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.

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the modern payment network systems, which can generate millions of transactions per day across a large number of global institutions. Training a detection model of fraudulent transactions requires not only secured transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .

As the rate of crime rises, the utilization of existing Artificial Intelligence algorithms is proving to be extremely beneficial. To a large extent, it helps in the prediction of crime and the criminal. Fremont, CA: Artificial Intelligence is slowly but constantly improving as a tool for punishing criminals and detecting illegal activity. It is no longer merely a hypothesis to be considered. Many law enforcement agencies throughout the world are preventing crime with the most up-to-date solutions.

Furthermore, the rate of reported crimes is dependent on the victims or bystanders to self-report. We design and implement an end-to-end system for real-time Though there exist algorithms for fully automated action crime detection in low-light environments. Unlike Closed-recognition [1-5], many are not applied in real-time Circuit Television, which performs reactively, the Low-Light or low-light environments. The existing benchmark action Environment Neural Surveillance provides real time crime recognition datasets such as HMDB-51 [6] (Human Motion alerts. The system uses a low-light video feed processed DataBase), UCF-101 [7] (University of Central Florida), in real-time by an optical-flow network, spatial and temporal and Sports-1M [8] contain primarily daytime videos. UCF networks, and a Support Vector Machine to identify released the UCF-Crime dataset [9] for general anomaly shootings, assaults, and thefts. We create a low-light actionrecognition detection and recognizes 13 crime categories, including arrest, dataset, LENS-4, which will be publicly available.

The land of art, literature and culture will now see the government authorities keeping a close eye on the lawbreakers. In a move that will empower the law enforcement authorities, the Kolkata Police is now expanding the footprint of the CCTV cameras AI-powered devices in crime detection. According to a noted news wire, Kolkata Police has already installed 3,000 closed-circuit cameras all across the city. Police Commissioner Anuj Sharma said, "We are expanding it. Recently, you have seen instances of crime detection by analysing the CCTV footage… With the installation of such cameras, catching those indulging in anti-social acts will become simpler."

It was a pleasant 21 degrees in New York when computers defeated humanity -- or so many people thought. That Sunday in May 1997, Garry Kasparov, a prodigal chess grandmaster and world champion, was beaten by Deep Blue, a rather unassuming black rectangular computer developed by IBM. In the popular imagination, it seemed like humanity had crossed a threshold -- a machine had defeated one of the most intelligent people on the planet at one of the most intellectually challenging games we know. The age of AI was upon us. While Deep Blue was certainly an impressive piece of technology, it was no more than a supercharged calculating machine.