Financial service providers face a surge in transaction fraud as the transition to digital banking and online transactions continues. Across the United States and Europe, roughly nine in ten consumers now rely on digital payments1 – a shift that offers convenience but also creates new avenues for malicious actors. Recent estimates forecast that payment fraud will exceed $326 billion for the period between 2023 and 2028, with remote purchases of physical goods accounting for over 47 percent of fraud losses2. From identity theft to online shopping scams, criminals are devising increasingly sophisticated tactics that rigid rules-based fraud detection systems are often struggling to detect.
Adapting to fraud's ever-changing threats
Many companies in the financial services sector rely on outdated, rules-based systems for fraud detection. And while these systems can initially work well, they depend on static criteria – such as transaction limits or known fraudulent IP addresses - monitored by human security experts. Over time, these conditional statements become tangled and can't easily handle the sheer volume and complexity of online transactions.
As digital payments rise, scamming practices grow more sophisticated. Criminals use technology not only to bolster social engineering and malware generation, but to spread disinformation, discover new vulnerabilities, and scale up fraud operations in real-time. Some of the most common threats include; authorized push payment fraud, where victims are manipulated into making real-time payments to fraudsters; identity theft, where fraudsters impersonate legitimate customers; online shopping scams, where fraudsters set up fake e-commerce sites; and investing scams, where criminals lure potential investors with promises of high returns. This growth in digital fraud means that network security and protecting customer data has become more challenging than ever, with companies using modern cloud technology to protect their global payment systems.
Traditionally, a bank might have relied on static rules, historical data, and human monitoring to identify any anomalies in its financial data. However, the rapid increase in the sheer amount of digital transaction information, along with complex global money movement among consumers and businesses, has the potential to overwhelm these legacy systems, making real-time monitoring incredibly difficult to implement and a need for a more adaptive solution.
Rethinking fraud prevention with AI workflows
Instead of relying on static rules and archaic data patterns, financial institutions can use artificial intelligence (AI) to analyze massive volumes of transactional and behavioral signals to identify fraud more dynamically. This approach addresses fraud in three critical areas:
- Identity verification – validating new users via know-your-customer (KYC) and anti-money laundering (AML) processes, comparing personal details against watchlists, and flagging high-risk profiles.
- Identity authentication – ensuring returning users aren't compromised, by analyzing intrinsic behaviors, such as typing patterns and device handling.
- Fraud prevention – continuously scanning transactions in real-time to halt suspicious transfers before the funds are lost.
To effectively prevent fraud, AI must not only detect suspicious activity but also process vast amounts of data efficiently. This capability is built on three core pillars - accelerated data processing, enhanced model training, and real-time model inference - each playing a crucial role in strengthening fraud detection at scale.
1. Accelerated data processing
AI-accelerated data science can process vast datasets, including years of historical transaction data, in a fraction of the time it takes a legacy system. For banks and payments companies that have large volumes of data, this AI acceleration extracts actionable insights from petabytes of data, ensuring fraud models remain dynamic and responsive to new threats.
2. Enhanced model training
Unlike traditional rules-based approaches, which struggle to identify emerging fraud tactics, machine learning (ML) algorithms can uncover hidden patterns in large pools of historical and real-time transaction data, improving accuracy and reducing false positives by detecting anomalies that might indicate fraudulent behavior. By comparing device usage, spending thresholds, or location-based signals, it is possible to spot not only widespread, coordinated attacks but also specialized, small-scale scams.
3. Real-time model inference
AI-powered fraud detection operates in real-time, with the ability to analyze and score transactions in milliseconds with ultra-low latency inference, ensuring risk is assessed before a transaction is complete. By continuously refining fraud detection accuracy, financial service providers can seamlessly process genuine customers whilst swiftly blocking fraudsters, leading to better accuracy and a smoother customer experience.
The future of fraud detection
Graph neural networks (GNNs) transform fraud detection by modeling transactions in new and innovative ways. GNNs turn transactional rows of data, into networks of connected accounts, devices, and user attributes. Unlike traditional models, which assess transactions in isolation, GNNs evaluate connections across multiple sources. The technology can spot intricate links – such as shared IP addresses or repeated usage trends – identifying fraudulent activity that might otherwise be missed when using outdated methods. This approach is particularly effective for detecting complex fraud rings and money laundering schemes, where criminals use multiple accounts, devices, or even geographical locations to avoid detection.
GNNs can also be used to address regulatory risks, such as fines for the non-reporting of suspicious financial crimes, by identifying and mitigating them in real-time, ensuring regulatory compliance, whilst also protecting organizations from reputational and financial damage.
Staying ahead of fraud with AI
Advanced AI technologies provide banks and payments companies with fully-automated fraud prevention systems, capable of detecting, analyzing, and responding to fraudulent activity in real-time. By continuously learning from transaction data, detecting subtle anomalies, and adapting to evolving criminal tactics, AI delivers a level of speed and accuracy that legacy systems cannot match.
These AI-driven tools do more than detect anomalies, they proactively prevent fraud by predicting emerging fraud scenarios, address money laundering risks, enhance KYC processes, and stress-test systems against new threats before the vulnerabilities can be exploited by scammers.
As financial services increasingly shift to digital channels, institutions that embrace AI to detect fraud are better equipped to reduce losses, protect their reputations, and deliver seamless customer experiences. From improved detection accuracy to faster, safer transactions, achieved in a cost-effective manner without compromising the customer experience, AI workflows stand out as a transformative tool and the future of fraud prevention.
Better together: fraud detection workflows from AWS and NVIDIA
Financial institutions handle petabytes of transactional data, with traditional fraud detection models often taking days to train. By using a scalable cloud environment, powered by accelerated computing, financial services companies can dramatically reduce model training times, enabling fraud prevention systems to evolve at pace with emerging threats.
Companies taking this approach can benefit from low-latency fraud prevention, which scales seamlessly under peak workloads, allowing them to detect and mitigate suspicious activity in milliseconds.
Together, Amazon Web Services (AWS) and NVIDIA help the financial services industry build, run, and scale AI solutions, delivering infrastructure, software, and services to enable accelerated innovation in the cloud. This long-standing collaboration provides powerful, flexible compute resources, enabling financial institutions to analyze ever-increasing amounts of transaction data and build scalable, secure, and efficient fraud prevention systems.
To achieve these efficiencies, financial institutions can leverage Amazon EMR, a cloud big data platform, with NVIDIA RAPIDS Accelerator for Apache Spark, to accelerate data ingestion and feature engineering. By incorporating Amazon SageMaker, a cloud-based ML platform, with NVIDIA RAPIDS, they can build, train, tune, and deploy models efficiently and further reduce model training times using GPU-accelerated algorithms. For real-time fraud detection at scale, SageMaker complemented with NVIDIA Triton Inference Server, provides a highly scalable platform to deploy and serve multiple ML models seamlessly.
Amazon Neptune ML leverages GNNs to improve prediction accuracy by over 50% compared to non-graph methods3. It offers both fully managed and self-managed options, automatically creating, training, and applying ML models to financial services graph data.
With internal testing by AWS and NVIDIA yielding 14x faster end-to-end data processing, model training, and model inference – alongside 8x lower costs – this approach proves to transform fraud prevention. Some financial institutions who have already adopted this solution, have reported up to 100x improvement in model training times alone4 5. By reducing false positives, accelerating decision-making, increasing accuracy, and improving customer experience, AWS and NVIDIA help financial institutions stay ahead in an increasingly complex and fast-moving digital landscape.
Join industry experts from AWS and NVIDIA for a webinar on leveraging AI workflows for advanced fraud detection.
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