Artificial intelligence (AI) is changing the financial industry quickly. Banks, insurance companies, and asset managers are now handling large amounts of data and complexity. AI tools help them work faster, smarter, and with fewer mistakes. In 2023, financial services firms spent about $35 billion on AI, making it one of the largest investments in any industry. Analysts expect this spending to reach nearly $97 billion by 2027.
Many leading banks worldwide are dedicating 30–40% of their IT budgets to AI projects. Major lenders have also introduced AI systems, such as chatbots and language models, for use by most of their employees. In summary, AI has moved past the experimental stage in finance; it is now a key part of the industry, impacting everything from customer service to back-office tasks.
AI offers clear benefits by automating routine tasks, speeding up decision-making, and providing new insights. For instance, AI can analyse loan applications, detect fraud, summarise financial reports, and draft presentations. In banking, machine learning adjusts portfolios and generates trading signals. Insurers use AI to price policies and process claims. Finance teams use AI to forecast budgets and identify accounting errors. Overall, AI helps finance professionals work more efficiently, saving proactive teams 20–30% of their time on data tasks.
Rapid technology changes create challenges like data privacy, security, fairness, and oversight. Regulators worldwide, such as the EU and Singapore, are addressing these issues. Industry leaders emphasise the need for clear and ethical AI practices. The key message is that finance professionals adopting AI can boost productivity and improve services while balancing innovation with responsibility.
What Is AI in Finance Today?
AI in finance includes various technologies and applications. It uses traditional machine learning to detect fraud or predict credit risk, and it employs robotic process automation (RPA) bots to manage routine transactions. Some companies are also exploring advanced generative AI systems, such as OpenAI’s ChatGPT, which can understand and create text, code, or images. Additionally, some firms are looking into agentic AI, which can autonomously perform multi-step tasks, like completing the entire accounting close process with little human involvement.
These tools are used in all areas of finance. In banking, AI helps with customer service through chatbots, gives personalised advice, and runs fast trading algorithms. In insurance, AI improves underwriting and processing claims. In wealth management, robo-advisors use AI to create investment portfolios. Even in corporate finance, AI aids in forecasting, budget planning, and audits. A survey found that 54% of financial companies had started using AI by early 2025, which is much higher than in other sectors.
Additionally, about one-third of financial firms reported using generative AI tools by 2025, up from 21% the previous year. In summary, AI is widely used in finance, though often still in early stages, and its adoption is increasing rapidly.
Key Applications of AI in Finance
AI touches virtually every part of the finance value chain. Some of the most important applications include:
Customer Service and Virtual Assistants:
Banks and insurance companies use AI chatbots and virtual assistants to enhance customer service. These systems can understand everyday language and answer questions, help with transactions, and offer personalised advice 24/7. For example, Bank of America’s assistant, Erica, and BBVA’s assistant, Blue, let customers check their balances, make payments, and receive tailored advice through voice or chat. BBVA recently announced it will introduce ChatGPT Enterprise to 120,000 employees, showing how even banking staff will use generative AI to assist clients. An industry survey found that 41% of banks are already using AI chatbots to improve customer service.
Lending and Credit Underwriting:
Loan approvals are now faster thanks to AI. Machine learning models evaluate credit risk by analysing large amounts of data, including non-traditional sources. This allows for quicker and more accurate lending decisions for small businesses. For example, WeBank, an online lender in China, offers real-time, personalised loan terms to SMEs, speeding up approval times.
Insurers also use AI to quickly assess applications by analysing information and comparing it to existing data. This helps identify high-risk cases and customise premiums. Insurers using AI report significant improvements, with a McKinsey study showing 10–15% higher premium growth and 20–40% lower onboarding costs.
Fraud Detection and Risk Management:
Detecting financial crime is a common use of AI. Modern AI systems examine millions of transactions in real time to find unusual activities or patterns. For example, banks now use deep learning to monitor payments and account activities, allowing them to identify fraud more quickly than older rule-based systems. JPMorgan Chase uses AI for anti-money laundering (AML) and “know your customer” (KYC) checks. This technology automatically reviews transaction flows and customer documents to uncover hidden risks. Cybersecurity teams also use AI to spot hacking attempts and unusual network behaviour.
On the risk side, AI helps predict credit losses and market shocks more accurately. By automating these time-consuming tasks, institutions can let human experts focus on the most complicated cases.
Trading and Investment Management:
Artificial intelligence (AI) is a key player on Wall Street, with quantitative trading firms and hedge funds using advanced algorithms and machine learning to analyse market data and execute trades in fractions of a second. These AI models identify correlations across markets and assess news sentiment to inform trading strategies.
In retail investment, “robo-advisors” utilise AI to build and adjust portfolios based on customer goals and risk profiles. Wealth-management teams also employ AI tools like JPMorgan’s “Connect Coach AI,” which aggregates research and generates talking points for client meetings. Overall, AI enhances data-driven investing, allowing for automatic portfolio optimisation and improved market trend forecasting.
