The Innovation Lab at Kronofogden (Swedish Enforcement Authority) chose Cybercom as a partner to develop the next generation of tools for investigation, analyzing data, and improving decision-making. The goal was to improve the ability for enhanced debt collection. This initiative derived from the assignment of a future AI-powered workspace.
The project originated from exploring the latest technologies and collaborative trends from an outside-in perspective.
Today’s situation involved manual work in several systems, depending highly on the experience of each employee. Manual work and business support, based on old technology, make analysis and debt collection complicated and time-consuming.
Collaborative aspects and synergies between different cases were challenging.
Without consolidating internal information with data from external sources, important insights were impending.
The solution was a cloud-based system to process and analyze external data in order to make fast and sound decisions. A new way to collect, process and present data based on features such as; machine learning, for analyzing large amounts of unstructured information; automation of data collection and; notifications for presenting the right information at the right moment. This resulted in more accurate decision making when it comes to debt collection.
“When combining internal and external data in a systematic way, new insights and a more accurate view of potential assets to collect. I believe this agile approach is the future for having successful investigations, due to the ever-growing complexity of digitalization in society”
An employee at Kronofogden specialized in organized economic crime investigations
The selected work method for this assignment was to deliver Minimum viable products, MVPs. Each MVP was well defined in order to simplify legal and technical aspects, making the full-scale implementation stage easier.
By working in this lean and agile way, the organization could participate and collaborate with the development team, resulting in deliveries on a weekly basis. The solution allowed Kronofogden to understand the potential of machine learning and how multiple processes and decision points can be improved and automated.