Artificial Intelligence is no longer a buzzword in insurance; it is the engine of the sector’s next wave of growth. In 2024, 91% of insurance companies were either already investing in AI technology or planning to do so within the next five years. This urgency is driven in no small part by customers who expect claims to be settled as swiftly and effortlessly as an online purchase.
Despite the benefits of AI, many organisations are still falling behind in fully automating claims processing. 40% of organisations claim that data-related challenges remain one of the main obstacles. The key may lie in solutions like a logical data management (LDM) platform, which can be leveraged by companies into a strategic asset, enabling insurers to detect fraud earlier, settle faster, and build the friction-free experiences which customers now expect.
The data dilemma that constrains automation
A typical insurer sits on petabytes of structured and unstructured data; from customer profile and policy details, to claims histories and third-party feeds such as motor-vehicle registries or medical-billing platforms.
Each new line of business tends to add another data silo, often in a different cloud, or ageing mainframe – leading to fragmented data spread across multiple systems and formats. The result can be latency, duplicated effort, and all too often, dissatisfied customers. A report by Accenture showed that 60% of claimants were not satisfied with traditional insurance-claims handling processes, citing speed settlement issues as a main reason. Even the most sophisticated neural network cannot make instant, accurate decisions if its inputs arrive in batches, or live behind incompatible firewalls. Without unified, high-quality data, AI models will deliver inconsistent and unreliable results, leading to delays, errors, and missed opportunities for operational efficiency.
What logical data management does differently
Logical data management (LDM) offers a way out of the bottleneck. Instead of physically moving or duplicating data, logical data management creates a unified, virtual layer that seamlessly connects disparate data sources, presenting a single, trusted view in real time. Think of it as a universal translator: policy systems, data lakes, SaaS apps and partner APIs all keep their autonomy, while downstream users and AI services experience them as one logically consistent source. Crucially, security and lineage travel with the data, so compliance teams retain full visibility.
This approach allows insurers to access, analyse and govern data efficiently, regardless of whether it resides on premises or in the cloud. This also provides AI models with context rich, up-to-date information across enterprises. By enabling a secured, governed and agile data access, logical data management enhances the accuracy of AI-driven claims automation and supports compliance with regulatory requirements.
How LDM supercharges the claims value chain
I) First notice of loss (FNOL): A machine-learning model can immediately cross-check for a claimant’s policy, driving licence status, and recent weather patterns.
II) Triage and fraud detection: Natural-language processing parses adjusters’ notes the moment they are saved, flagging sentiment shifts or suspicious patterns while evidence is still fresh.
III) Customer service: Generative-AI (GenAI) assistants embedded in call-centre workflows can pull up a complete, rights-filtered claims file in seconds, guiding agents through the next best action.
Proof in practice
Logical data management was an important solution for Alexforbes, a leading South African financial services firm. The company – which manages more than a million pension fund members –needed a solution to solve data fragmentation and GenAI implementation challenges. The company relied heavily on power BI for broker and member contribution calculations but struggled with seamless integration and real-time data access.
To overcome these problems, Alexforbes implemented a logical data management platform which resolved governance issues, eliminated siloes, and enabled secure, real-time data access across platforms. As a result, real-time financial calculations improved broker and member contribution management, reducing overall operational delays. Natural language querying also simplified data retrieval, making critical information accessible even to non-technical users.
The future of claims processing
In 2023, Time Magazine predicted that AI progress is unlikely to slow down, and with the industry projected to increase in value by over 5 times in the next five years, insurers will need to innovate to stay competitive.
The ability to quickly and securely harness data will remain a key differentiator, and logical data management provides the foundation for this agility. Organisations need to be data-ready, and with a robust data management strategy, they will be best positioned to meet evolving policyholder expectations and maintain a competitive edge. The future of claims processing is automated, intelligent and most importantly – built on a foundation of trusted data.
