By Rutherford Johnson, PhD, ALM, Senior Lecturer, London School of Business and Finance
The banking and finance sectors are in the midst of a profound transformation, spurred in part by artificial intelligence advancing at lightning speed. Among the various branches of AI, Generative AI (aka “Gen AI”) stands out as a particularly disruptive force – something with the potential to revolutionise multiple fields. Unlike traditional AI that primarily focuses on pattern recognition and automating repetitive tasks, Gen AI’s focus is content creation. In fact, it can analyse extensive datasets to support and enhance decision-making processes. This techno-evolution is spurring improvements across banking operations, including risk assessment, fraud detection, customer engagement, and investment strategies.
One of the most impactful applications of Gen AI is customer service and service personalisation. Financial institutions are increasingly deploying tools like AI-powered chatbots and virtual assistants (admittedly of varying degrees of functionality and quality) to manage customer enquiries. Unlike their traditional counterparts, sophisticated contemporary AI models can produce human-like responses, comprehend complex queries, and offer tailored recommendations. This transformation not only has the potential to elevate customer engagement but also leads to reduced operational costs and guarantees continuous service availability.
But it goes beyond that. By analysing a client’s transactions, spending habits, and financial goals, Gen AI can even generate customised investment strategies, loan offers, and more – potentially better than human agents. This has the potential to raise customer satisfaction in a time when customer service often has a bad reputation. After all, clients are logically more likely to engage with services that resonate with their needs and goals. And, AI can do all this in multiple languages, too.
Another important impact is in fraud detection and risk management. The banking sector obviously faces constant threats from cybercriminals, who are continually refining their approach as technology evolves. Gen AI is redefining fraud defence by identifying anomalies in transaction patterns in real-time. Traditional systems, which usually rely on fixed rules, frequently fail to capture emergent fraud patterns. AI, on the other hand, can analyse millions of transactions, adapting to evolving fraud techniques, and generating alerts for suspicious activities – all shockingly fast.
In risk management, then, Gen AI can carry out stress tests, evaluate credit scores, and assess portfolio risks. It can simulate a wide array of economic scenarios, helping financial institutions to brace for potential downturns while also mitigating systemic risks. One of AI’s most powerful abilities is that it can analyse vast swathes of unstructured data, often finding in seconds what other analysis systems might miss completely. This helps financial institutions anticipate problems and make informed decisions proactively.
But it doesn’t stop there. AI helps in trading strategies as well. Fund and asset managers can use AI-generated models to analyse market data, pinpoint trends, and execute trades with optimal timing. Gen AI does all this autonomously, adapting based on ever-fluctuating market conditions.
For all its benefits, AI in banking and finance has its challenges, not to mention ethical considerations. The ever-present issues of data privacy and security are definite concerns, given the sensitive nature of data handled by financial institutions. AI models must be designed to protect data privacy; otherwise, they risk becoming a double-edged sword.
Another key worry is that there could be unintentional bias in AI-generated financial decisions. Thus, these systems warrant rigorous supervision. Although AI itself has no inherent bias in the same sense that humans might, AI models are trained on historical data. So, it is possible that they may inadvertently perpetuate existing biases if not carefully managed. And, there are other operational risks. For example, an over-reliance on AI for critical decisions or strategy could introduce systemic vulnerabilities. And, this has implications in regulatory frameworks as well.
So, the best approach is to consider AI a powerful tool to support human-centric work rather than making AI a sole decision maker.
Taken all together, the prospects of Gen AI within banking and finance appear most promising. Continued advancements in machine learning, natural language processing, and even quantum computing will likely drive technology further at an amazing pace. When managed properly, institutions that embrace AI-driven transformation will likely gain a competitive advantage. Despite its challenges, AI’s advantages in finance are too significant to overlook. Strategic adoption is a necessity rather than an option.
