AI in Insurance Finance: Hype vs. Reality

There were multiple objectives across the different projects, but they all shared a common overarching goal. It was to introduce partial or full automation of routine communication tasks triggered by specific account-related events and to apply AI models to improve the alignment of incoming financial data with existing records by using a combination of system information and supporting documentation.

Initially, it appeared that the right AI tools combined with a reasonable degree of human oversight would be sufficient to streamline several processes in this area. The technology seemed to be mature, and expectations were high for a smooth transition to automation. However, real-world implementation quickly revealed the limitations of this approach.

While the system architecture and AI logic were set up correctly, and human involvement was planned to handle exceptions, the automation often failed to deliver the expected results. Interestingly, the core problem was neither the AI algorithm nor the human input. The true obstacle was the data itself.

The initiatives uncovered a critical, often underestimated factor in finance automation: data quality and structure. The AI model struggled not because it lacked the intelligence to make matches, but because the input data was inconsistent, incomplete, or unstructured. Payment references did not follow a standard format, invoice numbers were missing or mismatched, making it difficult to create reliable automation rules.

The experience highlighted a key lesson in finance automation: having the right technology and human support is only part of the equation. Without clean, structured, and standardised data, even the most advanced AI solutions will underperform. The data management aspects of the business need to evolve into a standalone project, rather than being just a supporting component of the AI initiative.

Data correctness and standardisation are the backbone of any successful automation initiative, so organisations need to prioritise data governance, harmonisation, and validation efforts alongside their investment in automation tools.

In summary, finance automation is a comprehensive effort. It’s about empowering finance teams with tools that increase efficiency, accuracy, and speed, but it also requires new skills, such as digital literacy, data analysis, and strategic thinking. The most successful automation efforts integrate technological innovation and human expertise, creating a smarter, more agile finance function. However, a critical component that often determines the success or failure of these efforts is the quality of the underlying data. Clean, well-structured, and consistent data is essential for finance automation to work properly and deliver reliable results. Without a solid data foundation, even the most sophisticated technology and skilled professionals will struggle to deliver meaningful results. Investing in data management and governance is therefore just as important as choosing the right tools or building the right team to enable successful finance automation.