Artificial intelligence (AI) is reshaping the financial services industry, and has been for a while now. It already has many real-world applications and benefits. So, it’s no surprise that it’s caused a foundational shift in how finance teams operate—or that more companies are adopting it.
AI’s ability to innovate and streamline operations means greater efficiency and accuracy. It can also improve overall business performance and support faster, more informed decision-making.
But AI does come with real challenges. Find out how AI is actually used in finance, what the future may hold, and how organizations can apply it responsibly and strategically.
Key Points
- AI in finance refers to a range of technologies that drive innovation and solve real-world problems.
- Advanced AI changes the way businesses make financial decisions and manage their day-to-day operations. It’s redefining the finance services industry.
- Responsible AI use can support finance professionals through automation, efficiency, accuracy, and—of course—innovation.
- Building AI-related skills can benefit those seeking a competitive edge in financial services.
What Is Artificial Intelligence in Finance?
AI in finance is the use of certain technologies—like machine learning (ML), natural language processing (NLP), and predictive analytics—to support decision-making. These systems can:
- Analyze large volumes of data for key, actionable insights
- Make real-time calculations and measure performance
- Predict outcomes and support forecasting
- Streamline daily operations and repetitive tasks
- Improve how businesses understand and connect with customers
AI adoption has grown rapidly across the finance industry. According to a KPMG report, 88% of companies use AI in finance functions, with 62% applying it to a moderate or large degree. Many companies already rely on AI for financial reporting, accounting, taxes, and risk management, with adoption expected to keep expanding.
AI systems continuously learn from data to support financial decision-making. They do this by analyzing patterns across multiple data sources, identifying the factors that influence performance. They then turn that data into valuable insights that decision-makers can act upon.
Used responsibly, AI can improve overall performance and support innovation. This can help organizations remain competitive as financial complexity increases.
How AI is Used in the Finance Industry
The application of AI in finance is multifold. For many companies, it’s become a core part of their operations.
Some of the most prominent use cases for AI include financial planning and forecasting, data analysis, risk management and fraud detection, regulatory compliance, systems automation, and customer support.
Financial Planning, Forecasting, and Analysis
AI relies on predictive modeling to recognize patterns and trends in historical data. It then uses those insights to model possible best- and worst-case scenarios. This helps business leaders identify new opportunities—like acquisitions or investments—while also flagging potential risks earlier in the planning process.
AI also provides real-time performance insights into financial indicators like cash flow, market trends, resource allocation, and liquidity needs. Finance pros can use these insights to make data-driven decisions to support operations and achieve goals.
AI can help in other ways, too. For example, it can automate workflows, freeing up human talent for other tasks. It can also identify inefficiencies and support cost-saving initiatives across the finance function.
Risk Management, Fraud Detection, and Compliance
AI combines predictive analytics, real-time insights, and deep learning techniques to identify and manage risk. This includes things like market volatility or insurance underwriting risks.
Models trained on specific datasets can support more accurate risk scoring, like in credit-related systems. They also act as an early warning system against vulnerabilities or possible security threats.
Some systems use pattern recognition to identify anomalies that might point to fraud. For example, machine learning models are constantly learning and adapting. They can detect fraud quickly and accurately, which means fewer errors and quicker investigations (and resolution times).
AI also supports compliance efforts. For example, agentic AI systems can analyze invoices or other financial documents for accuracy and completeness. This helps teams identify issues earlier and reduce manual review work.
Customer and Client Experience
AI doesn’t just regurgitate existing information. Advanced systems can generate new outputs, too, such as personalized recommendations and contextual responses based on customer data and behavioral patterns.
Take AI-powered virtual assistants and chatbots, like Intuit Assist, for example. These tools can provide customers with personalized financial insights and 24/7 support. Designed to respond in natural language, they help improve responsiveness without increasing support overhead.
Generative AI (GenAI), in particular, has proven transformative in finance. Businesses use it to better understand customer trends and behaviors at scale. From there, they can refine products or services. All of this can lead to higher customer satisfaction ratings and brand loyalty.
How AI Changes Financial Decision-Making
Integrating AI in finance goes beyond supporting data-driven decisions. It changes how those decisions are made by expanding the speed and scale of financial analysis.
Unlike traditional, rules-based finance automation, AI-driven systems are designed to mimic human reasoning. They’re highly adaptable and continuously learn as they process new information. The more they take in, the more advanced their predictive insights can be.
As they evolve, AI models will grow even more dynamic. Today, they can:
- Automate workflows and streamline financial operations
- Anticipate customer or business needs before they come up
- Identify opportunities (and risks) teams can use to make informed decisions faster
- Collect and analyze system-wide data to deliver real-time insights
- Unlock value that might have been missed
- Flag anomalies or potential fraud and provide possible solutions
AI can improve team efficiency and help limit certain forms of human bias in decision-making. However, this requires properly training AI models on diverse datasets to ensure the output remains objective. It can also solve real challenges with greater responsiveness and speed. These are must-haves in the competitive finance services industry.
