to 63% of customers stated that a quick response is one of their top three expectations following a claim, even more important than the actual amount of compensation. Given the increasing frequency of natural disasters, the industry is struggling to respond. Can artificial intelligence (AI) solve the problem of claims automation?
Transforming insurance claims with help of AI
What is ai insurance claims processing?
Insurance companies manage risks by spreading them across a large number of customers and distributing a sudden financial impact over a long period of time. After major loss events such as floods and storms, it sometimes takes years for a claim to be fully settled. Normally it is done manually which comes with long processing time and possible human errors.
AI insurance claims processing involves the use of advanced technologies like machine learning (ML) and natural language processing (NLP) to automate and optimize the workflow of handling insurance claims. Instead of relying on manual methods, which are often slow, error-prone, and resource-intensive, AI streamlines the process by analyzing large amounts of data quickly and accurately. This ensures that insurers can process claims faster, detect fraud more effectively, and deliver better customer experiences.
Why insurance companies use AI for claims automation
To strengthen customer relationships, insurance companies have started to invest in claims automation. Artificial intelligence (AI) is playing an increasingly important role in this. Research by Oliver Wyman stated that 25% of insurers planned to have generative AI solutions in production by the end of 2023. According to a Sollers database, around 20% of AI investments in the insurance industry are in the claims area. Artificial intelligence is used in many parts of the claims handling process, including fraud detection, claims assessment and triage, contract analysis and subrogation. There are three goals that insurers want to achieve with the help of artificial intelligence and claims automation:
- increase efficiency and effectiveness,
- accelerate,
- expand the possibilities of the claims handler.
How to properly approach implementation of artificial intelligence in insurance claims handling
The importance of AI in claims settlement is likely to increase as the technology develops and companies become more experienced in using it. However, artificial intelligence will never function as a magical toolbox that suddenly transforms manual labour into a fully automated solution. It is a piece of the solution, but it will never be the solution itself. The most important part of automating claims handling is achieved by following a series of simple if-then rules and translating them into business processes. To achieve this, four steps must be followed:
- analysing the existing business processes and rules,
- prioritising the micro-processes with the greatest leverage,
- analysing and evaluating the existing tools and measures,
- developing a new operating model.
The key to a successful automation project is a clear understanding of the objectives. If no common goals are pursued in a project, failure is inevitable. This is why it is so important to discuss them. The organisation should come to a common understanding of the needs and goals and how to achieve them. Following this rule, automation does not necessarily have to be difficult to implement. Bigger does not always mean better.
Which parts of claims processing can be automated with AI
AI can be used in various areas of the claims handling process that follow after the first notification of loss (FNOL). It can provide a deeper understanding of incoming documents. AI that is trained on damage analysis can also analyse photos. These are the first steps to assess and classify claims. They provide the data necessary for claims triage. But AI can be implemented in other parts of the claims handling process such as fraud detection through the use of internal and external data as well as in subrogation.
All these microprocesses in claims handling can be supported or driven by insurance employees. In a mature automation strategy the role and the degree of involvement of employees can be calibrated according to customer preferences, lines of business and claims severity. It helps human agents to become faster and to solve ongoing tasks in better quality. AI in insurance claims handling helps claims handlers to better focus on what really needs their attention.
Technologies that make AI great fit for claims automation
The most common AI based technologies used in insurance claims handling are Optical Character Recognition (OCR) as well as Natural Language Processing (NLP). But there are more AI solutions used that are trained on individual datasets and/or industry datasets to recognise fraud patterns and to automatise subrogation.
Choosing right tool enabling claims automation with AI
The choice of tools should be based on the individual business situation. Sometimes a company needs rule-based claims automation tools for simple, repetitive processes, but in other cases comprehensive, end-to-end automation is required. Zero-touch claims processing, for example, can only be achieved with great effort. To better support insurers in their quest for claims automation, Sollers has forged technological partnerships. Let us give you a brief overview of why we chose them.
- Camunda offers an established platform that enables insurers to orchestrate business processes in a scalable and intelligent way.
- Appian provides an automation platform with a complete tool stack to design, automate and optimise complex business processes.
- Hyland delivers robust and efficient solutions for document management, governance and automation.
- Omni:us provides an AI-powered tool to make fast, transparent and insightful data-driven claims decisions, with a particular focus on low-value, high-volume claims.
It’s all about the right utilisation of the right tool in the right place. But that’s no guarantee of success. In our experience, automation is much more about people, customers and employees than it is about tools and artificial intelligence. If you plan to automate, don’t just think about the tools, think about the processes and be prepared to change the mindset. Make sure your organisation is ready to take full advantage of the benefits that automation can offer.
What stops insurers from transformation
Claims automation requires a change in the mindset of the people in charge and support in rethinking and reorganising processes. In our projects, we find that the biggest challenge in claims automation is not incompatible or outdated tools and systems, but a lack of collaboration between business and IT staff. Before you start restructuring the organisation, you need to make sure that both sides are listening to each other and are ready for change. Communication is the key.
How Sollers can help
Sollers experts can help insurance companies to design and set up the IT architecture that enables the use of AI in claims handling. They support companies in analysing and understanding AI tools and provide trainings. Typically, Sollers expertise is used to analyse existing business processes to identify opportunities and realize the possible benefits of AI automation. It is helpful to analyse available AI tools to understand whether they fit into given processes and production environments.
In the implementation process Sollers experts can design and plan AI automation projects and get a better understanding of costs and benefits of AI in claims. They don not only Manage the transformation roadmap but also Redefine business processes, Configure AI tools and Implement changes to insurer IT core and front-end systems.
Authors of the article
Rafał Karwowski – Consultant
Lennart Imorde – Head of Process Automation