How finance leaders stay ahead in the AI craze
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The AI craze in finance is real – here’s how leaders are staying ahead of the curve

Vivek Saxena

Finance and Accounting and Enterprise Risk and Compliance Service Line Leader

Lavi Sharma

Senior Partner- Finance and Accounting

Published

09/30/2024

Three major takeaways from our roundtable co-hosted with Gartner

The buzz around AI in finance is more than just talk—it’s a game-changing movement reshaping how the sector operates.

In fact, according to our recent survey of 550 business leaders across industries, the next two years are critical to gain a competitive edge from gen AI’s potential.

To dig deeper into how finance leaders are charting the course, we brought together a group of executives for a roundtable discussion. Joined by a Gartner expert, we gathered lessons on what’s working, what’s causing headaches, and the strategies they’re using to stay ahead.

Now, we’re sharing these insights to keep you at the forefront of the AI revolution.

1.  Top use cases

Where are CFOs getting the most use out of AI? The roundtable highlighted that most AI initiatives in finance are focused on intelligent process automation and anomaly detection.

These areas are catching on faster than others, mainly because there are now more off-the-shelf AI solutions available. Such ready-made projects are designed for widespread use and fit right into existing financial systems without any major tweaks needed.

Finance leaders are seeing the benefits first-hand. For example, they’re enhancing business operations by investing in AI-driven chatbots and virtual assistants. These tools provide staff with deeper insights into data through easy-to-use, everyday language queries, helping them make better decisions and understand customer needs more effectively.

In addition, the use of AI in anomaly detection can lead to better risk management, since AI can analyze both past and current market conditions to spot unusual patterns that might signal new threats. This lets decision-makers jump on issues early, before they get worse.

2. Biggest hurdles and how to overcome them

Despite its benefits, integrating AI into finance isn’t without its challenges. According to our discussions, the largest barrier to adoption is a lack of skilled professionals with the right expertise needed to drive AI initiatives. Issues related to data quality and governance, which can make or break success with AI, were also noted.

Interestingly, this echoes what we found in our survey of 550 senior executives, with 34% of respondents from finance functions saying that “difficulty finding, training, and retaining AI talent” and a “lack of data quality or strategy” are two of the top three barriers of gen AI adoption.

These challenges highlight the need for training programs that upskill current employees, as well as strong data management practices to help ensure the data that AI uses is accurate and reliable.

Also, to make your AI adoption even smoother, finance leaders emphasized improving privacy, auditability, and collaboration between finance and IT.

3.  It’s (still) all about the data

AI adoption rates in finance differ across industries due to varying business needs and existing technological architectures. However, the core factor driving these adoption rates is data. Specifically, the availability of data and concerns about privacy have the most significant impact on how quickly AI is adopted.

Synthetic data—AI-generated data that mimics real data without including any sensitive information—is a great way to address these challenges. It bridges the gap between the need for robust AI solutions and the limits of available data. As technology and methods continue to evolve, we’re likely to see even more innovative uses and wider adoption of synthetic data in finance and other sectors.

We also learned that tech partners are joining forces with external data providers. What does this mean for you? You’ll have access to a wider mix of data sources, including market data, customer behavior, and demographic information, that might not be available internally. Using AI and machine learning to crunch this rich data, you’ll unlock deeper insights that empower you to make decisions with confidence.

From taking data management to the next level to driving widespread use cases, it's clear that the AI hype in finance isn't just hype at all. Yes, there are challenges – adopting new technologies is never seamless – but the depths of insights finance leaders are now gaining are sparking new levels of innovation and decision-making. That's why so many now see AI as a key tool for gaining the competitive edge they've been seeking.

Staying ahead means staying informed. Use our roundtable takeaways to help get the most from your AI investment, steering it towards real-world benefits and long-term growth.

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