Banks, financial institutions, governments and various other types of organizations are losing billions every year due to financial crime activities such as fraud. As such, detecting and preventing fraud has, in recent years, become of paramount importance. To achieve this, there is an urgent need for advanced data management, machine learning, data analytics and AI tools to help with detecting and preventing fraud in the most efficient and effective manner by identifying suspicious activities as early as possible, raising the right alarms, and shielding financial assets from fraudulent activities.

This can be done by detecting irregular or inconsistent behaviour, by recognizing different structures referring to the same real world entity, or by uncovering efforts to conceal real identities. This can be enhanced by the ability to correlate and merge information from different sources, or by building   informative unified views of customers (or potential customers) that in turn can be used for accurate risk assessment. Central to all these options is the ability to cope with the large volume of modern financial transactions, the complexity of the financial systems, the interconnected nature of modern life, as well as the dynamic nature of real-world data that continuously evolves. Unsupervised techniques are important, since training data may not always be available or createable. Graphs play an important role towards achieving the above mentioned goals. They model data through nodes and edges (relationships), and by not adhering to specific schemas and can more easily model a vast number of different situations and networks.

This specific position is for an Assistant or Associate Professor to perform state of the art research in the exploitation of graph technologies for the benefit of financial crime prevention. This includes, but is not limited, to:

• merging and correlating information from different heterogeneous sources to build rich knowledge bases that can be analyzed in order to provide insights and accurate risk assessments;
• exploiting the evolving nature of the data to identify frequent graph patterns, or to extract evolution rules, that allow the identification of cases diverting from expected behaviour;
• developing data exploration techniques for discovering facts or interesting situations, the existence of which has been previously unknown;
• investigating the scalability and computational features of existing graph databases and graph analytic technologies;
• guaranteeing the explainability of the results produced by the developed methods. This is to be used for auditing purposes as well as for inspiring confidence in the results; identifying indirect connections, correlations, and similar situations across different parts of the graph data.

Your time will be spent at Utrecht University as well as ING. You will be encouraged to set up and guide research activities within the corresponding group, but also set up research collaborations with different divisions within the department of Information and Computing Sciences, or the university. In addition, you are intrinsically motivated to supervise MSc and PhD students, and will contribute to the department's teaching curriculum. At ING, we would like you to actively participate in the activities of the Analytics for Financial Crime group, apply the solutions developed at the university on ING real data and offer new techniques for fighting financial crime.

We are looking for a driven and versatile Assistant/Associate Professor with excellent communication skills. You also have:

• a doctorate degree in data management, advanced analytics, machine learning or a closely related field;
• a proven track record and potential in his/her own field of expertise;
• the willingness and motivation to obtain the university teaching qualifications;
• previous experience with supervision of BSc, MSc and PhD students;
• a strong desire to develop and materialize state of the art, robust, practical solutions.

• an appointment at the level of Assistant or Associate Professor, initially for a period of 5 years. The position will be subject to a mid-term evaluation after approximately 2.5 years and an end-term evaluation. Following a positive evaluation the position will become permanent after 5 years;
• the gross salary – depending on previous qualifications and experience - ranges between €3,746 and €6,940 (scale 11-14 according to the Collective Labour Agreement Dutch Universities (cao)) per month for a full-time employment. Salaries are supplemented with holiday bonus of 8% and a year-end bonus of 8.3% per year;
• a pension scheme, collective insurance schemes, partially paid parental leave, and flexible employment conditions based on the Collective Labour Agreement Dutch Universities.

In addition to the employment conditions laid down in the cao for Dutch Universities, Utrecht University has a number of its own arrangements. For example, there are agreements on professional development, leave arrangements and sports. We also give you the opportunity to expand your terms of employment yourself via the Employment Conditions Selection Model. This is how we like to encourage you to continue to grow. For international employees the university offers help with finding housing, child care and schools, as well as a partner programme and a Dutch language course.

More information about working at the Faculty of Science can be found here.

The department of Information and Computing Sciences is nationally and internationally renowned for its fundamental and applied research in computer science and information science. In our constantly changing (digital) society, the department of Information and Computing Sciences is continually looking for new, realistic ways to push the boundaries of both science and social applications. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. We collaborate extensively with partners inside and outside the university, including in the focus areas of Applied Data Science, Human-centered Artificial Intelligence, and Game Research.

The department provides the Bachelor's programmes in Computer Science and Information Science and six English-language Research Master's programmes in these areas.

At the Faculty of Science there are 6 departments to make a fundamental connection with: Biology, Chemistry, Information and Computing Sciences, Mathematics, Pharmaceutical Sciences and Physics. Each of them is made up of distinct institutes which work together to focus on answering some of humanity’s most pressing problems. More fundamental still are the individual research groups - the building blocks of our ambitious scientific projects.

Utrecht University is a friendly and ambitious university at the heart of an ancient city. We love to welcome new scientists to our city – a thriving cultural hub that is consistently rated as one of the world’s happiest cities. We are renowned for our innovative interdisciplinary research and our emphasis on inspirational research and excellent education. We are equally well-known for our familiar atmosphere and the can-do attitude of our people. This fundamental connection attracts Researchers, Professors and PhD candidates from all over the globe, making both the university and the Faculty of Science a vibrant international and wonderfully diverse community.