Bayan Bruss

Eren Kurshan, Dhagash Mehta, Bayan Bruss et al.

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of generative AI, which has empowered criminal activities ranging from sophisticated phishing schemes to the creation of hard-to-detect deep fakes, and to advanced spoofing attacks to biometric authentication systems. The exploitation of AI by criminal purposes continues to escalate, presenting an unprecedented challenge. AI adoption causes an increasingly complex landscape of fraud typologies intertwined with cybersecurity vulnerabilities. Overall, GenAI has a transformative effect on financial crimes and fraud. According to some estimates, GenAI will quadruple the fraud losses by 2027 with a staggering annual growth rate of over 30% [27]. As crime patterns become more intricate, personalized, and elusive, deploying effective defensive AI strategies becomes indispensable. However, several challenges hinder the necessary progress of AI-based fincrime detection systems. This paper examines the latest trends in AI/ML-driven financial crimes and detection systems. It underscores the urgent need for developing agile AI defenses that can effectively counteract the rapidly emerging threats. It also aims to highlight the need for cooperation across the financial services industry to tackle the GenAI induced crime waves.

Mayuka Jayawardhana, Nihal Sharma, Kazem Meidani et al.

Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label prediction on the tabular data format surpassing the historical successes of tree-based models. This has led to investigations on their applicability to forecasting time series data which can be formulated as a tabular problem. While recent work to this end has displayed positive results, most works have limited their treatment of multivariate time series problems to several independent univariate time series forecasting subproblems, thus ignoring any inter-channel interactions. Overcoming this limitation, we introduce a generally applicable framework for multivariate time series forecasting using tabular foundation models. We achieve this by recasting the multivariate time series forecasting problem as a series of scalar regression problems which can then be solved zero-shot by any tabular foundation model with regression capabilities. We present results of our method using the TabPFN-TS backbone and compare performance with the current state of the art tabular methods.