Use of AI in claims management
We have already looked at the challenges in non-life insurance in a previous blog post. We now examine how insurers can strategically harness AI technologies to improve claims management. After all, AI and, in particular, generative AI is the top technology topic – including in the insurance industry. According to a study conducted by management consultancy Deloitte in conjunction with Insurtech Hub Munich in December 2024, the majority of insurance companies see the greatest potential of AI in claims and contract management. ‘Almost half of insurers (47 per cent) are currently investing in AI in these areas,’ it concludes. Efficiency gains and cost reductions are the main drivers for the use of AI.
Fast and personalised claims settlement with the help of AI
AI technologies such as large language models (LLMs) are revolutionising claims settlement by not only digitalising processes end to end, but also by enabling adaptive and intelligent decision-making. By virtue of LLMs, claims can be analysed and assessed in a fully automated way – with flexible, context-based processing. Modern systems combine natural speech processing with structured data sources. Thanks to machine learning, claims can be understood, categorised, prioritised and proactively processed in real time. Manual intervention by case handlers can be reduced, while complex or contentious cases continue to be reviewed by human expertise. Ideally, the claim can be settled automatically without any manual intervention – quickly and easily.
Insurers benefit from optimised workflows, reduced costs and increased efficiency. At the same time, faster and personalised claims settlement significantly improves customer satisfaction. The future of claims management is based on intelligent, adaptive systems that can adapt flexibly to new requirements and enable customer-oriented processing.
From document analysis to decision-making support
AI applications provide tangible support in document analysis and classification, as well as claims triage and management, and can provide decision-making support if necessary. On the basis of an LLM, the incoming documents are analysed, and structured information extracted and enriched with additional data. Combined with good claims processes, this is the key lever for automation. Based on the information contained, claims can be processed completely in the dark, tradespeople, experts or workshops can be commissioned in time, and special cases can be sent to the right case handler for assessment. AI-based decision-making aids help case handlers decide whether to obtain an expert appraisal or initiate a recourse review, for example.
Win–win situation in claims management
Fast claims processing and a high-quality 24/7 service not only promote customer satisfaction, but also free case handlers from time-consuming routine tasks. After all, customer service employees spend around 35 per cent of their time gathering information from various insurance documents, stresses Christopher Freese, Head of the Insurance Practice Group at Boston Consulting Group (BCG), in a recent interview. Easing the workload of case handlers with the help of AI also plays a key role in view of the shortage of skilled workers and the processing backlogs that have arisen at some insurers.
AI in recourse management
With state-of-the-art data analysis and AI technologies, non-life insurers can also make more efficient use of recourse management. By analysing structured and, above all, unstructured data such as emails, damage reports or tradespeople’s invoices, patterns and trends can be identified that point to possible recourse claims. This allows insurers to identify potential recourse cases at an early stage, assess them more accurately and make better decisions.
AI in fraud detection
According to an estimate by the German Insurance Association (GDV), the losses caused by insurance fraud in non-life and accident insurance amount to more than six billion euros per year. The proportion of suspicious claims is estimated to be around ten per cent. To combat insurance fraud, many insurers now use AI-based fraud detection software, which analyses vast amounts of data from various sources to detect atypical claim characteristics. For example, image analysis systems can be used to determine whether images of damage have been subsequently manipulated or have already been used in other cases. In the event of inconsistencies or anomalies, the case handlers are alerted immediately.
Long-term success through early investment in AI
In claims management, AI and automation can optimise and speed up processes, ease the workload of case handlers and significantly improve customer service. The pace of innovation with regard to the use of AI technologies in this area is high. We are following this process extremely closely and are working intensively on it. Together with our customers, we are working hard to champion AI-based applications for efficient claims management. After all, only non-life insurers that invest in AI technologies at an early stage will be sustainably successful in the long term.
