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 important that they happen at the board level.
I understand. Financial organizations want to harness the enormous potential of generative AI while mitigating its risks. In the finance and banking sector, however, organizations are seeking additional guidance on the best path forward. Indeed, large generative AI language models (LLMs) possess prowess in text-based generation, easily finding language and word patterns. In the digital-based finance and banking sector, does generative AI have as much application potential?
In short, yes. But it’s an evolution. Finance and banking organizations, however, have many reasons to consider generative AI LLMs, including their deployment for current as well as future use cases.
And the financial sector is investing to achieve this. According to MarketResearch.BizThe financial services market for generative AI reached $847 million in 2022 and is expected to grow at a CAGR of 28.1% over the next decade to exceed $9.48 billion by 2032.
The challenges of generative AI
Despite the estimated scale of generative AI in financial services, the financial organizations I speak with understand that there are distinct challenges. Most of the time, these organizations talk about the risks that are inherently part of generative AI technology. At the top of the list are data privacy and security as well as accuracy of results.
A lesser known challenge is the need to have suitable storage infrastructure, an essential tool. To effectively deploy generative AI (and AI), organizations must adopt new storage capabilities that are different from the status quo. Indeed, large sets of real-time unstructured data are used to create, train and implement generative AI. Without innovative storage solutions, organizations face last-minute issues, such as latency, that hinder or even completely halt the deployment of generative AI.
New storage solutions must handle these data sets at high speed and scale; existing storage was not designed for this. Instead, AI-based infrastructure uses cutting-edge features such as distributed storage, data compression, and efficient data indexing. At Dell, we have built these AI capabilities into Dell Power Scale 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 companies to advance generative AI, here are some of the use cases that are at the forefront of adoption.
Fraud detection and prevention. A core skill of generative AI is pattern recognition. In the financial sector, generative AI can be used to identify abnormal transaction patterns in real-time, helping to detect and prevent fraudulent activity.
PayPal is a good example, improving the detection of fraudulent transactions using Intel® technologies integrated with a real-time data platform from Aerospike. Key results included a 30x reduction in missed fraudulent transactions with a 3x reduction in hardware cost.
Regulatory conformity. 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 useful 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 personalized investment recommendations.
Automating. Finance is a document-intensive industry, requiring applications, contracts, account statements, etc. Generative AI can automate and streamline these processes and other repetitive tasks such as data entry and reconciliation, helping financial institutions gain operational efficiencies.
Client experience. In financial organizations, the use of AI-based chatbots and generative virtual assistants can improve customer experience. By providing 24/7/365 support with reduced wait time, generative AI can answer customer questions in the context of personalized account insights and improve customer service. overall customer experience.
Financial services
Financial services organizations are adept at technological innovations. The industry has been embracing AI for years, and deployments are now significantly accelerated by generative AI. The operational efficiencies and advanced intelligence that financial services employees and their customers benefit from are clear benefits.
As an industry that understands how to proactively manage risk, I am confident that generative AI will spread throughout the financial services industry and power many positive transformations to improve business outcomes. I consider myself very fortunate to work with many of these organizations and help lead the way in our new era of generative AI.
Read my related article on accelerating generative AI here.