Process Automation and Corporate Finance:
Many back-office finance tasks are automated by AI, assisting with invoice processing, reconciliations, expense management, and report generation. A McKinsey study found that finance teams using AI can reduce data gathering and routine analysis time by 20–30%, allowing analysts to focus on strategy. AI-driven planning tools enable CFOs to run financial forecasts or what-if scenarios quickly. Additionally, agentic AI is being piloted to automate workflows, checking compliance in contracts and invoices automatically. These advances enhance accuracy, speed up monthly closings, and improve internal controls.
Insurance Claims and Underwriting:
Insurance companies use AI to improve their services beyond traditional banking. AI helps them process applications by looking at data like photos, medical records, and satellite images to better assess risk. In managing claims, AI tools can prioritise cases, spot possible fraud, and estimate damages. For instance, some auto insurers use image recognition to assess damage to vehicles from photos. According to McKinsey, insurers that have updated their underwriting and claims processes with AI have achieved 5% more accurate claims and gained significant efficiency.
AI is also expanding into areas like regulatory compliance (“RegTech”), where it automates report filing and monitors changing rules; and emerging finance fields like cryptocurrency trading and DeFi.
Benefits of AI in Finance
The wide-ranging use cases above translate into clear advantages:
Efficiency and Cost Savings:
AI takes over repetitive tasks to make work faster and reduce mistakes. Tasks like routine credit checks, document reviews, and compliance scans that used to take days can now be done in minutes. Experts believe that banks using AI could improve their efficiency by 10–25%. Companies also save money on labour costs and can use their staff for more important work.
Better Risk and Fraud Control:
With AI’s ability to recognise patterns, financial institutions can identify problems more quickly. This leads to better fraud detection, with fewer false alarms, more accurate credit-risk assessments, and fewer compliance issues. One report states that advanced AI can find unusual activities with unmatched speed and accuracy.
Enhanced Customer Experience:
AI is essential for delivering personalised customer service across various industries. Chatbots and virtual advisors use sophisticated algorithms to provide immediate, tailored support, significantly enhancing customer satisfaction and fostering stronger brand connections.
Additionally, machine learning analyses customer data to suggest products or investments that align with individual preferences and financial goals. Research shows that personalised offers and support lead to increased customer loyalty, with over 70% of consumers more likely to stay with brands that respond to their unique needs. By leveraging AI, companies can build lasting relationships and drive long-term success.
Revenue Growth and Innovation:
Institutions are shifting from cutting costs to focusing on growth through AI. Finance leaders now view AI as a way to boost revenue. They are using it to create new products, like tailored investment strategies, improve marketing, and offer 24/7 trading services. Recent studies show that marketing, sales, and corporate finance departments see significant revenue increases from AI, which also enhances cross-selling and investment results. In short, AI helps finance companies innovate quickly and find new opportunities.
Data-Driven Decisions:
AI uses large amounts of data in finance to help teams make better decisions. It quickly analyses vast amounts of market data, customer transactions, and economic indicators. This analysis gives insights that are hard for people to find on their own. Finance teams can run complex “what-if” scenarios, stress tests, and root-cause analyses with just one click. This ability to analyse data quickly leads to better and faster decision-making, which is essential in a fast-paced market.
Challenges and Responsible Use
Despite the promise, AI in finance comes with important caveats. The technology is powerful but not infallible:
- Data and Bias: AI models depend on the quality of the data they use for training. In finance, this data can be messy, incomplete, or biased based on past discrimination, like unfair lending practices. If we don’t check carefully, AI can unintentionally continue these biases in decisions about credit or hiring. Organisations need to use diverse and high-quality data and keep an eye on their models to ensure fairness.
- Opacity and Explainability: Many AI systems, like deep learning models and large language models, work in ways that are hard to understand. This lack of clarity worries regulators, especially in important areas like loan approvals. As a result, banks and insurers are putting money into tools and policies to check AI decisions and ensure that humans review important choices.
- Security and Fraud: Numerous AI systems, such as deep learning models and large language models, often operate in complex ways that can be difficult to interpret. This inherent opacity raises concerns among regulators, particularly in critical domains like loan approvals. Consequently, financial institutions, including banks and insurance companies, are investing in tools and implementing policies designed to audit AI-driven decisions. This ensures that significant choices are subject to human review, promoting greater transparency and accountability in the decision-making process.
- Regulation and Ethics: Governments are creating rules for using AI in finance. For instance, the US Financial Stability Oversight Council has called AI a “potential vulnerability” in the financial system and is pushing for better oversight. The EU’s AI Act, which is awaiting enforcement, will set strict rules for managing risks and ensuring AI systems can be explained. Singapore’s Monetary Authority and other regulators have shared guidelines on the risks of AI models. As a result, most financial institutions (84% and growing) are now establishing formal AI governance frameworks, conducting ethics reviews, and training staff to meet these new standards.
- Implementation Risk: Not every AI pilot works well. The financial industry sees many AI projects fail, often because of poor integration or unrealistic expectations. Banks carefully test their models and check them with humans side by side. They begin with lower-risk tasks, like automating back-office work, before using AI for customer-facing services. This cautious approach helps prevent expensive mistakes.
