Corruption and billing fraud in the German healthcare system cause annual damage of around 14 billion euros for the community of solidarity. In order to uncover fraudulent networks, mass data such as e-mail or telephone traffic must be examined in cases of suspicion. This makes investigations resource-intensive and slow.
Our project »Criminal Networks: Combating Billing Fraud and Corruption in Healthcare« is researching the traceable identification of anomalies in fraud networks using artificial intelligence (AI).
The project is part of the initiative »Artificial Intelligence in Civil Security Research« of the German Federal Ministry of Education and Research (BMBF). In this project, we are developing a so-called »weak artificial intelligence« for investigative authorities and health insurance companies that combines algorithms for detecting anomalies in fraud networks with the domain knowledge of the users.
»Weak« is the name of the AI because the goal here is not to replace human intelligence, but to support it with learning algorithms. A weak AI is only capable of performing concrete tasks whose solution it has learned before. Coupled with expertise of the investigating persons, the hybrid approach helps to facilitate the research.