Finance businesses are exploring how generative AI can support employees and customers with a range of text and numerical processes. Financial companies want to capture generative AI’s significant potential while managing the risks. In the finance industry, however, businesses are exploring support on the best way forward. The broad language models within generative AI have strengths with text-based generation, determining language and word patterns, but in the numerically based finance industry, does generative AI hold as much potential?

Despite the scale of generative AI in financial services, financial companies recognise the challenges involved. Most organisations discuss the risks associated with generative AI technology, which typically include data privacy, security and output accuracy. 

Another less discussed challenge is the requirement for the necessary storage infrastructure. To efficiently deploy both AI and generative AI, businesses must implement new storage capabilities to manage the large, real-time, unstructured data used to develop, train and implement generative AI. Without the necessary storage solutions, businesses will experience challenges such as latency that hinder and possibly stop generative AI deployment altogether. 

New storage solutions must be capable of managing data sets at speed and scale, which existing storage solutions cannot provide. AI-enabled infrastructure depends on innovative services like distributed storage, data compression, and data indexing. With the appropriate storage, businesses are perfectly positioned to support and accelerate generative AI.

Examples of use cases with adopting generative AI

Fraud detection and prevention – A core competency of generative AI is identifying patterns. In the finance industry, generative AI can support the recognition of anomalous transactions in real time, helping to determine and prevent fraudulent activities. For example, PayPal implemented a real-time data solution called Aerospike. The results included a 30x reduction in the number of missed fraud transactions and a 3x reduction in associated hardware costs. 

Regulatory Compliance – The finance world is heavily regulated, but generative AI can support the delivery of compliance reports. Through automating selected processes, like document verification and customer identity validation, generative AI can simplify processes like anti-money laundering and know-your-customer (KYC).

Financial Support – Generative AI can assist employees and their customers. It can help deliver a more bespoke financial analysis, including credit risks, credit score, budgeting and savings. 

Automation – Finance consists of multiple documents, countless contracts and account statements. Generative AI automates and streamline processes and repetitive tasks like data entry and reconciliation. 

Customer experience – In the finance world, applying generative AI-powered solutions can strengthen the customer experience. By providing constant support via chatbots, generative AI can be responsible for customer queries within a personalised portal.

Financial Services businesses are willing to embrace technological innovation. For years the industry has welcomed AI, and progress is accelerating thanks to generative AI solutions. The operational efficiencies and greater intelligence to support financial services employees and customers are distinctive benefits. 

In an industry used to proactively manage risk, generative AI will likely expand within the finance industry and support many positive transformations.