Essential Insights – 2024 AI In Finance Survey

Hey there, finance enthusiasts! I remember when “AI in finance” would have sounded like something straight out of a sci-fi movie. Fast-forward to 2024, and it’s as commonplace as your morning cup of Joe. With that in mind, I recently conducted the 2024 F9 Finance AI In Finance Survey to delve deeper into this fascinating world.

The aim? To understand how AI is reshaping the finance landscape and what that means for folks like you and me. Oh, and before you ask, no, I wasn’t trying to find out if robots are planning to take over Wall Street! I’m saving that for the sequel.

Key Takeaways

There is a gap between desire and adoption. 46% of CFOs are excited to use AI tools in Finance but only 19% of companies surveyed have actually adopted it.
Fraud detection has been the primary entry point for companies into AI, followed by customer service.
Finance employees are not prepared for AI. Only 30% reported a high degree of familiarity with the technology. And for 28% of respondents, a lack of skilled professionals was the biggest challenge they encountered in rolling out AI tools.
Once people understand AI tools, there is a high likelihood that they have a favorable perception and believe that AI tools will improve the finance field.
Despite being top of mind for public perception, more than 40% of companies have no guidelines around the ethical or fair use of AI tools.
Cost savings and efficiencies are, no surprise, the biggest benefit companies have found from AI tools in finance.

AI In Finance Survey Methodology

Here’s the scoop on how I conducted the survey.

Just like a friendly chat over coffee, I gathered insights from a diverse group of finance professionals, ranging from bright-eyed seniors to seasoned executives.

I looked for survey participants across various organizations like banks, insurance companies, investment firms, fintech startups, and non-financial corporations.

I also looked for a broad range of years of experience to get different perspectives on the topic.

Every participant received the same 9-question survey (plus the three demographic questions above). The survey ran for a month across multiple platforms, such as LinkedIn, F9 Finance, and partner websites. I received 2,000 complete and validated responses, which is statistically significant at a +/- 5% margin of error. We excluded any results where the entire survey was not completed or where the IP address could not be verified.

I asked about their experience in the finance industry, and how well they knew AI technologies. I was curious to know if their organizations had embraced AI and, if so, where they first implemented it. Was it for risk management, customer service, or perhaps process automation?

Next, I wanted to understand what benefits they’ve reaped from AI. Did it increase efficiency or reduce costs, or maybe it helped improve customer satisfaction? But, just like any good story, there are always challenges. So, I asked about those, too: data privacy concerns, high implementation costs, or maybe the lack of skilled personnel.

Ethics is a hot topic when it comes to AI, so naturally, I was eager to learn how organizations ensure ethical AI use. And of course, I wanted their opinion on AI’s impact in the next five years and its role in shaping the finance industry’s future.

And finally, I wrapped up the chat with a key question: What’s the secret ingredient for successful AI adoption in finance? Is it a clear strategy, a skilled workforce, strong data infrastructure, ethical guidelines, or stakeholder engagement?

Detailed Analysis of Results

Now that I’ve laid out how our survey was conducted let’s dive into the good stuff—the results!

Picture this as a treasure map. We’re going to explore each X mark (read: survey section), and I’ll be your friendly guide, helping you understand what these shiny nuggets of information mean for you.

Question 1: How Familiar Are You With AI Technologies In Finance?

Finance organizations still have a long way to go with AI as only 30% of finance professionals surveyed were very familiar or extremely familiar with the technology. A much larger share, 42% showed no or limited familiarity with the technology. This is going to be a significant roadblock for organizations looking to roll-out automated tools

Question 2: Has Your Organization Adopted AI Technologies?

I can’t say I was surprised, but only 19% of organizations have actually adopted AI technology and a full 40% don’t even have plans to. This is a sharp contrast to the 46% of CFOs who are reportedly excited to use the technology in Finance according to CNBC.

Question 3: In Which Area Did Your Organization First Adopt AI?

Fraud detection was a clear leader for organizations to pilot AI with 26% of the share. This was followed closely by customer service at 21% and process automation at 20%.

Why has fraud detection grown so fast? AI excels in analyzing vast amounts of transaction data in real-time, identifying anomalies that could indicate fraudulent behavior. By continuously monitoring banking transactions, app usage, and payment methods, AI systems can quickly flag unusual patterns that deviate from a user’s typical financial behavior, thus accelerating the fraud detection process.

One of the most powerful aspects of AI in fraud detection is its ability to learn and adapt over time. Machine learning algorithms analyze historical transaction data to identify patterns and trends associated with fraudulent activities. This continuous learning process enables AI systems to become increasingly effective at spotting potential fraud, even as tactics evolve.

That said, as generative AI continues to evolve, I would expect these categories to become more balanced.

Question 4: What Is The Main Benefit To Your Organization From Adopting AI?

No surprise here, 40% of organizations found the main benefit from AI to be reduced costs, with another 20% mentioning increased efficiency. Next time I do the survey I may include verbatims because I was surprised that 20% of companies increased revenue.

Question 5: What Has Been The Biggest Challenge In Adopting AI?

28% of those surveyed mentioned the lack of skilled personnel as the biggest challenge, and another 28% mentioned data privacy and security concerns.

The rapid advancement and adoption of AI across various sectors have led to a high demand for skilled professionals who can develop, implement, and maintain these technologies. However, the supply hasn’t quite kept up, leading to a talent gap.

