Transforming Financial Services with Data-Driven Insights

May 30, 2022

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based.

The many ways to leverage data and AI

Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources. For example, some major credit reporting services now let customers link their bank accounts. The service provider then incorporates data that was not formally used in the past, like regular rent and utility payments, to modify the customer’s credit score. Financial services institutions can then better assess risks, increase credit limits, and optimize annual percentage rates for each customer.

But that is just the tip of the iceberg. For years, most financial institutions traditionally built applications that only worked within their own ecosystems. Finance tools that could pull a consumer’s data from multiple institutions were rare, and their methods for collecting data were usually technically complicated. The global open banking initiative seeks to change those conditions.

The potential for industry disruption is enormous. Open banking enables the exposure of customer financial data via APIs, extending an organization’s reach far beyond traditional financial services institutions. The open banking market is expected to reach $43.15 billion by 2026, growing at a compound annual growth rate (CAGR) of 24.4% through 2026, according to Allied Market Research.

Open banking presents a way for financial data providers to easily share their data. This information for AI/ML analysis will allow businesses to create new products and personalized offers for consumers.

The data-driven insights from this data sharing offer many opportunities for financial services organizations. But it also opens the door to new competition. For example, open banking allows non-bank entities to provide financial services directly to customers eliminating the financial services middleman. Already, Walgreens and Walmart have announced new banking initiatives. Traditional banks could greatly suffer if retailers embrace such services as both retailers have very high volumes of foot traffic every week.

Where is AI/ML used?

Given the availability of much more customer data, financial institutions are looking for AI/ML to help get deeper insights and actionable information. AI technologies, including machine learning, can help improve loan underwriting and reduce financial risk. From a different risk perspective, AI is often used to fight fraud and aid anti-money laundering efforts.

Additionally, AI can be used in both front and middle office applications. Some uses include enabling frictionless, 24/7 customer interactions via intelligent chatbots, personalizing the customer experience using recommendation systems and utilizing robotic process automation to reduce human error in day-to-day operations.

What’s needed?

The wide variety of AI applications in financial services institutions all need huge amounts of compute resources to run AI workloads and train ML models efficiently and cost-effectively. This is an area where a cloud-based, GPU-accelerated approach can help.

Workloads can greatly benefit from elastic and scalable cloud-based, GPU-accelerated resources running optimized AI/ML algorithms, routines, and libraries. Marrying the right cloud and GPU technologies can provide the requisite scalability, faster and more efficient runtimes, and increased model accuracy.

As such, organizations greatly benefit when teaming with partners that offer the right technology and deep industry-specific AI expertise. Microsoft and NVIDIA have been working together for years in this AI/ML arena.

One of the biggest obstacles to the broader democratization of AI is concerns regarding sharing and use of personal data. For example, banks are often unable to collaborate on tasks such as fraud and money laundering detection due to concerns regarding the security and privacy of transaction data. One area where Microsoft and NVIDIA have recently focused on addressing some of the specific issues in financial services is offering Azure confidential computing with NVIDIA GPUs for trustworthy AI.

NVIDIA and Microsoft recently announced that they are combining the power of GPU-accelerated computing with confidential computing for state-of-the-art AI workloads. With support for Ampere Protected Memory (APM) in NVIDIA A100 Tensor Core GPUs and hardware-protected VMs, financial services organizations will be able to use sensitive datasets to train and deploy more accurate models with state-of-the-art performance and an added layer of security that their data remain protected.

This follows years of collaboration where Microsoft and NVIDIA have delivered tightly integrated and optimized technologies. For example, libraries perform certain tasks to efficiently use GPUs. Installing and configuring these libraries takes time and effort. Azure takes care of pre-installing these libraries and setting up all the complex networking between compute nodes through integration with GPU pools. Additionally, by collaborating, NVIDIA and Azure have developed optimal configurations for GPU-accelerated AI workloads. That saves companies time and operational costs.

And most importantly, the compute resources made available by Microsoft and NVIDIA give financial institutions a way to become data-driven, transforming their processes and operations based on the derived insights from their analysis. This, in turn, helps these companies monitor financial performance, identify areas for improvement, uncover new opportunities, and better serve their customers.

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