In a conversation with PYMNTS, Michael Haney, head of product strategy at Galileo Financial Technologies, highlighted the pivotal role of generative artificial intelligence (GenAI) in revolutionizing financial services. He emphasized that combining machine learning with GenAI is poised to usher in a new era of back-office productivity, ultimately transforming how financial organizations use data to deliver hyper-personalized experiences. Haney’s insights are part of the “What’s Next in Payments: Payments and GenAI” series.
Embracing GenAI for enhanced efficiency
While financial services organizations have been increasingly adopting machine learning to enhance productivity, the shift toward generative AI is gaining momentum. GenAI has the potential to supercharge back-end operations, improving productivity, efficiency, and overall quality. Unlike traditional machine learning that may require manual adjustments, GenAI models possess the capability to learn and adapt swiftly as conditions change, offering a dynamic approach to problem-solving.
Within the realm of machine learning, neural networks play a crucial role. These networks aim to mimic the workings of the human brain, often incorporating multiple layers for enhanced capacity, efficiency, performance, and accuracy.
Generative AI represents a significant advancement in machine learning, breaking away from the rigid rules and engines of the past. Modern techniques rely on transformers, or deep learning models, which can predict the next word in a sentence or suggest images, videos, or music. This capability results in human-like responses at unprecedented levels, offering a more intuitive and personalized user experience.
Transforming financial services workflows
In the realm of payments and financial services, AI has the potential to transform various workflows and interactions. One key area of impact is client servicing, where AI-driven insights can enhance operational productivity. However, gaining consumer consent for data sharing is crucial as utilizing data in innovative ways becomes increasingly important.
Operational teams are now equipped with natural language queries for data visualization, providing valuable insights into payment volume fluctuations and other critical data. Generative AI-powered technologies, like virtual assistants, streamline operations for customers and bank staff, simplifying tasks such as customer service and fraud detection.
AI can also play a pivotal role in improving loan decision processes, supporting the entire loan lifecycle from applications to credit collections. In commercial settings, AI aids treasury managers in analyzing cash flow, interest rate changes, and liquidity risk.
Hyper-personalization and consumer guidance
Hyper-personalization is a natural outcome of AI integration, but it requires scrutiny to prevent bias in models. Haney emphasized that consumers often grapple with many payment options, causing guidance to select the most suitable payment method based on speed, cost, and risk.
Consumers can become overwhelmed by the array of payment choices, making AI-powered recommendation engines invaluable. These engines help users navigate the trade-offs and recommend the optimal payment rails for specific transactions.
Moreover, structured and unstructured data, combined with real-time context, can be leveraged to provide tailored offers at the point of sale, enhancing the customer experience. AI is also finding applications in client service operations, marketing operations, and product development within the financial services sector.
Haney anticipates that the evolution of technology will lead to the emergence of specialized large language models tailored to specific verticals. Decision-making use cases are expected to be at the forefront of innovations in the coming months and years.
In summary, GenAI, powered by machine learning and generative artificial intelligence, is reshaping the landscape of financial services. It promises to enhance productivity, improve efficiency, and deliver hyper-personalized experiences while ushering in a new era of data-driven decision-making.
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