Banking and AI: how can we ensure ROI?

Banking and AI: how can we ensure ROI?

By Marshall Choy, SVP of Product at SambaNova Systems

Generative AI has established itself as a cultural phenomenon in recent months, but now enterprises want to know how AI can work for them. For the banking industry in particular, the advent of artificial intelligence (AI) in the enterprise represents a tremendous opportunity and one that is not to be missed.

By leveraging AI technology and solutions, early-adopting financial institutions can significantly reduce costs, create new efficiencies in their operations, and ensure tangible return on investment (ROI). This, in turn, has the potential to boost profits and drive higher returns for shareholders.

Financial institutions that choose to forego the use of generative AI models in their customer interactions are missing out on the potential benefits that AI can bring. Without it, you get a less comprehensive view of customers which could result in missed opportunities, customer churn, and steadily decreasing returns on technology investments.

With a large proportion of banks already using AI on a day-to-day basis, and more and more taking steps to incorporate AI into their daily operations each day, the potential is clear to see.

Improving fraud detection and risk management, reducing over-leveraging, and bettering customer service are all key components in the era of AI-powered banking.

Keeping your customers engaged

Given current economic challenges, customer engagement and user experience are quickly becoming front-end priorities for retail banks and other financial institutions in order to retain or grow their customer base. Many are relying on AI-driven technologies to facilitate and enhance these processes.

From customer-facing generative AI that uses generative models to provide automated, tailored support and help customers access the services they need, to data-driven insights that help financial institutions understand customer behaviour and preferences, generative AI platforms are already revolutionising the way banks interact with their customers.

The implementation of customer-facing generative AI may seem like a large investment. However, the payoffs aren’t just long-term pipe dreams. The benefits of increasing the number of loyal customers can be measured in the short term as marginal gains over close competitors. By putting generative AI to work in customer service, customer queries can be handled quickly and efficiently, and banks can automate processes such as onboarding, fraud detection, and customer segmentation, resulting in cost reductions and better visibility into the needs of your customers.

Generative AI is becoming increasingly useful for improving the customer experience, as the value of stored datasets can be unlocked with the implementation of foundation models. These pre-trained generative AI models, either built in-house or through third-party service providers, enable banks to quickly process millions of datasets that are located across different environments, allowing them to identify common solutions to recurring issues and inform their customer interactions. As these datasets often contain unstructured data, such as customer emails and phone calls, generative AI can help banks to make sense of this information and improve their customer service. In turn, this will lead to happier customers and reduced churn rates.

Staying Compliant with AI

Customer experience is, no doubt, a key part of banking in 2023. But compliance will remain as important as it has ever been – and that’s where AI comes in to balance these obligations. Foundation models offer a level of monitoring that is not only round-the-clock but also able to detect any suspicious activity instantly, thus providing a layer of insulation against hefty fines from regulators.

By automating KYC and KYB processes, you can expedite onboarding times: directly influencing customers’ satisfaction levels and boosting retention. Moreover, continuous generative AI-driven risk monitoring negates some of the pressure on human personnel when it comes to detecting fraudulent activities.

In today’s digital arena, where consumers expect quick and secure services as a bare minimum, using these new tools to their fullest extent could be the difference between winning a new customer or losing them to your fiercest competitor.

Owning AI models

The banking sector has become increasingly reliant on cloud technology to keep up with competitive rivalries and scale activities. Following its rapid rise to prominence, however, AI is helping banks to look beyond the cloud and bring greater data control to operations.

Speed, security, and efficiency are vital criteria for financial institutions and their operational investments. To achieve these goals, banks have been turning to purpose-built large language models (LLMs) as a proven, capable, and profitable investment. Through the implementation of these LLMs that are built on a bank’s own unique data, banks can increase their operational capabilities and customer experience, without concerns over data privacy. Instead of storing valuable customer data in the public cloud when using AI, businesses should look to secure foundation models that provide greater control over how data is used, as well as ownership of the model itself.

The fourth industrial revolution is here. Generative AI is here to stay, and that goes for the banking industry too. Banks have taken their time to evaluate and adopt these new technologies in the past, but with rivals already on the move, and generative AI firmly in the public consciousness, now is the time to act.

AI exists to augment the capabilities of the bankers already in the job: giving them round-the-clock risk monitoring, automated customer onboarding, and generating insights from their unstructured data. By implementing this technology, we can free up the people in a firm to do their best work and ensure maximum return on investment.