How AI Impacts Finance Jobs and Skills
There’s no doubt that AI has had a major impact on the finance industry. But just because more companies are using it doesn’t mean humans are becoming irrelevant.
Although AI is eliminating certain types of jobs, it’s also expected to create about 170 million new jobs between now and 2030. Popular AI-related fields include machine learning, data science, AI engineering, and software engineering.
Within finance teams, AI can streamline and automate key processes that once required significant manual effort. Some AI agents can even manage entire workflows without human interaction. Sometimes, the results are faster and more accurate than what humans can accomplish alone.
But human oversight remains crucial when adopting AI into any company. After all, AI’s predictive technology is still prone to bias in training data. Many AI models also need real people to train them.
Ultimately, AI’s purpose is to augment roles and shift responsibilities. That way, finance pros can focus on other, more complex matters in the workplace.
For those looking to stay competitive as AI adoption grows, building relevant capabilities matters. Developing the right AI skills can help you adapt as roles evolve.
Data Privacy, Security, and Responsible AI in Finance
As impactful as AI has been (and will continue to be) in finance services, it’s still a developing technology. As such, it comes with challenges that require active management.
One concern is algorithmic bias. This is when an AI model outputs unfair or discriminatory responses. It’s especially common in areas like credit scoring, particularly when models are trained on datasets that reflect existing inequities.
AI systems also often rely on highly sensitive data, like financial or medical records. While access to this data enables more accurate insights, it also increases exposure to cybersecurity threats and data breaches. These risks raise important questions about regulatory compliance and overall security.
Responsible AI usage is critical in addressing these concerns. Any company that uses AI should do so with the proper governance and oversight. Otherwise, there’s the risk of poor transparency and other ethical concerns.
Intuit’s AI-driven platform was designed on established AI principles that prioritize responsible and transparent use. Our principles are (and always have been) rooted in customer and stakeholder trust.
Learn more about our AI research and how we create systems that enhance human talent, prioritize fairness, and maximize privacy and security.
How AI Will Shape the Future of Finance
AI is already contributing to significant growth in the finance industry. This is especially apparent with data-driven digital brands. Approximately 92% of companies that use AI in finance say they’ve either met or exceeded return-on-investment (ROI) expectations.
AI is redefining how teams and organizations run and compete. As the role of AI in finance evolves, there’s a clear shift toward intelligence-driven finance operations. Businesses looking to stand apart from the competition can use it to innovate and achieve long-term initiatives.
Even so, long-term success will depend on balance. Industry leaders must still find ways to leverage human talent if they want to thrive. At the same time, they’ll need to build internal systems that emphasize accountability and responsibility.
Intuit Intelligence Powers Smarter Financial Decisions
Artificial intelligence is transforming finance from reactive to more intelligent. In practice, AI generally takes three forms: reactive, generative, and agentic.
The most basic version is reactive AI. It functions by delivering a predictable output based on the information it’s been given. As long as the input remains the same, the responses are, too.
Generative AI is designed to create original content (like text or audio) based on user prompts. It relies heavily on machine learning and other technologies to mimic the way humans learn and make decisions.
Agentic AI works autonomously to make decisions. It’s highly adaptable and can pursue goals without much human oversight. It also relies on technologies like machine learning and natural language processing.
Organizations can apply these approaches in different ways. For businesses looking to grow and compete in an AI-driven economy, Intuit Intelligence offers a system-level approach to embedding AI across financial workflows.
Ours is a system-level approach to embedding AI across financial workflows. The platform delivers smarter insights, enables faster decisions, and offers scalable solutions—all in one place.
Learn more about our partnership with ChatGPT and how it helps power confident financial outcomes.
FAQs
Will AI replace human financial professionals?
AI is designed to support finance pros, not replace them. It can handle repetitive, rules-based tasks that don’t require human judgment, allowing professionals to focus on higher-value work such as analysis, strategy, and decision-making.
What regulations govern the use of AI in finance today?
There are some federal regulations in place regarding AI and finance in the US, particularly in areas such as lending and consumer protection. However, comprehensive governance frameworks for everyday AI use are still developing, requiring organizations to stay informed and adapt as standards and policies evolve.
Is financial data safe when AI models are involved?
AI systems often rely on sensitive financial data, which makes data protection a critical concern. While many organizations apply strong security controls and compliance practices, regulations and transparency requirements continue to evolve. As a result, managing data privacy and security remains an ongoing responsibility when using AI in finance.
How can financial professionals keep up with the rapid pace of change due to AI?
Keep learning and adapting. Learn AI skills to boost your finance or tech career. Machine learning, programming languages, data science, software engineering, and AI product management are all good places to start.