Artificial Intelligence (AI) has permeated just about every aspect of industrial production and financial product/ operations management. Organizations now leverage the near-omniscient power of AI to make very accurate, timely decisions based on the humongous amounts of data they may have collected or have access to. This is particularly useful in financial risk management, as it allows organizations to quickly analyze massive amounts of static and dynamic data and make informed decisions regarding the investments they make, the clientele they cater to, and/ or the demographic segments they target.
GPU-accelerated AI is a powerful new technology that utilizes the massive parallel processing capabilities of Graphics Processing Units (GPUs) to dramatically increase the speed and efficiency of AI Computing and Data Analytics.
In this blog post, we will discuss how GPU-accelerated AI is being used in the field of financial risk management and the potential benefits it can bring.
Table of Contents
The Role of AI in Financial Risk Management
In the rapidly fluctuating, ever metamorphosing world of finance, AI algorithms are being used to analyze unprecedented amounts of data, identify trends, detect anomalies, and make predictions. Cloud GPU enabled process acceleration is being deployed to speed up the training of these AI/ ML algorithms, allowing them to analyze data more quickly, seamlessly incorporate modifications/ parameter changes if any, and deliver more accurate market trends and predictions.
For example, AI algorithms can be used to analyze financial and other data to identify potential risks and opportunities, shift in customer preferences, or potential changes in financial regulations. They can also be used to realistically predict the likelihood of certain events, such as defaults or fraud, and to pinpoint data patterns that may indicate risk.
In 2020, nearly 60% of respondents in a survey sent to organizations in Financial Services Sector acknowledged their reliance on AI for fraud detection. By 2022, this number had climbed to nearly 80%, as per leading GPU manufacturer Nvidia. Payments and transactions fraud detection is the predominant AI use case (31%) across all respondents, and the use of AI in FinTech is expected to reach USD 26.67 billion by 2026.
Examples of AI applications in financial market activities (Source)
Suggested Read: Financial Fraud Detection with Deep Learning and AI
Experience Lightning-fast Computing Power with Cloud-based GPU Resources
Introduction to GPU-accelerated AI in Financial Risk Management
GPUs are specialized processors that are designed to accelerate the rendering of images and graphics. They have become increasingly popular in the field of Artificial Intelligence (AI) because of their ability to perform many calculations simultaneously at lightning-fast speeds, making them especially well-suited for interlinked applications such as Machine Learning/ Deep Learning (ML/DL), Data analytics and High Performance Computing (HPC).
GPU-accelerated AI confers numerous advantages when it comes to GPU for financial risk management. These include –
1. Risk Calculation Within Seconds
GPU-accelerated financial systems can crunch massive amounts of inter-related data and perform real-time risk calculations within seconds. These capabilities allow enterprises and banking organizations to better comprehend the risks involved in their undertakings, thus leading to informed financial decision-making.
2. Prediction Accuracy
AI/ML algorithms can be trained to process colossal chunks of data, identify patterns and anomalies, and furnish highly accurate predictions. GPU-accelerated AI comes in handy to significantly improve the accuracy of these predictions by streamlining modification to existing data and incorporation of previously unreferenced data. Enhanced accuracy equals better-informed decisions.
Furthermore, the technology can also be leveraged to identify future opportunities and risks, often even before they appear on the horizon of other analysts.
3. Enhanced Fraud Detection
AI-assisted financial management systems can be trained to detect fraudulent transactions by combing through massive reams of financial data and detecting deviations, anomalies and unauthorized transactions. GPU-acceleration allows AI algorithms to be trained with substantially more speed, accuracy and precision. Data analytics is, thus, conferred increased fraud-detection proficiency.
Furthermore, GPU-accelerated AI financial management systems can also track other unscrupulous activities such as unauthorized access, geographic location spoofed logins, cyber phishing, brute force attacks, etc.
4. Automation of Risk Management Processes
Several risk management processes such as identifying unusual activities, monitoring transactions, and alerting the relevant banking officials/ financial authorities when required can be automated with the help of AI algorithms. When you add GPU-acceleration to these AI algorithms, the output is remarkably more accurate and quick vis-a-vis CPU-assisted detection and/ or manual decision-making. An important example of this is analyzing market developments, predicting future economic/ regulatory/ compliance/ other changes, and taking positions in the market in accordance with the gleaned predictions.
5. Early Warning Systems
GPU-assisted financial management systems are extraordinarily useful for ‘what if’ situations. The processed data and insights emanating from these systems have proven to be extremely valuable for bankers dealing with credit risk management. By employing AI algorithms, the data can be processed and analyzed in real-time, as well as subjected to analytics through different aspects such as financial, industrial, geographic, and perception indicators.
Early warning systems can be made even more efficient to trigger alerts about the slightest errors/ risks with the assistance of GPUs. For example, AI can analyze historical data related to a particular stock or bond and identify any pattern that indicates potential risk. By doing this, organizations can take steps to reduce their risk exposure and make more informed decisions.
6. Identity Verification
AI algorithms can be trained to identify individuals based on facial features, biometrics, gait patterns, and more. GPU acceleration can speed up the training of algorithms and allows the processing of large biometric/ facial patterns datasets with extremely high accuracy and precision. Identity verification has become an important constituent of Multi-Factor Authentication (MFA) for mitigating financial frauds where someone impersonates another individual in order to perform fraudulent activities. GPU-accelerated AI algorithms can manifolds improve the efficiency and efficacy of identity checks.
The financial industry is one of the most difficult to manage, logistically as well as logically. It requires tremendous chunks of data to be processed at once and efficiently, that too in more-or-less real time. What also matters is how this available data is used, the kind of analytics it is subjected to, and the speed of these analytic systems. GPUs with advanced analytics can help generate significant revenue and also enable the organization to perform better and react to changing market situations quicker.
How Can Financial Risk Management Models Leverage GPU-accelerated AI?
Artificial Intelligence models used in financial risk management are developed on the assumption of availability of substantial computational resources. These resources may, however, not be available to financial institutions/ organizations at all times. Deploying GPUs delivers this massive processing power as and when required, and facilitates task accomplishment with far superior speed and accuracy. Leveraging Cloud GPU technology enables enterprises to opt for a pay-as-you-go model and use computational resources only when required.
It can be confidently asserted that GPU-accelerated AI has established its predominance in five key sectors of Financial Risk Management –
– Massive-scale data analytics
– Real-time monitoring of transaction and other financial data to detect suspicious activity
– Detecting patterns indicating fraud or financial risks/ vulnerabilities/ compliance failures
– Biometric-based MFA to prevent impersonation attempts
– Managing customer churn or other customer-related risks
Conclusion
Financial organizations have always been dependent on large, multifarious datasets sourced from multiple avenues. Profit, risk management, brand management and fraud prevention being the most crucial elements for financial firms’ wellbeing. Since the introduction of GPUs, managing colossal chunks of data has become spectacularly easy, requiring mere milliseconds to process Gigabytes worth of available information and produce valid, meaningful output.
Combining AI models with GPU resources has inarguably proven to be of great help to financial institutions in terms of efficiency, accuracy and groundbreaking profits. Additionally, GPU-accelerated resources can be utilized with some tweaks for protection from scammers or fraudsters, ensuring compliance with legislative regulations, among other advantages.
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