DC5: Fraud detection in financial networks

Objectives

Detecting fraud is currently one of the most important topics in Finance. However, it is also one of the most complex, given that fraudsters typically represent and generate a highly dynamic system, requiring that the boundaries and objectives of any system designed to detect and reduce fraud be constantly adapted to new extrinsic structures. This enables the definition of not only a static fraud detection system, but also a dynamic AI learning system, particularly in relation to network analysis.

Expected Results

On a meta-level, a set of Machine Learning and Artificial Intelligence models will be defined to enable a research-based approach that can be applied directly in financial institutions. The models are defined in such a way that the outcomes of the learning process within the institutions can be used to define and design new algorithms from a scientific standpoint. The work on network algorithms during the process of designing Machine Learning environments, will result in the publication of seminal papers.

Secondments (1)

Recruitment & Hosting Details

DC 5 WWU WU Vienna M9 36

6

DC 5 WWU WWU Month 9 36 months D 4.2 - 4.3

Fraud detection in financial networks (WP 4)

Objectives: Detecting fraud is currently one of the most important topics in Finance. However, it is also one of the most complex, given

that fraudsters typically represent and generate a highly dynamic system, requiring that the boundaries and objectives of any system

designed to detect and reduce fraud be constantly adapted to new extrinsic structures. This enables the definition of not only a static fraud

detection system, but also a dynamic AI learning system, particularly in relation to network analysis.

Expected Results: On a meta-level, a set of Machine Learning and Artificial Intelligence models will be defined to enable a research-based

approach that can be applied directly in financial institutions. The models are defined in such a way that the outcomes of the learning process

within the institutions can be used to define and design new algorithms from a scientific standpoint. The work on network algorithms during

the process of designing Machine Learning environments, will result in the publication of seminal papers.

Planned secondments: RAIFFEISEN, Dr. Stefan Theußl, M27, 18 months, the research exposure in a global business environment will

account for practical considerations when proposing the use of innovative methods for fraud detection

Fellow Host institution PhD enrolment Start date Duration Deliverables

6

Deliverables