Waiting for a disruptive technology is like waiting for a bus – you wait ages and then two [or possibly three] come along at once. In our digital world, this saying can be updated to reflect the arrival of generative AI (GenAI) and its key components: Large language models (LLMs), synthetic data generation, and digital twins.
An Accenture report reveals that banks in the US are poised to benefit the most from GenAI productivity gains, with potential annual increases in value estimated between $200 billion and $340 billion. With the banking sector experiencing significant improvements in productivity, especially in retail and corporate sub-sectors, it’s evident that GenAI is a true game-changer.
According to HFS Research, GenAI’s impact is expected to surpass all major technological breakthroughs in history by 2025, including the printing press, steam engine, internet, and smartphone. This rapid evolution towards a revolutionary era is leaving hesitant or uninformed individuals at a disadvantage.
Forward-thinking financial firms have already started deploying GenAI, reaping benefits in operational capabilities and speed of development. The shift from an evolutionary period to a revolutionary epoch signifies the beginning of a significant transformation in banking practices.
While the revolution brought about by GenAI is bloodless, its potential is immense. With the right deployment and utilization, GenAI can reshape traditional banking activities like lending, fraud detection, and customer interactions. Each component of GenAI – LLMs, synthetic data generation, and digital twins – offers unique benefits and applicability in the banking sector.
Understanding Large Language Models (LLMs)
LLMs are machine learning models that excel in processing natural language, generating text, and engaging with users. These models have a wide range of applications in banking, from personalized customer service to enhancing credit analysis and fraud prevention capabilities.
Financial criminals are also leveraging AI advancements for fraudulent activities, emphasizing the need for banks to stay vigilant and utilize GenAI for robust fraud prevention measures.
Exploring Synthetic Data Generation
Synthetic data generation allows the creation of on-demand data using algorithms or rules rather than real-world data. This advancement is crucial for improving data quality in areas like climate risk assessment, payment fraud detection, and prudent lending practices.
Additionally, synthetic data can aid in training robust models for fraud detection, risk assessment, and customer intelligence, all while ensuring compliance with regulatory standards.
Digital Twins in Banking
Digital twins are virtual models of real-world objects or systems created using historical or synthetic data. While some may question their relevance in banking, the potential for using digital twins in performance improvements, particularly in IoT-connected devices like ATMs, cannot be overlooked.
Embracing the GenAI Revolution
In the words of The Beatles, “You say you want a revolution, Well, you know, We all want to change the world…” GenAI is revolutionizing the banking sector at an unprecedented pace, opening up new possibilities and opportunities for growth. This is just the beginning of a transformative journey in banking.
