Companies and organizations are increasingly using AI to protect their customers and thwart the efforts of fraudsters around the world.
Traditional Methods of Fraud Detection
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.
The Power of AI
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.
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.
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.
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 same AI technologies that help combat fraud in financial services and the public sector can also be applied to healthcare. Insurance companies can use pattern and anomaly detection to look for claims that seem atypical, either from the provider or the patient, and scrutinize billing data for potentially fraudulent activity. Real-time monitoring can detect suspicious activity at the source, as it’s happening. And automated claims processing can help reduce human error and detect inconsistencies while improving operational efficiency.
AI for Fraud Detection Could Save Billions of Dollars
Financial services, the public sector and the healthcare industry are all using AI for fraud detection to provide a continuous defense against one of the world’s biggest drains on economic activity.
The NVIDIA AI platform supports the entire fraud detection and identity verification pipeline — from data preparation to model training to deployment — with tools like NVIDIA RAPIDS, NVIDIA Triton Inference Server and NVIDIA Morpheus on the NVIDIA AI Enterprise software platform.
Conclusion
AI for fraud detection has the potential to save billions of dollars by providing a continuous defense against fraud. By leveraging the power of AI and accelerated computing, financial institutions, government agencies, and healthcare organizations can improve fraud detection accuracy, reduce false positives, and scale rapidly to meet the growing volume of fraud in the digital era.
FAQs
Q: What are the traditional methods of fraud detection?
A: Traditional methods of fraud detection include rules-based systems, statistical modeling and manual reviews.
Q: How does AI help combat fraud?
A: AI helps combat fraud by providing a more accurate and efficient way to detect fraudulent activity, reducing false positives, and scaling rapidly to meet the growing volume of fraud in the digital era.
Q: How do financial institutions use AI to detect fraud?
A: Financial institutions use AI to detect fraud by leveraging NVIDIA technologies such as the NVIDIA RAPIDS Accelerator for Apache Spark and the new NVIDIA AI workflow for fraud detection.
Q: How do US federal agencies fight fraud with AI?
A: US federal agencies fight fraud with AI by leveraging machine learning and accelerated computing to improve fraud detection and prevent improper payments.
Q: How can AI help healthcare stem potential fraud?
A: AI can help healthcare stem potential fraud by providing pattern and anomaly detection, real-time monitoring, and automated claims processing to detect suspicious activity and reduce human error.
