With advancing technologies simple data manipulation can now be carried out by a large number of people. This lead to a situation where fraud methodology is steadily improving and its occurrence is part of every day business. Furthermore it has extensive consequences especially in the banking and insurance sectors. These have an ever increasing demand for reliable fraud detectors to prevent unnecessary expenditures.
The tool sets, which are presently available, facilitate an environment in which fraud attempts can be very diverse. Simple efforts may involve submitting a previous insurance request or inventing new parties to generate the case at all. However, more advanced methods can be applied to manipulate images for an increased insurance premium or attack a credit card transaction to reroute money transfers. Very often the recognition of these cases is difficult to be carried out or only happens after a certain delay. This can mainly be attributed to two reasons: (1) The data to properly assess the case is
not readily available to employees or relevant systems (2)
Data tampering can be very well made and almost impossible to detect, either by employees or by internal systems. This leaves a large gap in methodology to cover these use cases.
Machine learning techniques can be immensely advantageous in this context. They help accelerating and improving the detection process in two ways. Firstly, they facilitate an environment in which incoming as well as historical data can automatically be categorized and analyzed from very diverse channels. In this way databases can be properly maintained to boost data integrity, availability and accessibility system wide. Furthermore, this environment fosters a setting in which data lakes can be build and grow to also advance future machine-learning models. Secondly, state-of-the-art deep learning models can generate completely new tools and respective capabilities. Information of very different sources, like the descriptive text of a request, its attached images or the history of a client, can simultaneously be used to increase the
fraud-detection accuracy. In addition, latest architectures based on deep-convolutional neural networks or approaches via
reinforcement learning demonstrate the ability to effectively distinguish between tempered and authentic images.