Companies and organizations are increasingly using AI to protect their customers and thwart the efforts of fraudsters around the world.
Voice security company Hiya found that 550 million scam calls were placed per week in 2023, with INTERPOL estimating that scammers stole $1 trillion from victims that same year. In the U.S., one of four noncontact-list calls were flagged as suspected spam, with fraudsters often luring people into Venmo-related or extended warranty scams.
Traditional methods of fraud detection include rules-based systems, statistical modeling and manual reviews. These methods have struggled to scale to the growing volume of fraud in the digital era without sacrificing speed and accuracy. For instance, rules-based systems often have high false-positive rates, statistical modeling can be time-consuming and resource-intensive, and manual reviews can't scale rapidly enough.
In addition, traditional data science workflows lack the infrastructure required to analyze the volumes of data involved in fraud detection, leading to slower processing times and limiting real-time analysis and detection.
Plus, fraudsters themselves can use large language models (LLMs) and other AI tools to trick victims into investing in scams, giving up their bank credentials or buying cryptocurrency.
But AI - coupled with accelerated computing systems- can be used to check AI and help mitigate all of these issues.
Businesses that integrate robust AI fraud detection tools have seen up to a 40% improvement in fraud detection accuracy - helping reduce financial and reputational damage to institutions.
These technologies offer robust infrastructure and solutions for analyzing vast amounts of transactional data and can quickly and efficiently recognize fraud patterns and identify abnormal behaviors.
AI-powered fraud detection solutions provide higher detection accuracy by looking at the whole picture instead of individual transactions, catching fraud patterns that traditional methods might overlook. AI can also help reduce false positives, tapping into quality data to provide context about what constitutes a legitimate transaction. And, importantly, AI and accelerated computing provide better scalability, capable of handling massive data networks to detect fraud in real time.
How Financial Institutions Use AI to Detect Fraud Financial services and banking are the front lines of the battle against fraud such as identity theft, account takeover, false or illegal transactions, and check scams. Financial losses worldwide from credit card transaction fraud are expected to reach $43 billion by 2026.
AI is helping enhance security and address the challenge of escalating fraud incidents.
Banks and other financial service institutions can tap into NVIDIA technologies to combat fraud. For example, the NVIDIA RAPIDS Accelerator for Apache Spark enables faster data processing to handle massive volumes of transaction data. Banks and financial service institutions can also use the new NVIDIA AI workflow for fraud detection - harnessing AI tools like XGBoost and graph neural networks (GNNs) with NVIDIA RAPIDS, NVIDIA Triton and NVIDIA Morpheus - to detect fraud and reduce false positives.
BNY Mellon improved fraud detection accuracy by 20% using NVIDIA DGX systems. PayPal improved real-time fraud detection by 10% running on NVIDIA GPU-powered inference, while lowering server capacity by nearly 8x. And Swedbank trained generative adversarial networks on NVIDIA GPUs to detect suspicious activities.
US Federal Agencies Fight Fraud With AI The United States Government Accountability Office estimates that the government loses up to $521 billion annually due to fraud, based on an analysis of fiscal years 2018 to 2022. Tax fraud, check fraud and improper payments to contractors, in addition to improper payments under the Social Security and Medicare programs have become a massive drag on the government's finances.
While some of this fraud was inflated by the recent pandemic, finding new ways to combat fraud has become a strategic imperative. As such, federal agencies have turned to AI and accelerated computing to improve fraud detection and prevent improper payments.
For example, the U.S. Treasury Department began using machine learning in late 2022 to analyze its trove of data and mitigate check fraud. The department estimated that AI helped officials prevent or recover more than $4 billion in fraud in fiscal year 2024.
Along with the Treasury Department, agencies such as the Internal Revenue Service have looked to AI and machine learning to close the tax gap - including tax fraud - which was estimated at $606 billion in tax year 2022. The IRS has explored the use of NVIDIA's accelerated data science frameworks such as RAPIDS and Morpheus to identify anomalous patterns in taxpayer records, data access and common vulnerability and exposures. LLMs combined with retrieval-augmented generation and RAPIDS have also been used to highlight records that may not be in alignment with policies.
How AI Can Help Healthcare Stem Potential Fraud According to the U.S. Department of Justice, healthcare fraud, waste and abuse may account for as much as 10% of all healthcare expenditures. Other estimates have deemed that percentage closer to 3%. Medicare and Medicaid fraud could be near $100 billion. Regardless, healthcare fraud is a problem worth hundreds of billions of dollars.
The additional challenge with healthcare fraud is that it can come from all directions. Unlike the IRS or the financial services industry, the healthcare industry is a fragmented ecosystem of hospital systems, insurance companies, pharmaceutical companies, independent medical or dental practices, and more. Fraud can occur at both provider and patient levels, putting pressure on the entire system.
Common types of potential healthcare fraud i
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Companies and organizations are increasingly using AI to protect their customers and thwart the efforts of fraudsters around the world.
