Cloud-based AI workflows for fraud prevention
Banks, payment companies, and their fintech partners are navigating a surge in online and mobile transactions as more consumers switch to digital wallets, online wealth management, peer-to-peer lending, and more. According to McKinsey’s “State of consumer digital payments in 2024” report, nearly nine in ten consumers now rely on digital payments across the United States and Europe – a shift that offers convenience but also creates new opportunities for fraud.
AWS & NVIDIA: Empowering financial institutions and fintechs to build, run, and scale AI solutions
The “Online Payment Fraud” report by Juniper Research forecasts that payment fraud will exceed $326 billion between 2023 and 2028, with nearly half of these losses attributed to remote purchases of physical goods.
From identity theft to authorised push payment (APP) fraud, criminals are deploying increasingly sophisticated tactics, often outpacing legacy, rules-based fraud detection systems.
The evolving fraud landscape
Many banks and payment companies still rely on static parameters and manual oversight to identify suspicious transactions. Traditional transaction-monitoring systems use rules-based approaches, flagging activity based on predefined criteria. However, these parameters are manually set and updated, leading to high false-positive rates and making the process labour-intensive and difficult to scale.
As digital transactions grow in volume and complexity, legacy systems struggle to keep up. The sheer amount of digital transaction information, combined with increasingly sophisticated fraud tactics, has outpaced static rule-based approaches. Moreover, regulatory requirements such as the Bank Secrecy Act (BSA) in the US and the revised Payment Services Directive (PSD2) in the EU, further pressure firms to implement robust fraud prevention measures.
Criminals leverage advanced tactics not only to refine social engineering techniques and generate malware but also to exploit loopholes and scale fraud operations in real-time. Modern fraud tactics include APP fraud, where victims are manipulated into making real-time payments to fraudsters; account takeover, where criminals gain unauthorised access to user accounts; identity theft, where fraudsters impersonate legitimate customers; and loan and credit scams, where criminals apply for funds using stolen or synthetic identities.
The role of fintechs in fraud prevention
To combat these challenges, banks and payment companies are partnering with fintechs to implement advanced fraud detection systems. Fintechs play a dual role – developing and deploying modern fraud detection solutions for their payment operations while also supporting banks and payment companies in enhancing their fraud detection capabilities.
This collaborative approach gives financial institutions access to state-of-the-art fraud prevention tools, without requiring them to build complex systems from scratch. By leveraging modern fraud detection systems, organisations can proactively identify and mitigate risks while ensuring regulatory compliance and seamless customer experiences.
AI workflows for fraud prevention
Rather than relying on rigid rules and historical data, financial institutions now turn to artificial intelligence (AI) developed by fintech partners for a more adaptive solution. AI can analyse massive volumes of transactional and behavioural signals, detecting fraud in near real-time. This approach strengthens fraud detection across three critical areas:
- Identity verification – validating new users via know-your-customer (KYC) and anti-money laundering (AML) processes, comparing user details against watchlists, and flagging high-risk profiles.
- Identity authentication – ensuring seamless yet secure transactions for returning users by analysing intrinsic behaviours, such as typing patterns and device handling.
- Fraud prevention – scoring transactions in real-time, detecting unusual or high-risk activity, and halting fraudulent transactions.
Key pillars for AI-powered fraud detection
AI workflows for fraud detection are built on three core pillars, each enhancing security at scale and improving fraud prevention outcomes.
1. Accelerated data processing
AI-accelerated data science processes vast datasets at a speed legacy systems cannot match. For financial institutions handling payments, real-time data ingestion and analysis ensure fraud models remain dynamic and responsive to emerging threats. By extracting actionable insights from petabytes of transactional data in milliseconds, organisations can proactively address fraudulent activity.
2. Enhanced model training
Machine learning (ML) algorithms uncover hidden fraud patterns in large pools of historical and real-time transaction data. Unlike rigid, rules-based systems, ML continuously adapts to new threats, improving accuracy, reducing false positives, and enhancing the customer experience. By analysing device usage, spending behaviour, location signals, and other transactional anomalies, financial institutions can detect both large-scale coordinated attacks and smaller, targeted scams.
3. Real-time model inference
AI-powered fraud detection operates in real-time, analysing and scoring transactions in milliseconds, balancing security with customer convenience. With ultra-low latency inference, organisations can effectively flag and intercept suspicious payments before funds are lost.
The future of fraud detection
Graph neural networks (GNNs) transform fraud detection by providing a deeper, more connected view of transactional data. Unlike traditional models that assess transactions in isolation, GNNs map relationships between accounts, devices, and user attributes, turning rows of transactional data into interconnected networks.
By evaluating connections across multiple sources, GNNs can identify intricate links – such as shared IP addresses or repeated usage trends – detecting fraudulent activity that might otherwise be missed when using outdated methods. This approach is particularly effective in detecting complex fraud rings and money laundering schemes, where criminals operate across multiple accounts, devices, or even geographical locations to avoid detection.
GNNs also help organisations meet regulatory compliance requirements, such as BSA in the US and PSD2 in the EU, by detecting and flagging suspicious financial activity in real-time. This enables firms to mitigate regulatory risks, reducing the likelihood of fines for non-compliance while safeguarding their reputation and financial stability.
Lead the fight against fraud with AI
Advanced AI technologies provide organisations with fully automated fraud prevention systems, capable of detecting, analysing, and responding to nefarious 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 evolving threats before the vulnerabilities can be exploited.
As fintechs continue to reshape the financial landscape, financial firms that adopt AI-driven fraud detection workflows are better equipped to minimise losses, strengthen security, and build customer trust. By improving detection accuracy, reducing false positives, and delivering seamless customer experiences, AI workflows are the future of fraud detection.
Better together: fraud detection workflows from AWS and NVIDIA
Banks and payment companies are partnering with fintech innovators who develop specialised fraud prevention solutions using Amazon Web Services (AWS) and NVIDIA technologies.
Financial institutions handle petabytes of transactional data, with traditional fraud detection models often taking days to train. By leveraging a scalable cloud environment powered by accelerated computing, fintechs can dramatically reduce model training times, ensuring fraud prevention systems evolve at pace with emerging threats.
Organisations that take 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.
To achieve these efficiencies, fintech partners 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, alongside 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.
Further optimising fraud prevention, Amazon Neptune ML leverages GNNs to improve prediction accuracy by over 50% compared to non-graph methods. Offering both fully managed and self-managed options, the service can automatically create, train, and apply ML models to financial 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 transforms fraud prevention. Some financial institutions that have already adopted this cloud-based solution have reported up to 100x improvement in model training times alone.
Together, AWS and NVIDIA empower financial institutions and fintechs to 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 organisations to analyse ever-increasing amounts of transaction data and build scalable, secure, and efficient fraud prevention systems.
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