The Financial Services sector is undoubtedly one of the most data-driven industries. Financial institutions have truckloads of data sourced from multiple channels. However, only access to humongous datasets does not drive positive outcome.
Unless there are available accessible and affordable methodologies/ technologies to comprehend and analyze said data for generating business insights. This is the area where Financial Services sector needs to be at the forefront of innovation.
In the race to derive the most out of available data, Graphical Processing Units (or GPUs) are revolutionizing the financial industry at a flash-flooding pace. These sophisticated processors offer financial enterprises simultaneous capabilities to optimize processes, minimize costs, manage risks, and enhance efficiency and accuracy.
In this post, we will discuss some use cases of GPU technology in finance as well as the multi-pronged benefits that GPUs can deliver to the financial sector.
Table of Contents
Introduction to GPU Technology
GPUs are specialized processors that can process huge amounts of data quickly and efficiently. Though wildly popular in the gaming industry for decades, they have been steadily gaining traction in industries such as finance, healthcare, architecture, cybersecurity, data analytics, etc., owing to the wide range of advantages they offer over traditional processors (CPUs).
Given their parallel processing architecture, GPUs can process data several hundred times faster than traditional CPUs, allowing financial enterprises to quickly generate insights from the data at their disposal. In a sector susceptible to massive upheavals within the blink of an eye, expeditious response to changing market conditions is imperative.
Furthermore, GPUs are instrumental in training sophisticated Artificial Intelligence/ Machine Learning (AI/ ML) models that can undertake predictive analytics, decipher patterns and anomalies invisible to the human eye, learn human languages and nuances to power interactive chatbots, and so on.
Finally, GPUs are also more energy-efficient vis-a-vis CPU arrays capable of delivering the same performance. In cutthroat industries like finance, even the slightest reduction in operational costs can work wonders for the bottom line.
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Coming to GPU Applications in the Financial Sector
Use Case #1: Trading and Investment
Every second counts in stock trading. Every decision in the stock market will depend upon substantial historical data and data from different mathematical models comparing past trends and current pricing patterns. GPUs can help with these simultaneous data analytics to accurately gauge market movements.
GPU technology is heavily being used to optimize trading and investment decisions. By leveraging GPU efficiencies, businesses can analyze colossal datasets quickly and make informed decisions in a fraction of the time it would take CPU-based systems to process the same workloads.
GPUs can likewise be used to power AI-automated algorithmic trading systems where their parallel processing capabilities can be harnessed to make instantaneous buy-or-dump decisions without human intervention. Note that GPUs cannot speed up algorithmic trade but only the decision-making since the former depends on internet speed, proximity access to the stock market, and other factors.
Read: GPU Computing in Financial Analytics
Use Case #2: Risk Analysis
Every aspect of financial market from insurance to stock trading, necessitates detailed risk assessment. GPUs can assist financial enterprises in quickly and accurately identifying and managing potential risks in their portfolios and redistributing investments in response to changing risk profiles.
GPU-enabled systems can simultaneously reference multiple databases (customer demographic profiles, past credit history, market developments, financial crime patterns, law enforcement databases, etc.) and reduce risk aggregation from hours to seconds. It is most visible when insurance companies can provide you with credit scores and interest rates instantly on demand and not take days to furnish answers.
Commodity market investors can also benefit from GPU-based analysis because they can easily assess the impact of any new trend, market/ geopolitical development, or other news on the current prices.
Most importantly, GPUs can be used to develop and implement ML algorithms for risk analysis.
Not only does this help businesses pinpoint and monitor complex risks, but it also automates the subsequent response processes. Deutsche Bank recently partnered with leading GPU manufacturer Nvidia to develop an AI/ ML-based risk analysis model.
Read: How GPU Computing is Improving Financial Analytics
Use Case #3: Statistical Modeling
GPUs can be used to develop and test powerful statistical models that can incorporate millions of variables and their cross-relationships to predict the changing contours of financial markets authoritatively.
