Data standardisation is the backbone of automation

AI will have a significant impact on the insurance industry and will help to address operational challenges. Recent projects in the UK insurance industry, however, show how challenging it can be to automate with AI. Without a solid data foundation, even sophisticated technologies and skilled professionals will struggle to deliver meaningful results.  

The sudden rise of artificial intelligence has led many insurance companies to rush to implement AI-based solutions. Several use-cases have highlighted the importance of addressing fundamental requirements before getting started. One of the main challenges in a recent AI project in London was that data management could prove to be an obstacle. In our experience this is not an isolated issue in the insurance industry. Even well-prepared companies run the risk of underestimating the actual requirements of AI readiness.  

Finance automation in the insurance industry is progressing, but it is still behind other areas, largely due to legacy systems and regulatory complexity. Insurers have begun to automate key processes such as accounts receivable and payable, expense management and financial reporting, but it is being done on a fragmented basis leading to inconsistent data quality and inefficiencies that undermine the full potential of automation.  

An illustrative example of the challenges faced in implementing AI in finance automation comes from a series of projects undertaken by organisations in the UK insurance sector. These projects aimed to automate parts of their finance operations to improve efficiency and accelerate key business outcomes, such as improving cash flow through better debt management and optimising resource utilisation. At first glance, the combination of a suitable AI technology with a degree of human oversight seemed sufficient to streamline these workflows. The AI tool seemed to be promising, and all participants expected an easy transition to automation. However, real-life implementations quickly revealed that this approach was not sufficient. 

Automation did not deliver the expected results 

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.  

Integration of technological innovation and human expertise 

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. 

Małgorzata Kaczmarek, Business Specialist at Sollers Consulting

Technology in Insurance: Facts and Comments

Karin Lindström in Computersweden reports about a survey among businesses in the Nordic countries:  

“47 per cent of IT projects are on budget and 51 per cent are completed on time. In addition, only 60 per cent of IT projects are deemed to be of sufficient quality.” 

https://computersweden.se/article/3976194/bara-halften-av-alla-it-projekt-klarar-budget-och-tid.html 

IT transformation is critical for insurers, so underperforming in IT delivery impacts their costs, customer experience and growth. Effective delivery of IT programmes is key to long-term competitiveness. Still, many insurance IT programmes in the Nordics would benefit from leaner programs that deliver value quickly and optimize for attractive TCO. It is often not the case, so we see huge potential to improve effectiveness.

Slawek Gdyk , Head of Nordics at Sollers Consulting

Maximilian Sachse writes in Frankfurter Allgemeine Zeitung about an initiative of Brad Smith, Vice Chair & President of Microsoft, to increase the European datacenter capacity by 40% over the next two years. 

“In view of the geopolitical volatility, Microsoft is aware that European governments are thinking about cloud alternatives, writes Smith, possibly also with state support. ‘We recognise the importance of a diverse ecosystem and are committed to working with European participants across the ecosystem,’ writes Smith.” 

https://www.linkedin.com/posts/bradsmi_microsofts-support-for-europe-always-has-activity-7323272844841308160-kv8e?utm_source=share&utm_medium=member_desktop&rcm=ACoAABOj1fwBcGr7Z3ltSpMUieWS0Exgn_XwwTg 

https://www.faz.net/aktuell/wirtschaft/unternehmen/mehr-rechenzentren-microsoft-kuendigt-grosse-europa-expansion-an-110447766.html 

As the market for cloud services matures, multi-cloud will become a preferred option for many insurers. In a multi-cloud approach, an insurer uses services from two or more providers in parallel. Multi-cloud is becoming the preferred option for two reasons: the first is compliance with regulations. The second point is the increased agility that comes with a multi-cloud strategy.

Dominik Kamiński , AI & Cloud Lead at Sollers Consulting

Figure of the month

*The amount of synergies the Finnish insurer Sampo plc estimates to achieve through the full acquisition of Topdanmark due to strong expansion of digital sales, a 55 m€ increase from the original estimate one year ago

https://www.sampo.com/globalassets/investors/quarterly-reporting/2025/q1/investor_presentation_q1_2025.pdf