As I work with financial services and banking organizations around the world, one thing is clear: AI and generative AI are hot topics of conversation. These conversations are so weighty, they are happening at the boardroom level.
I get it. Financial organizations want to capture generative AI’s tremendous potential while mitigating its risks. In the finance and banking industry, however, organizations are seeking extra guidance on the best way forward. That’s because generative AI large language models (LLMs) have prowess in text-based generation, readily finding language and word patterns. In the numerically based finance and banking industry, does generative AI have as much application potential?
In short, yes. But it’s an evolution. Finance and banking organizations, however, have plenty of reasons to look at generative AI LLMs, including their deployment in current use cases as well as for future use cases.
And the finance industry is investing to do so. According to MarketResearch.Biz, the financial services market for generative AI reached USD 847 million in 2022 and is poised to grow at a CAGR of 28.1% during the next decade to exceed USD 9.48 billion by 2032.
Generative AI challenges
Despite the estimated size of generative AI in financial services, financial organizations that I speak with understand that there are distinct challenges. Most predominantly, these organizations talk about the risks that are an intrinsic part of generative AI technology. At the top of that list are data privacy and security as well as output accuracy.
A lesser-known challenge is the need for the right storage infrastructure, a must-have enabler. To effectively deploy generative AI (and AI), organizations must adopt new storage capabilities that are different than the status quo. That’s because vast, real-time, unstructured data sets are used to build, train, and implement generative AI. Without novel storage solutions, organizations face final-mile issues such as latency that hamper—and in some cases fully halt–generative AI deployment.
New storage solutions must handle those data sets at speed and scale; existing storage was not designed to do so. Instead, AI-enabled infrastructure uses state-of-the-art capabilities like distributed storage, data compression, and efficient data indexing. At Dell, we’ve engineered these AI capabilities into Dell PowerScale and ECS. With the right storage, organizations can accelerate generative AI (discussed in more detail here).
Financial use cases for generative AI and AI
As I work with financial services enterprises to help advance generative AI, here are some of the use cases that are at the forefront of adoption.
Fraud detection and prevention. A foundational competency of generative AI is pattern recognition. In the financial industry, generative AI can be used to identify anomalous transaction patterns in real-time, helping to detect and prevent fraudulent activities.
PayPal is a good example, improving the detection of fraudulent transactions using Intel® technologies integrated into a real-time data platform from Aerospike. Key results included a 30x reduction in the number of missed fraud transactions with a 3x reduction in hardware cost.
Regulatory compliance. In the highly regulated world of finance, generative AI can help produce compliance reports. By automating processes like document verification and customer identity validation, generative AI simplifies practices like anti-money laundering (AML) and know your customer (KYC).
Financial assistant. Generative AI is a helpful tool for employees of financial services organizations and their customers. It can help generate personalized financial analysis, including credit scores, credit risks, budgeting and savings plans, and tailored investment recommendations.
Automation. Finance is a document-heavy industry, requiring applications, contracts, account statements, and more. Generative AI can automate and streamline these processes and other repetitive tasks such as data entry and reconciliation, helping financial institutions gain operational efficiency.
Customer experience. In finance organizations, the use of generative AI-powered chatbots and virtual assistants can elevate customer experiences. By providing 24/7/365 support with lesser hold time, generative AI can cover customer questions within the context of personalized, account information and improve the overall customer experience.
Financial services organizations are adept at technology innovations. For years, the industry has embraced AI, and deployments are now being greatly accelerated by generative AI. The operational efficiencies and advanced intelligence to support financial services employees and their customers are clear benefits.
As an industry that understands how to proactively manage risks, I’m confident that generative AI will be unleashed across the financial services industry and fuel many positive transformations to improve business outcomes. I consider myself very fortunate to work with many of these organizations and help usher in our new era of generative AI.
Read my related article about accelerating generative AI here.