Moreover, GPUs’ unparalleled processing speeds can enable businesses to include more variables and instantly reorient the models and algorithms as and when required. Besides ensuring the faithfulness of the statistical model to current conditions, this also reduces operational and workforce costs involved in continuous refactoring, thereby maximizing efficiency.
Use Case #4: Fraud Detection
Payment card fraud losses worldwide exceeded USD 32 billion in 2021. Over the next 10 years, the payment cards industry is projected to lose USD 397 billion worldwide!
The predominant reason behind this aggressive increase in fraud is that bankers and retailers are expected to make split-second judgments to maximize credit, pump up numbers and reduce customer waiting time.
Whereas real-time risk analysis before forwarding loans or accepting payment instruments should have required more assessment time and application of mind, the same is observed more in breach. This is where GPUs shine.
GPU-based financial systems can seamlessly reference diverse databases, crosscheck biometrics and other identity authentication parameters, validate against law enforcement digital resources, generate risk profiles, assess crime and fraud patterns and methodologies, and so forth to minimize the potential for fraud. They deliver the capability for real-time deduction of suspicious activity and enable prohibitive action before any damage is done.
Again, GPUs can also be used to develop ML-optimized systems for fraud detection and minimizing exposure to prevalent fraud markets/ customers.
Read: Improve Insurance Fraud Detection Using ACE GPU Server
Use Case #5: Portfolio Optimization
Portfolio optimization is a pre-requisite for every business organization and individual investor. Not putting all eggs in one basket is not just an idiom but a practical necessity in the financial market.
GPU-enabled AI/ ML systems enable businesses to identify investment opportunities to maximize returns and minimize risks in their current portfolio.
Access to rich statistical data in easily comprehensible visual form also makes it easy for businesses to understand the investment allocation required for mitigating risks and improving profits even when their portfolio spans multiple sectors and hundreds of financial entities.
By leveraging the unerring accuracy of GPUs for portfolio optimization, businesses can thus improve their overall financial performance.
Use Case #6: Robotic Process Automation (RPA)
Robotic Process Automation in the financial sector refers to automating and streamlining various financial processes ranging from data entry and market monitoring to investment performance tracking.
It is an emerging subset of ML/ DL. GPUs can altogether change the game by accelerating process automation and identifying complicated decision chains where RPA can be a promising solution, thereby improving efficiency and minimizing costs in the long run.
Use Case #7: Security and Compliance
GPUs can also ensure security and compliance in the Finance industry. They are specially designed for analyzing large amounts of data quickly and accurately, allowing businesses to identify potential security threats and take corrective action before any damage is done.
Once more, GPUs shoulder the inevitable responsibility of helping develop and implement Machine Learning algorithms for security and compliance. These algorithms automate identifying and remediating security issues/ anomalies/ errors.
Read: Financial Fraud Detection with Deep Learning and AI
Final words: Benefits of GPU Technology in Finance
As we’ve seen, GPUs offer various benefits for financial institutions and service providers.
By delivering unprecedented data processing capabilities at lightning-fast speeds, GPUs enable enterprises to make informed decisions about investments, strategies, and business operations.
Businesses can, thus, be highly competitive in an increasingly complex and cut-throat industry. Process automation, cost minimization, and enhancement in security and compliance postures are other much-vaunted benefits of GPU technology.
Performance gains, processing efficiency, and cost-effectiveness aside, GPUs are also more energy-efficient and use much less electricity vis-a-vis CPU arrays, culminating not only in a positive impact on the business bottom line but also on the environment.
Conclusion
We’ve seen that GPU technology is revolutionizing the finance industry. We also discussed some use cases of GPU technology in financial services sector. Of course, there are many other promising use cases and applications being researched and developed even now as more and more businesses realize that they can unlock their potential and maximize profits by leveraging the mesmerizing powers of GPUs.
As the volume and complexity of data continue to grow, GPU deployment is certain to become a given across financial enterprises in the near future.
Ace Cloud Hosting excels at enabling financial enterprises to streamline their workflows and process colossal amounts of data in the blink of an eye. Connect with our Consultant and see for yourself how effortlessly the most advanced GPUs can accelerate your business applications. This is one decision you’ll never regret!
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