The finance sector is increasingly harnessing the capabilities of artificial intelligence (AI) to enhance productivity and improve customer experiences. Experts from Toptal shed light on the innovative possibilities and the hurdles that lie ahead.
After a period of gradual development, AI is now catalyzing rapid innovation across various industries. The finance sector, traditionally cautious due to its regulatory nature, is starting to adopt AI for tasks such as analysis and forecasting, fraud detection and prevention, personal finance management, compliance, and customer service. However, both challenges and opportunities persist.
In 2021, financial institutions were viewed as relatively behind other sectors in AI adoption, primarily due to regulatory issues, insufficient AI infrastructure, and a shortage of skilled professionals.
The emergence of large language models (LLMs) and generative AI (Gen AI) in early 2023 marked a turning point. According to IDC, global spending on AI hardware and services is projected to surpass $500 billion by 2027, with financial organizations expected to double their AI investments during this period, as reported by the International Monetary Fund. This surge is driven by AI’s potential to minimize human error, forecast market trends, expedite document analysis, and process vast datasets. However, the risk of enabling advanced forms of theft, fraud, cybercrime, or even triggering a financial crisis remains a significant concern. As adoption accelerates, caution will be paramount.
In this article, three Toptal finance experts—Carlos Salas Najera, former Head of Equities at London & Capital; Arvind Kumar, who has worked globally with KPMG, Goldman Sachs, and EY; and David Quinn, a seasoned finance professional running his own wealth management firm—share their insights on the intersection of AI and finance.
How Is AI Transforming the Finance Sector?
Financial institutions are leveraging AI and LLMs to streamline data-intensive tasks, detect fraud, and enhance customer service. Despite a slow start, the finance industry is rapidly accelerating its adoption of these technologies.
“Over the past decade, a significant shift occurred when firms like BlackRock adopted AI, compelling others to either catch up or risk obsolescence,” notes Salas, who specializes in AI and machine learning for investment applications. In 2023, BlackRock integrated AI across its operations to refine investment strategies, improve client outcomes, and foster innovation. Salas emphasizes that the advantages of AI in finance—such as enhanced operational efficiency and informed decision-making—are well-documented. However, some investment firms remain hesitant due to the need to modernize legacy systems and the complexities of integrating new technologies into existing financial models.
The rapid rise of ChatGPT and the growing acceptance of AI in everyday life have alleviated some of these concerns. “Reluctance has diminished as more success stories and tangible benefits of AI adoption have emerged,” Salas explains. “Moreover, regulatory bodies are becoming more open to AI applications in finance, facilitating its implementation. Nonetheless, significant gaps in regulatory frameworks still lead many financial firms to adopt a cautious, wait-and-see approach.”
Current AI Use Cases in Finance
A survey of around 400 financial services professionals worldwide reveals the following key applications:
- Risk management: 36%
- Portfolio optimization: 29%
- Fraud detection (transactions/payments): 28%
- Algorithmic trading: 27%
- Document management: 26%
- Customer experience: 26%
Four Key Areas Where AI Can Make an Impact in Finance
- Data Analysis
AI’s primary advantage in finance lies in its ability to read, classify, and extract insights from vast and complex datasets that are beyond human capacity. Companies often gather extensive information, yet over two-thirds may go unused.
Unlike traditional automation tools, machine learning algorithms can analyze data to identify patterns, enabling predictions or decisions without explicit programming. For instance, JPMorgan Chase previously relied on human workers to manually review commercial loan agreements, a process fraught with complexity and prone to errors. To streamline this, the bank developed a contract intelligence platform called COIN, which uses natural language processing (NLP) to automatically extract and analyze key information from loan documents.
The implementation of COIN has significantly reduced the time required for document reviews, transforming a process that once took 360,000 hours annually into one that now takes mere seconds, all while minimizing errors and maintaining operational efficiency.
- Portfolio Optimization
AI enables companies to leverage previously underutilized data in real time, enhancing their responsiveness to market changes, particularly in trading. Renaissance Technologies pioneered sophisticated algorithms over 40 years ago to exploit fleeting price discrepancies, achieving an impressive 63.3% return from 1998 to 2018.
LLMs can be fine-tuned for similar purposes, outperforming traditional algorithms by analyzing vast amounts of unstructured data and adapting to new information. BlackRock’s latest AI initiatives utilize specialized LLMs trained on targeted
Is AI the Future of Finance?
As artificial intelligence continues to advance at a rapid pace, financial institutions must prioritize ongoing innovation and adaptability to maintain a competitive edge. The development of financial AI technologies necessitates a proactive strategy that includes investing in continuous staff training, regularly updating systems, and exploring new AI applications.
Collaboration is essential in the financial sector, particularly when navigating the complexities of AI. No single organization can tackle these challenges independently. By partnering with banks, fintech firms, regulators, and technology providers, companies can address shared concerns such as data privacy, AI model accuracy, and regulatory compliance. These collaborative initiatives can lead to collective solutions and help establish industry-wide standards and best practices for AI implementation, ensuring a cohesive and ethical integration of technology within financial services.
In the short term, our three Toptal experts emphasize a key takeaway for financial organizations embarking on their next AI project: exercise patience and maintain realistic expectations. Salas points out that while AI holds significant promise, it is not a cure-all, and its deployment demands careful attention to ethical, regulatory, cultural, and technical challenges. Companies should prioritize building a strong infrastructure, investing in data quality and governance, and nurturing a culture of innovation and continuous learning to fully realize the advantages of AI in finance. If organizations approach these tasks diligently, the benefits will eventually materialize.
