There are strict rules governing financial institutions with a number of global regulatory groups publishing financial compliance requirements. Financial institutions face many challenges and legal responsibilities for risk management, compliance violations, and failure to catch financial fraud.
Fraud in financial systems can occur in unstructured data, or communications such as audio, images, and chats. It is hard to identify this type of fraud because the data contains only minimal markers to use in identification, which requires advanced analytics and techniques to uncover discrepancies.
Banks and other financial institutions face steep fines for regulatory and compliance violations. The Financial Times reports that “an analysis by Behavox which showed that just 0.0024% of the of voice-based communications listened to and 0.0002% of texts analyzed by Behavox were judged concerning in 2021. Despite their low frequency, the consequences for banks in terms of fines for regulatory and compliance actions are steep. Banks were fined $15 billion worldwide for such violations in 2020 alone.”
There are a large number of financial regulatory updates published every year. Compliance officers and financial staff try to accurately and quickly identify criminal actions such as credit card fraud, insider trading, market manipulation, money laundering, and trading violations. However, the sheer volume of transactions and changes in regulations make this an almost impossible task.
Financial Services Regulating Authorities
A new wave of national and international regulations makes adopting risk management critical for financial institutions. Regulatory agencies and requirements include the US Securities and Exchange Commission (SEC), European Systemic Risk Board, and European Central Bank (ECB).
Importance of AI in financial risk management
Artificial intelligence (AI) is increasingly being used as a financial fraud risk management and compliance tool to fight a wide range of financial regulation violations. AI, machine learning (ML), or deep learning (DL) models provide regulators and compliance officers with new capabilities. AI models perform analysis tasks autonomously, ingesting large volumes of data and then recognizing patterns in that data suggesting fraud or non-compliance. AI solutions aid in reducing the chances of human errors that might lead to missing financial fraud, labeling valid transactions as fraud, or costly sanctions for non-compliance.
An NVIDIA-sponsored 2022 State of AI in Financial Services survey, found that 78 percent of financial services professionals state that their company uses accelerated computing to deliver AI-enabled applications through machine learning, deep learning or high performance computing. According to the survey, “With more than 70 billion real-time payment transactions processed globally in 2020, financial institutions need robust systems to prevent fraud and reduce costs. Accordingly, fraud detection involving payments and transactions was the top AI use case across all respondents at 31 percent, followed by conversational AI at 28 percent and algorithmic trading at 27 percent.”
How cloud-based, GPU-accelerated AI meets risk management needs
Running AI models used in fraud prevention and risk management requires huge computational resources that are often not available in data centers. NVIDIA graphics processing units (GPUs) provide processing power for AI, ML, or DL models that cannot be matched by central processing units (CPUs). For example, American Express uses NVIDIA GPUs and AI in anomaly fraud detection running tens of millions of daily transactions. Using the NVIDIA solution, American Express saw a 50x improvement over CPU processing.
Microsoft and NVIDIA have a long history of working together to support financial institutions in detecting fraud, performing risk management, and meeting financial compliance requirements. Using Microsoft Azure cloud, NVIDIA GPUs and NVIDIA AI solutions provide scalable, accelerated resources needed to run AI/DL algorithms, routines, and libraries.
The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to financial institutions. The Azure Machine Learning service integrates the NVIDIA open-source RAPIDS software library that allows machine learning users to accelerate their pipelines with NVIDIA GPUs. The NVIDIA TensorRT acceleration library was added to ONNX Runtime to speed deep learning inferencing. Azure supports NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs), which are optimized for the cost-effective deployment of machine learning inferencing or analytical workloads.
Risk management and compliance solutions
Organizations need help with financial governance, locating financial risks, and meeting compliance regulations. Microsoft provides governance checklists and best practices to aid financial institutions in risk management and compliance. The Microsoft Azure Security website provides valuable information on Microsoft Azure security and framework settings. A Microsoft Purview Compliance Manager software tool simplifies compliance and reduces risk by providing multicloud and continuous regulatory assessment, updates on new regulations as well as a compliance score for the organization.
Another tool that is useful in financial fraud detection and management is the FIS Memento solution. This tool is a cross channel solution providing protection in real time using a combination of rules, AI, ML, and statistical techniques to detect and prevent fraud in banking and payments channels. FIS Memento is offered as an on-premise, or Azure Cloud based solution.
There is a massive growth in financial fraud and requirements for financial organizations to provide risk management and comply with financial regulations. Organizations cannot adequately respond to these needs with infrastructure found in many data centers.
Financial organizations need transformative technology to address the complexity of managing financial risk and meeting regulatory requirements. Microsoft and NVIDIA GPU-accelerated AI solutions running on the Microsoft Azure cloud provide the technology to automate and streamline the risk management process.