What Is AI in Finance? Use Cases, Benefits, and Career Paths

AI has everyone talking. In 2026, nowhere is that truer than in finance.

At numerous finance and fintech summits across the globe, AI is a hot topic. It dominates keynotes, prompts thought-provoking sessions, and draws thousands of attendees.  

Not familiar with the possibilities of AI in finance? Let’s fix that—starting with common applications of AI in the field and exploring how they can transform your career.

In the age of deep-learning and LLMs, artificial intelligence (AI) has quickly become a cornerstone of finance. This is in large part thanks to natural language processing (NLP); because LLMs can read, analyze, and summarize text in a way that mimics human processing, they can perform both financial analysis and customer service with efficiency. 

While generative AI use initially centered on marketing and customer operations, it’s gaining recognition for its more widespread applications. And it’s not just banks and financial institutions. Credit card companies, hedge funds, insurance companies, and fintech firms are all tailoring AI tools to their unique needs. 

According to the McKinsey Global Institute, GenAI is predicted to make waves in the banking sector—driving additional annual revenue of $200 billion to $340 billion. Recent headlines in business journals and finance publications can confirm: the future of finance is synonymous with AI.

A few factors contribute to the fast pace and wide scale of AI adoption. 

For one thing, every transaction is data. From using Apple Pay to withdrawing funds from your bank, from shopping online to Venmo-ing a friend for coffee—our daily interactions with money become data for the finance sector.

But what happens when there’s too much data? In 2025, Visa processed an average of 901 million transactions per day. AI use allows finance professionals to keep up with the sheer volume of data and gain meaningful insights faster. 

The push for operational efficiency and real-time decision-making has also spurred AI adoption forward. Financial firms who don’t embrace AI may become bogged down in tedious manual processes and time-consuming tasks—and, in the process, fall behind their competitors.

Machine learning models have played a role in the financial sector since the 2010s, driving improvements in credit and insurance risk analysis; high-frequency trading; and anti-money laundering initiatives. As AI tools continue to evolve, their integration within financial firms and organizations is steadily growing. 

Some of the most prominent use cases for AI technologies include: 

• Algorithmic Trading and Investment Management  

• Automation of Financial Operations   

• Back-End Processing

• Credit Scoring and Lending Decisions   

• Customer Service and Personalization

• Fraud Detection and Financial Crime Prevention   

• Internal Software and Code Development/Harmonization

• Risk Management and Forecasting 

It’s easy to equate AI with “speed,” but its benefits reach much further. Machine learning and LLMs becoming commonplace can improve customer experiences and augment the workload of finance professionals.

Some of the key benefits include:

• Improved risk management
• Regulatory compliance
• Better decision-making
• Enhanced customer experiences

The future of finance is all about real-time decisions. Because AI tools process large amounts of data efficiently, they can be used to identify financial risks to a company and detect fraud in real-time. 

When navigating complex regulatory situations, human error runs rampant. In these cases, introducing AI models simplifies the monitoring and reporting processes needed to maintain compliance.

By combing through non-traditional data sources, AI technologies enable more informed decision-making within the financial space, particularly in terms of financial inclusion. As a result, determinations of creditworthiness become less black and white.  

So many financial institutions are built upon customer interactions, which can be increasingly personalized thanks to AI. And conversational AI systems, like chatbots, can handle a high volume of customer interactions faster than solely human agents. 

"AI doesn’t introduce entirely new risks,” said Aaron Puckett in Orlando Business Journal. “It amplifies the ones already in place.” And it's true. 

While the financial sector has always had its share of risks, widespread use of AI and automation may intensify the problem. A working paper published by the Bank of International Settlements (BIS) highlighted the challenges—longstanding and newly emerging—that come with AI implementation, such as:

• Data privacy and security
• Algorithmic bias
• Economic disruption
• Legal, regulatory, and compliance issues

Any time sensitive data is handled—like financial information—there’s the potential for it to become compromised. However, AI greatly increases the scope of potential data breaches due to the sheer volume of information it takes in and retains.

Since AI systems are trained on historical data, they can perpetuate biases from that data and feed into systemic inequalities, which can negatively impact entire demographics. AI bias may affect credit scoring, risk assessment, and insurance pricing—and the opaque nature of its decision-making makes it hard to determine the AI model’s reasoning.

AI is known for reducing operational costs, but its effect on the economy as a whole may be more complex. Advancements in AI have the potential to reduce the size of the workforce (particularly if entry-level jobs are replaced by automation); when this occurs on a large scale, not only the economy but the financial sector as a whole are destabilized.

The regulatory gray area surrounding AI is primarily due to its newness. In the absence of standardized regulations on ethical AI use, organizations must balance their new capabilities with potential legal and compliance issues.

With AI reshaping the finance sector, the career landscape is growing alongside it. You can leverage AI in virtually any finance role to increase your productivity and bolster decision-making. These include:

• Director of Payments Engineering 

• Financial Data Analyst

• Financial Planning and Analysis Manager 

• Fintech Product Manager

• Quantitative Analyst 

Additionally, new career paths have emerged that specifically call for AI expertise, such as:

• AI Financial Data Scientist 

• AI Fraud Detection Specialist

• AI-Powered Finance Manager 

• AI Risk Management Specialist

• Internal IT Auditor (AI Risk Focus) 

The U.S. Bureau of Labor Statistics projects consistent growth for the finance industry, and AI is leading the charge. How can you break into the field?

1. Practical AI experience is invaluable. If you don’t yet work in finance, introduce AI tools into your workflow where you can. 

1. Build your technical skills and knowledge. Think programming, machine learning, and financial modeling.

1. Stay up to date with industry news. Keeping tabs on fintech summits and publications ensures you’ll stay on top of the AI finance landscape. 

But without a doubt, the best step you can take toward a career in AI finance is education.

The University of Cincinnati is on the cutting edge of AI. You can be, too.

Our online Graduate Certificate in Applied AI in Finance is exactly what you need to keep up with the future of finance. It’s ideal for students interested in leveraging AI to make data-driven financial decisions. The twelve-credit curriculum allows you to dive deep into: 

• Blockchain and cryptocurrencies (Bitcoin, Ethereum, stablecoin, NFTs)

• Cryptocurrency markets and portfolios 

• Algorithmic trading  

• Investment management and strategy

By building a solid foundation in financial analysis, you’ll master AI applications for today’s evolving financial landscape. Best of all, the certificate can be completed 100% online in under a year (two to three semesters).  

It’s time to forge your path in AI and finance. If you aren’t sure where to start, let UC help you.

Get in touch with your Enrollment Services Advisor for personalized guidance, application help, or just to get the ball rolling.