A strategic and balanced approach to leveraging generative AI will guide institutions towards successful and sustainable integration, paving the way for an era of technological excellence in banking and payment
In the continuously evolving landscape of banking and payments, embracing technological innovation is an imperative for survival and growth. Among the plethora of emerging tech, Generative Artificial Intelligence (AI) stands out for its potential to reshape multiple aspects of the banking and financial services industry.
Incorporating Generative AI into banking and payments transcends technological adoption; it is a strategic transformation.
In an era in which customers demand seamless digital experiences, financial institutions that adopt Generative AI demonstrate innovation and tech leadership. This can enhance the brand’s reputation and attract the right customers and partners, while elevating the financial institution’s value to garner interest from investors.
The banking and payments space offers a fertile ground for integrating Generative AI with existing applications to deliver tangible value that will fuel innovation and drive a competitive edge. Examples of these opportunities include personalizing customer interactions, detecting fraud, improving KYC processes, creating customer-friendly financial advice, embedding digital assistants in client service operations, automating compliance reporting, and personalizing chatbot interactions.
Leveraging Generative AI for operational excellence remains unexplored. There is tremendous potential to complement client-service operations, machine learning operations (MLOps), and financial operations (FinOps) for complex data analysis, contextual understanding, risk assessment, and customer engagement.
While institutions address the possibilities of Generative AI and its strategic deployment, a few aspects require consideration. One of these is whether the Large Language Model strategy should be centralized or decentralized. This decision must be made in the context of the business requirements as centralization will offer uniformity and decentralization will allow customization for specific needs. Another decision is choosing between integrated internally API connections or internal model hosting. Evaluating factors such as the need for data privacy, real-time responsiveness, and control can facilitate this decision.
Another interesting aspect for financial institutions to delve deeper into is how Generative AI can complement existing Machine Learning (ML) solutions. Banking and payment operations often involve intricate data analysis; Generative AI can be leveraged for tasks requiring contextual understanding, such as risk assessment and customer engagement.
Use of Generative AI comes with its own challenges – such as addressing security, privacy, and compliance, where models must be trained on anonymized data and deployed in secure environments to prevent breaches, especially considering the sensitive nature of financial data. Ownership of content generated must be explicitly defined, with ownership agreements for automated financial reports and analysis. AI models must be designed to counteract any bias to ensure fairness and equitable output. Additionally, navigating and adhering to regulatory terrain is important – this typically includes transparency in data handling, secure user consent, and data retention policies, to name a few.
Senior Vice President of Engineering and Transformation,
Global Services,
Fiserv
One of the biggest advantages of leveraging Generative AI is the experimentation power it lends to internal teams, helping to strengthen expertise, build a culture of collaboration and continuous learning, and fuel innovation. All of this while keeping the customer at the center. That said, businesses are nascent in venturing beyond using AI to automate repetitive and manual tasks to enable more knowledge-based work and decision making. While organizations find the most effective ways to bring Generative AI into their mainstream business, upskilling and reskilling teams will be integral to adoption. This will have to be strongly supported by a culture of collaboration, continuous learning, and customer centricity.
Foundational to any AI deployment is a comprehensive cost-benefit analysis considering parameters like API subscription costs, hosting investments, and potential productivity gains. Additionally, it is important to evaluate whether the projected benefits align with expenses such as model development, deployment, and maintenance.
In a sector where innovation sets the pace, a strategic and balanced approach to leveraging generative AI will guide institutions towards successful and sustainable integration, paving the way for an era of technological excellence in banking and payment.
