The United States Treasury Department is turning more to artificial intelligence (AI) to fight fraud, using the technology to thwart 4bn in improper payments in the last year. The agency released the estimate in a press release Thursday announcing the success of its "technology and data-driven approach". In fiscal year 2024, which ran from October 2023 to September 2024, the Treasury used machine-learning AI to halt 1bn in cheque fraud, it said. At the same time, its AI processes helped weed out 3bn in other improper payments, including by pinpointing at-risk transactions and improving screening, it added. The 4bn total annual fraud prevention was six times higher than that captured in the previous year, according to the agency.

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Artificial Intelligence (AI) is transforming nearly all industries, and ecommerce is no exception. One of the areas where savvy online businesses are using AI to streamline operations is fraud detection. Where merchants once employed legions of employees dedicated to reviewing transactions, algorithms can now analyze millions of data points to flag irregularities and fraudulent behavior. Successful fraud detection requires a delicate balance and extreme precision.

If you've been keeping up with the news lately, you've probably noticed that AI is everywhere. From the concept of self-driving cars to newcomers like voice generation, deepfake videos, and OpenAI (Midjourney and ChatGPT), AI is changing the way we live and work. But it's not all sunshine and rainbows – there are also concerns about the ethical implications of AI, particularly when it comes to fraud. The first question we must ask ourselves is: why is AI a dangerous fraud trend in banking? AI has the power to automate and streamline banking processes, which can be exploited by fraudsters.

As fraud typologies become more complex,, it is harder for firms to ensure they have robust detection practices in place. Yet while some red flags cover many fraud types, precise detection requires a forensic approach to pick up on complex, highly contextual, activity. In a constantly evolving risk environment, how can firms ensure they are detecting fraud proactively, efficiently, and accurately? Customer behavior changes are often a core indicator of fraud. Certain changes in customer behavior are clear enough that they broadly apply to most situations.

Using a GPU (Graphics Processing Unit) can accelerate trading by allowing for faster processing of large amounts of data. This can be particularly useful for traditional banks, capital market firms and fintech companies that rely on data-intensive trading algorithms and need to process large amounts of data in real-time. Running machine learning algorithms: Machine learning algorithms can be computationally intensive, and a GPU can speed up the training process. This can be especially useful for developing and testing trading strategies that rely on machine learning. Data processing: A GPU can process large amounts of data quickly, which can be useful for tasks such as real-time data analysis and market monitoring.

A new study from Juniper Research has found the global business spend on AI-enabled financial fraud detection and prevention strategy platforms will rise from just over $6.5bn in 2022 to $10bn by 2027. Growing at 57% over the period, the report predicts that as fraudsters become more sophisticated in their attacks, merchants and issuers will become more adept at utilising highly advanced AI-enabled fraud detection methods to combat crime. The report identified the ability of AI to recognise fraudulent payment trends at scale as being critical to provide improved fraud prevention. AI-enabled fraud detection and prevention market platforms use AI to monitor transactions and identify fraudulent transaction patterns, reducing fraud risks by blocking transactions in real-time. The research analysis predicts cost savings from AI deployment will be critical to taking system use beyond regulatory compliance.

Today, artificial intelligence (AI) has become an irreplaceable part of how we shop and do business on the web. It's a key component of the underlying infrastructure that brands and retailers rely on to engage customers, track trends, make better business decisions, and provide the most optimal, personalized customer experiences possible. Here are the top nine trends in eCommerce AI that you can expect to see in 2022 and 2023. AI voice assistants like Amazon's Alexa, Apple's Siri, and Google Assistant have become household names used by millions of people worldwide. In fact, 27% of shoppers took advantage of voice assistants to make online purchases in 2020, accounting for $40 billion of revenue in the U.S. and the UK alone.

Fraud is evolving, and many Australian businesses may struggle to keep up with fraudsters who are continuing to find new ways to evade detection and exploit vulnerabilities. In the twelve months to June 2021 alone, the Australian Payments Network found fraud on payment card transactions totalled $490.1 million, an increase of 9.2 per cent from the year before. Further, research from Statista shows that as of 2021, around 1.25 million dollars had been lost in online shopping scams in Australia. For retailers of all sizes, it has never been more important to get ahead and proactively find a solution that helps to stop fraudulent transactions without turning away legitimate customers and limiting opportunities for growth. What your business needs, however, depends on the size of your organisation or the trajectory of growth that you are on.

Startups face many challenges, but one outsized one is having to navigate regulatory requirements that differ in each jurisdiction. Particularly when it comes to financial compliance, implementing solutions isn't a walk in the park -- or cheap. A recent Accenture survey found that nine in 10 companies expect evolving business, regulatory and customer demands to increase their compliance costs by up to 30% over the next 2 years. An added hurdle is that information isn't always readily available to founders, resulting in a knowledge gap, Baran Ozkan tells TechCrunch. Ozkan is the founder of Flagright, a startup that aims to prevent financial crime, like money laundering or terrorist financing, with an API-first product.