Why the finance industry should be careful with generative AI and risk management

With ChatGPT taking the world by storm and Nvidia saying that AI has already helped 36% of financial services executives cut costs by 10% or more, the rise of generative AI (GenAI) is changing how financial companies manage their essential business functions.

But our AI technology, like ChatGPT, could be better. It has a lot of flaws that must be addressed. This piece will look at the business cases for and against using AI for risk management and compliance in the financial industry, as well as why organizations should take action but move slowly at this early stage in the lifecycle of this groundbreaking technology.

The start of the AI age

When ChatGPT came out in November 2022, it seemed like the start of a new technological era. OpenAI's revolutionary chatbot had one million users in just five days, and it had more than 100 million monthly users in just two months, which is a fantastic rate of growth. It took a while for companies in all fields to start looking for ways to replace workers with AI. Soon, newspapers around the world were full of stories about how AI was better at all kinds of chores and activities than humans.

Of course, only some have been happy to see AI come into the world. Several countries have moved quickly to ban it, but there are still a lot of big questions about things like data privacy and job stability. But now that the dam has broken, AI is not only here to stay, but it also looks like it will change the way many companies work at the core.

Changing how we handle risk and compliance

Risk and compliance management is one of the most essential parts of the modern financial business, and mistakes can be costly. This was made clear when the Prudential Regulation Authority (PRA) of the United Kingdom fined Credit Suisse International and Credit Suisse Securities £87 million for severe problems with risk management and governance between January 1, 2020, and March 31, 2021, related to the firms' dealings with Archegos Capital Management. It was the most significant fine ever given by the PRA, and it was the only time a PRA enforcement probe found violations of four PRA Fundamental Rules simultaneously.

Managing financial risk and ensuring you're following the rules can be challenging because it can take time. As companies grow, they become more complicated. They form new foreign business partnerships and process more and more data, which means they are subject to more risk and compliance laws.

AI is an ample opportunity in this area because it can automate boring, everyday jobs, give quick assessments, and help financial institutions better understand and deal with the risks they face. The potential is enormous, whether to find holes in policies and control systems or to analyze thousands of pages of rules from multiple jurisdictions in a second.

Not a safe thing to do.

That doesn't mean AI doesn't come with its risks. First, risk management and compliance tasks are just starting to use AI. Since they have yet to learn much about it, there will almost certainly be problems and mistakes as they get used to it. Many risk pros work around the clock to determine how to best add AI to programmes and processes running well for a long time.

Also, AI in its current state could be better. Even though robots like ChatGPT have made some good news, they have also made a lot of bad news, especially when it comes to high-profile mistakes, biased information, and not knowing much about the world after 2021 (at least for now).

So, for financial institutions to get the most out of AI's massive potential without running into its current limitations, they need to look closely at both the opportunities and challenges it brings. Risk and compliance managers should be at the forefront of exploring AI to find the best way to use it. Knowing how the technology works, what it can be used for, and what risks it offers should be basic requirements before partial or full-scale deployment is even considered.

Better than other people?

As was already said, one of the best things AI can do for financial institutions is automate and speed up essential but time-consuming jobs that are hard for humans to do because they are so dull. For example, it can look at thousands of pages of complicated global rules before making a correct suggestion about where specific restrictions apply. This feature can make a big difference in how much work a bank's risk and compliance team has to do, so team members can spend a lot more time on activities that are important for the business as a whole.

But it's important to remember that AI-driven systems are only as good as the data they have to work with. All AI systems learn by processing the information that is given to them. If this information is wrong or biased, it will quickly change the way the AI system thinks, leading to inaccurate results. When it comes to risk management, these problems can cause a lot of trouble. For example, an AI must use more data to spot essential risks or follow the rules.

Another critical worry is the possibility that robots could replace human workers and what that would mean for the job market as a whole. Even though it's clear that AI will be used increasingly to automate tasks currently done by humans, replacing people isn't without its problems. Human insight, judgement, and decision-making have a value that can't be replaced. This is especially true in areas as important as risk management, where experience plays a huge part everywhere.

The key is to find the right mix.

With all of these things in mind, how can organizations find the right mix that lets them take advantage of AI's benefits while protecting themselves from the risks that come with it? In an ideal world, a carefully structured approach would be taken in which compliance and risk managers are given the power and support to look at how processes and procedures for AI deployment are implemented, and everyone in the company can see and understand what is going on.

Now is the time to take action to understand the risks and possibilities and to start making business cases for transformational change. Test-and-learn programmes should be thought about and used to quickly learn, evaluate, and implement lessons with the right mix of human capital and operational efficiency.

In the long run, it's essential to set up adequate controls over what AI can do and how well it does. These should include committing to manual oversight, ongoing ad hoc testing, and putting in place other relevant methods to make sure AI works within the organization's risk appetite and compliance framework. The best results will likely come from a hybrid approach in which AI and people work together.

It's essential to remember that AI is just starting on a fascinating path. If current problems can be solved, AI can significantly affect how well risk management and compliance work in the financial business as a whole. Especially as regulatory settings worldwide continue to change quickly, AI's ability to adapt and provide insights into new risk requirements at speeds far faster than humans will likely be beneficial.

On the other hand, there are still a lot of questions about AI and how to use it. These questions are likely to stay for a long time. AI is already getting more and more calls for better control and rules until more is known about it. How this talk goes will significantly affect how many people join in the coming months and years.

Aside from this, the AI gold rush is in full swing, and those who can quickly adopt a well-thought-out and structured approach to AI are likely to benefit the most from the better risk management it offers.