AI agents streamline business analysis at Savings Bank

Assessing corporate customers’ financial situation requires gathering and analysing information from many different sources. Savings Bank set out to streamline this work with AI agents in collaboration with Fluentia. The result was AINO business analysis solution, which helps compile and analyze information to support experts in their work and decision-making.

The AI agent-based solution reduces experts’ manual information retrieval and helps form a more comprehensive overall picture to support decision-making. At the same time, the collaboration lays the groundwork for broader use of AI in the bank’s business operations.

The need for which a solution was sought: Streamlining information retrieval and analysis

Assessing corporate customers’ financial situation requires diverse information about the company’s business, financial position and liquidity. Information is available from many different sources, but compiling and analysing it has traditionally required a great deal of manual work from experts.

At Savings Bank, the goal was to streamline information retrieval and analysis with AI and thereby free up experts’ time for evaluating information and for actual decision-making. At the same time, the bank wanted to develop the use of AI more broadly as part of its business processes.

In the strongly regulated operating environment of the financial sector, the solutions had to fit the bank’s existing technical environment and take the industry’s special requirements into account.

Our solution: A solution based on collaboration between multiple AI agents

We implemented the AINO business analysis solution for Savings Bank, which uses an operating model based on collaboration between multiple AI agents that makes it possible to process information from different sources and use it to prepare business analyses. The solution helps form a more diverse overall picture of the company under review. It also brings the information sources behind the analysis into the expert’s view. The user can give feedback and request clarifications, based on which the AI agents complement and revise the analysis content.

The solution was implemented to fit Savings Bank’s operating environment and business needs. Development has also taken into account the information security and regulatory requirements of the financial sector.

AINO supports the work of experts. Assessment and decision-making always remain a human responsibility.

Cooperation: Business-driven and agile development

The idea and initial concept for the AINO business analysis solution emerged during an AI Hackathon organized by Fluentia, which was also attended by Savings Bank’s Chief AI & Productivity Officer, Jukka Palosaari. During the hackathon, Savings Bank’s business expertise and Fluentia’s AI expertise were brought together to develop the idea and create an initial prototype. The preliminary solution developed during the day showed promise and laid the foundation for further development collaboration.

From the outset, the collaboration has emphasised business needs and practical development. Savings Bank’s business representatives have worked directly with Fluentia’s development team, which has enabled rapid dialogue and flexible refinement of solutions as the work progresses.

Thanks to an agile and iterative operating model, development work has been able to move quickly, evaluate implemented features together and respond to new needs without heavy intermediate stages. Close collaboration has helped ensure that the technical solutions serve the everyday work of the bank’s experts and the bank’s business goals as well as possible.

The aim is to continue leveraging the opportunities of AI in other business needs of the bank as well, and to develop ways of working in which experts’ competence and the automation offered by AI complement each other.

“The rapid collaborative experiment at the hackathon demonstrated both Fluentia’s strong expertise and the effectiveness of a new way of working in practice. An agile approach and straightforward interaction made the collaboration smooth from start to finish. The experience also reinforced our view that the same operating model can be used to build even more impactful solutions going forward,” Palosaari says.