As more data is collected and analyzed by AI systems, concerns about privacy and security naturally increase. The sensitive nature of financial data makes it even more critical for companies to address these concerns effectively:

Implement Robust Data Protection Measures: Ensuring that the latest cybersecurity technologies and practices are in place is crucial. This includes encryption, secure access controls, and regular security audits.
Adopt Privacy-by-Design Principles: Companies should integrate privacy considerations into the development process of new products and services, rather than treating them as an afterthought. This involves minimizing data collection to what’s strictly necessary and giving users more control over their information.
Stay Compliant with Regulations: Keeping abreast of and complying with data protection regulations (such as GDPR in Europe or CCPA in California) not only helps avoid legal penalties but also builds trust with customers.

Question 6: How Does Your Organization Ensure The Ethical Use Of AI in Finance?

Well this is a bit concerning. 45% of companies using AI do not have specific measures in place around the ethical use of these systems.

Ethical considerations in AI encompass a broad range of issues, including but not limited to bias and discrimination, transparency and accountability, privacy, and the potential displacement of workers.

These concerns are not merely theoretical; they have practical implications for individuals and society at large. For instance, biased algorithms can lead to unfair treatment of certain groups, while lack of transparency around AI decision-making processes can undermine trust and accountability.

Question 7: What Impact Will AI Have On Finance In The Next 5 Years

Talk about an uneven distribution. A quarter of respondents believe AI will have a significantly positive impact, another quarter are neutral, and another quarter believe it will be somewhat negative. Digging into the results, this is closely correlated with those who are familiar with AI. If you “get” AI tools and understand how they are leveraged, you are statistically more likely to rate this question in a positive light.

Question 8: How Important Is AI In Shaping The Future Of Finance?

Similar to the last question, a full quarter of respondents believed AI is extremely important to the future of finance while another quarter believed it was not important.

This is again highly correlated with those who are familiar with AI tools and also correlated strongly with people who work at companies that have adopted AI tools.

Question 9: What Do You Think Is The Key Factor For Successful AI Adoption In Finance?

Strong data infrastructure took the lead with 38% of the share, followed by skilled workforce at 24%.

Data is often described as the fuel for AI. Without a robust data infrastructure, it’s challenging to collect, store, process, and analyze the vast amounts of information necessary for training AI models and making them accurate and reliable. A solid data infrastructure enables:

Efficient Data Management: Organizing and managing data effectively across its lifecycle, ensuring it’s accessible, secure, and compliant with regulations.
Quality and Integrity of Data: Ensuring data is clean, accurate, and free from biases is crucial for the performance of AI systems.
Scalability: Being able to scale data storage and processing capabilities as the organization’s needs grow.
Interoperability: Facilitating the integration of diverse data sources and systems to provide a unified view and support comprehensive analytics.

Implications for Finance Teams

Now, let’s talk about what all these survey results mean for you and your finance team. Consider this the golden key that unlocks the treasure chest of opportunities AI can bring to your financial management strategies.

Embrace AI, Don’t Fear It

First things first, let’s address the elephant in the room. According to a recent KPMG survey, 55% of executives do not expect AI to replace financial reporting jobs. So, if you’ve been losing sleep over robots stealing your job, it’s time to put those fears to bed. Instead, view AI as a friendly sidekick who’s here to make your life easier, not take over your job.

Employee Development Is Critical

Successful AI adoption requires more than just implementing the technology; it necessitates a deep understanding of how AI can be integrated into existing workflows and processes. Through targeted training and development efforts, employees can gain insights into the practical applications of AI within their specific roles, leading to smoother integration and higher levels of acceptance and engagement with AI tools.

When employees see opportunities for growth and advancement through AI-related training and development, they are more likely to feel valued and motivated to contribute to the organization’s success.

Get Ahead Of The Curve

AI adoption is still really low. While this comes with its own set of challenges, it also gives first movers an opportunity to set the standard and become leaders in Finance, Accounting, and FP&A. This can also help recruit and retain top talent who see forward-thinking organizations.

Data, Data, and More Data

1. Accuracy in Predictive Analytics: Financial institutions rely on predictive analytics for credit scoring, market analysis, and risk assessment. The accuracy of these predictions hinges on the quality of data fed into AI models. Inaccurate or biased data can lead to flawed insights, potentially resulting in poor decision-making.

2. Enhanced Fraud Detection: AI systems can analyze patterns and detect anomalies that may indicate fraudulent activities. The effectiveness of these systems depends on comprehensive and detailed transaction data to accurately distinguish between legitimate and suspicious behavior.

3. Personalized Customer Experiences: AI enables the delivery of personalized financial advice and product recommendations based on an individual’s financial history and preferences. High-quality data ensures that these recommendations are relevant and beneficial to the customer.

4. Regulatory Compliance: Financial institutions must comply with a myriad of regulations, including those related to anti-money laundering (AML) and know your customer (KYC) requirements. AI can automate and improve the efficiency of compliance processes, but this requires accurate and up-to-date data to ensure that institutions meet regulatory standards.

Achieving Strong Data for AI Applications

Organizations should establish mechanisms for collecting high-quality data from a variety of sources, including internal systems, social media, and third-party providers. Integrating this data into a cohesive system allows for a more comprehensive view of customers and operations.

Establishing a strong data governance framework helps ensure that data across the organization is managed according to clear policies and standards. This includes addressing data privacy, security, quality, and compliance issues, which are particularly pertinent in the finance sector.

For More Information

For more information on the survey or for detailed results, email [email protected]

Have any questions? Are there other topics you would like us to cover? Leave a comment below and let us know! Also, remember to subscribe to our Newsletter to receive exclusive financial news in your inbox. Thanks for reading, and happy learning!