Asif Alam is Chief Executive Officer at Compliance.ai, a disruptive AI company combining natural language processing and expert insights.
AI is an amazing tool and bank IT departments are slowly turning their departments towards a more generative approach to data. But this means that their risk management, risk mitigation and regulatory strategies must keep up with their AI ambitions.
In banking, the intersection of generative AI and compliance is becoming increasingly significant. The deployment of AI tools in the industry is not only about operational efficiency and customer experience enhancement but also about managing regulatory requirements.
Here are the good, the bad and the ugly aspects of AI integration to reveal best practices.
Poisoning The Data Well
There are four specific risks most experts associate with AI: data poisoning, reverse engineering, deep fakes and non-compliance.
Take data poisoning. A decade ago, banks had to worry about how to manage humans in the data chain. When an entire fortune can be wiped out with an errant SQL query, managing data becomes binary: some received access, but many did not. Now, with AI, that data is likely not human-readable.
When users manipulate these datasets with malicious intent or simply feed them erroneous information, the consequences can be far-reaching. Consider the ENRON files. These files, a large corpus of emails released during the 2006 trials, became a popular data set for many AI developers. Unfortunately, the data itself was flawed. Why? Because all of it came from the same group of humans—mostly men and mostly in Texas—and their biases trickled into LLM.
This corrupted data leads the AI to learn patterns and associations it should not, culminating in skewed outputs, biased results and harmful decisions.
Reverse engineering, also known as model extraction, poses another significant risk—particularly in competitive and security-sensitive domains. In this scenario, bad actors or rival entities could reverse-engineer an AI model, gleaning insights into its structure, functionality and the data it was trained on.
Next, we have deep fakes, the process of faking a person’s image or credentials. The advent of deep fake technology introduces a profound challenge to identification technologies, particularly facial recognition and know-your-customer (KYC) procedures that are integral to the banking and financial sectors.
Lastly, the global AI regulatory landscape is currently a patchwork of varying standards, guidelines and laws. The lack of a unified regulatory framework introduces uncertainty for organizations navigating the integration of AI into their operations. The recent formation of ai.gov, a U.S. task force on AI regulation, may begin to bring more clarity to the way we all use AI everywhere. Until then, however, a fragmented landscape can hinder innovation and potentially result in unintended legal repercussions.
AI Helping Itself
These same biases can be easily reduced, and the dangers ameliorated as long as your IT team is ready to get its hands dirty. First, remember that because no one knows what is inside most large language models, you should take special care to select a model or provider that offers at least some visibility into their systems. While this is nearly impossible right now, you can expect a time when companies will talk up the source of their training data.
Further, we can also look to AI companies that focus on attribution. These startups are appearing at a rapid clip and they will ensure that a particular bit of generative text, code or media can be properly attributed to its creators. Eventually, this is likely to be vital for any company considering the implementation of AI.
Once we’ve solved the attribution problem, we can easily use AI to generate content and extract data.
By employing AI algorithms capable of sifting through, analyzing and summarizing this data, banks can extract valuable insights more swiftly and accurately than ever before. This not only aids in making informed decisions but also ensures that the bank stays ahead of industry trends and customer expectations and remains within regulatory compliance.
Further, building streamlined processes for teams is another critical aspect of AI implementation. The traditional banking workflow is often laden with manual processes that are time-consuming and prone to errors. AI can automate many of these tasks, from data entry and document verification to customer service inquiries. By doing so, it frees up human employees to focus on more complex and strategic tasks that require a human touch.
The AI Train Is Now Boarding
Entering 2024, financial managers who understand AI regulation and management are going to be in high demand. Compliance and regulatory controls will be paramount and without a coherent and cogent vision, most banks could be stuck in the past.
The key to AI and banking is careful adoption. The benefits could soon outweigh the risks and, as such, you can expect plenty of questions about it at the next board meeting. What, then, can AI do for a traditional banking institution?
Operational efficiency, staying ahead of compliance regulations and risk mitigation are critical. The financial sector is stringent and proactive risk management is vital, not just for legal sanctity but also for maintaining customer trust and institutional reputation.
Next, enhancing customer experience is another important aspect of banking innovation. Today’s consumers demand fast, convenient and personalized services. Financial institutions are responding by leveraging technology to provide more tailored products, faster response times and improved customer interaction channels.
Amidst these advancements, achieving a competitive advantage has become challenging. The sector is densely populated, and standing out requires more than just competitive pricing; it demands innovation, service excellence and a relentless pursuit of operational efficiency. Financial institutions that successfully integrate new technology can help position themselves as industry leaders, potentially earning a larger market share. AI is just one part of that expansion.
The essence of continuous learning and future-proofing are vital. Banking is ever-evolving, driven by technological innovations, consumer expectations and changing regulatory environments. Institutions with a culture of innovation ensure they are not just keeping pace—but are ready to adapt to future disruption.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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