Transforming financial services with data-driven insights

Banks and financial services institutions are facing 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 provide highly personalized services and innovative offers. And increasingly, the way to do that is to use AI/ML to gain data-driven insights on which those services and offerings can be based.

The many ways to harness data and AI

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

But that’s just the tip of the iceberg. For years, most financial institutions have traditionally built applications that only worked within their own ecosystems. Financial tools capable of extracting a consumer’s data from multiple institutions were rare, and their data collection methods were usually technically complicated. The Global Open Banking Initiative seeks to change these conditions.

The potential for industry disruption is enormous. Open banking enables the exposure of customer financial data through 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 offers financial data providers a way to easily share their data. This information for AI/ML analysis will allow companies 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-banking entities to provide financial services directly to customers by eliminating the middleman from financial services. Already, Walgreens and Walmart have announced new banking initiatives. Traditional banks could suffer greatly if retailers adopt such services, as both retailers have very high volumes of foot traffic each week.

Where is AI/ML used?

Given the availability of much more customer data, financial institutions are looking to AI/ML to gain deeper insights and actionable insights. Artificial intelligence 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 in anti-money laundering efforts.

Additionally, AI can be used in front and middle office applications. Some uses include enabling frictionless, 24/7 customer interactions through intelligent chatbots, personalizing customer experiences using recommendation systems, and using customer automation. robotic processes to reduce human error in daily operations.

What’s needed?

The wide variety of AI applications in financial services institutions all require huge amounts of computational resources to run AI workloads and train ML models efficiently and cost-effectively. This is one 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. The combination of the right cloud and GPU technologies can provide the required scalability, faster and more efficient run times, and increased model accuracy.

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

One of the biggest obstacles to the wider democratization of AI is concerns about the 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 about the security and privacy of transaction data. One area where Microsoft and NVIDIA have recently focused on solving some of the specific issues of financial services is offering Azure Confidential Computing with NVIDIA GPUs for trusted AI.

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

This follows years of collaboration where Microsoft and NVIDIA have delivered tightly integrated and optimized technologies. For example, libraries perform certain tasks to use GPUs efficiently. 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. This saves companies time and operational costs.

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

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