4 concrete ways AI can optimize your claims management

Claims management is at the heart of insurance. It's what customers pay premiums for. Multiple contacts, loss of information, complex user journey... there are many frustrations that technology can help solve. Beyond the buzzword, how to concretely leverage Artificial Intelligence (AI) to improve your customer experience.

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First of all, is it actually worthwhile investing time and effort on claims management optimization? According to a study conducted by Accenture, 71% of consumers expect claims management to be fast, efficient and smooth, but most importantly digitized. Obviously, this is no scoop to brokers. It is therefore not surprising that 74% of them are now banking on AI to develop more efficient, reliable and human-oriented processes. In fact, 79% of claims management professionals believe that this technology will have a significant impact on the entire value chain by limiting customer trips to the store, lowering call rates, reducing file processing times and assessing claims more accurately. 

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In short, if some aspects of claims management will always remain Human tasks, AI offers many opportunities for innovation. Here are four examples.

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Automate all low-value tasks

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Automation is the lifeblood of AI. It allows to industrialize many time-consuming and repetitive actions, but also to provide more accurate and reliable analysis and information processing. What is also known as "intelligent automation" seems to have already been well adopted by the insurance industry: according to a McKinsey report, more than half of all claims management activities are now automated.

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In fact, using automation allows you to:

• Create automatic end-to-end workflows: the objective here is to be able to standardize some of the recurring actions that you carry out as part of your claims management. For instance: categorization of supporting documents, the detection of specific contractual clauses, email reminders, the sharing and storage of information, the allocation of claims between managers, the management of contracts and validation processes;

• Reduce human errors and oversights: let AI take care of verifying IDs, medical certificates and limits for you. Automation is also useful to significantly reduce the error rate and thus avoid, among other things, undue payments;

• Set up automatic, real-time notifications to your customers to keep them informed of the status of their case and the next steps to come, such as the intervention of a repairer or the payment of compensation;

• Centralize all your claims management in one place. 

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There is a wide range of automation solutions available today: Cognigy, for example, improves the flow of conversations with your customers with a centralized automated management platform that drives your virtual agents. UiPath, on the other hand, takes a "Robotic Process Automation" approach that can address many of your challenges, such as customer processing efficiency and reducing human error rates.

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Automation is therefore your ally in reducing costs and maximizing efficiency, while freeing up more time for your customers. Time that can then be reinvested in the management of more complex claims, where human intelligence is essential. 

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Putting intelligence in your data

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Capitalizing on collected data is another promise of AI. Using, analyzing and consolidating data gives you the means to extract the intelligence needed to better understand your customers' profiles. So, by leveraging your data through tools like those devised by Data Axle, you'll be able to offer your customers tailored programs and experiences, and help them reduce their claims risk - a boon when you consider that personalization is one of the consumer's prerequisites in any claims journey. 

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And this is far from being the only added value: AI, gives you access to real-time contextualized data, valuable for decision support. That way, you are able to solve complex problems quicker or address a large number of requests simultaneously. It is also an excellent tool for optimizing your underwriting process: thanks to a data-driven strategy, you can efficiently evaluate the profile of potential customers and develop programs adapted to their needs. 

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Embrace Natural Language Processing

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To get the most out of your data, you can go even further and leverage "Dark Data", which is unstructured data, data found in handwritten documents, data shared during telephone conversations or data contained in emails. This data, which usually escapes you, is nevertheless - as Accenture reminds us - a manna of strategic information. How can you exploit it? With the help of Natural Language Processing (NPL), an AI-based technology capable of transforming text or audio data into structured and intelligible information. In addition, NPL adds a human-like "sentiment" analysis layer to the data, allowing you to determine whether information is positive, negative or neutral. 

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Integrated with your claims management, the NPL facilitates the collection of information exchanged with your policyholders during telephone calls. This information is analyzed and injected directly into your clients' files, without the need for call transcription or any manual action on your part, using solutions such as the one developed by Datasaur. Here the capabilities of the technology are particularly useful for understanding and transcribing complex audio conversations.

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This technology can also be coupled with chatbots - like the very recent ChatGPT developed by OpenAI - in order to improve the understanding of customer requests and to offer a more relevant user experience. This is what some insurance companies are doing, using virtual assistants that can understand NLP and quickly authenticate a user. Others are using NPL to assess customer sentiment in email exchanges. 

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Fight fraud

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Since the pandemic, the insurance world has seen an upsurge in fraud, especially in claims reporting. Fortunately, AI is also a formidable weapon here. NLP, for example, has a role to play: this technology scrutinizes masses of unstructured information and detects possible suspicious customer behavior. This is a complex analysis through which managers can identify a significant number of frauds more quickly. 

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You can also use speech analytics technologies, which are powerful enough to analyze a customer's voice in real time during telephone conversations and distinguish elements of language and behavior that betray a potential fraud attempt. 

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Some insurance companies are developing predictive models based on Machine Learning. Here, the objective is to capitalize on different cases of fraud identified beforehand to extract "typical models" that are analyzed and memorized by the machine. Thanks to advanced learning, the technology is then able to recognize new fraud patterns similar to the typical models. This is an effective way to prevent fraudsters from getting away with it. 

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Simplifying the work of employees, strengthening expertise, and satisfying customers: this is the winning equation of AI applied to claims management… maybe something to consider.  

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