Finance firms need to use AI in a responsible way. They want to move quickly, as this year’s Davos report says, AI is changing everything from customer experiences to product development and risk management. However, they also need to have strong controls in place. The best results come from AI projects that have clear goals, such as faster processing, better insights, or new revenue, and that are closely monitored with ethical design, audits, and human involvement.
Global Trends and Examples
AI adoption in finance varies by region, but the overall trend is global and accelerating.
North America:
US banks and fintech companies are at the forefront of artificial intelligence innovation, significantly shaping the financial landscape. For instance, JPMorgan Chase has integrated its proprietary AI platform into various operations, with tens of thousands of employees utilising it daily for tasks ranging from customer service to data analysis. Bank of America has developed Erica, a highly sophisticated AI-powered virtual assistant that interacts with millions of customers to provide personalised financial advice, manage accounts, and even help with transactions.
Furthermore, prominent wealth management and capital markets firms like Goldman Sachs are leveraging AI technologies for advanced trading strategies and market analysis, enhancing their decision-making processes and operational efficiencies. On the regulatory front, institutions such as the Securities and Exchange Commission and the Federal Reserve are actively engaging in initiatives aimed at exploring the applications and potential risks associated with AI in finance, considering its impact on market stability and consumer protection.
A recent survey revealed that nearly 50% of finance firms across the US and Canada have implemented active AI deployments, with larger banks particularly leading the charge. These developments underscore the growing importance of AI in delivering enhanced financial services, improving customer experiences, and driving competitive advantages in the fast-evolving financial sector.
Europe:
European banks are investing heavily in artificial intelligence (AI) to improve their operations and customer service. For example, BBVA in Spain has teamed up with OpenAI to use ChatGPT technology for its 120,000 employees. This project aims to create a smarter, more personal banking experience for customers.
In the UK, banks like Barclays and HSBC have set up AI labs to boost trading strategies and make operations faster. These labs use machine learning to analyse market trends and help make better decisions.
Banks in Continental Europe are prioritising data privacy as they adopt AI solutions. Many are working on privacy-enhancing technologies to protect customer data while still using advanced analytics. This focus on secure AI is supported by the upcoming EU AI Act, which will require financial AI systems to follow strict rules for consumer protection.
Cities like London and Zurich, known as financial centres, are also home to fintech startups that use AI technology. These startups are developing automated lending services and advanced payment systems, further shaping the European financial landscape.
Asia-Pacific:
Many banks in Asia are leading in digital innovation by quickly using advanced technologies like artificial intelligence (AI). In China, banks such as Ant Group, Ping An, and WeBank have built systems that are “AI native.” They use AI to improve credit scoring, offer personalised financial management tools, and enhance customer service with chatbots and virtual assistants.
In Southeast Asia, fintech super-apps like Grab and GoJek have successfully added AI-powered financial services to their social platforms. This allows users to access various services from ride-hailing to food delivery and financial transactions while AI personalises their experiences and makes operations smoother.
In Japan and South Korea, major banks are testing AI technologies. They use AI assistants to better interact with customers and predictive models to improve risk assessment and decision-making.
Singapore’s Monetary Authority of Singapore (MAS) has taken steps to guide the responsible use of AI by publishing guidelines on AI model risk management in 2024. This shows Singapore’s commitment to using AI ethically in its financial sector, promoting responsible and transparent technology use.
Other Regions:
The Middle East and Africa are advancing in the use of artificial intelligence (AI) in banking. In the United Arab Emirates (UAE) and Saudi Arabia, banks are testing AI solutions to improve customer service and detect fraud. They partner with technology companies to integrate AI tools into their systems. For instance, UAE banks use chatbots to answer customer questions, while Saudi banks use AI to spot fraudulent transactions.
In Africa, mobile-focused banks are using AI for credit scoring. They analyse data from mobile phone use and transaction history to assess creditworthiness. This approach helps banks provide services to people without traditional accounts, promoting financial inclusion and access to credit.
Many banks and fintech companies work together. Traditional banks often team up with AI startups to create new services, such as robo-advisors or AI solutions for back offices. Large tech companies also help by providing cloud-based AI platforms, and some, like Amazon and Google, now offer tools specifically for banking.
Overall, AI is now common in global finance. According to S&P Global Ratings, by early 2025, more than half of financial firms will have active AI projects, which is higher than the average for other industries. Right now, most banks use AI internally, so not many have fully deployed it across all customer services. However, this is changing quickly. Leading banks aim to fully integrate AI strategies by 2025.
Future Outlook
AI will play a bigger role in finance in the future. Financial leaders expect to increase their budgets for AI in 2026 and beyond, with about two-thirds planning to invest more next year. In banking, new technologies will expand AI’s influence. Important trends include agentic AI, which provides autonomous assistance for tasks like reconciliations, multimodal models that can handle text, speech, and images together, and federated learning, which allows collaboration on AI without sharing raw data. These developments could make AI tools smarter and more integrated.
Industry analysts believe the finance sector will lead the growth of AI spending worldwide. One forecast predicts that by 2028, the banking industry alone will make up about 20% of all global AI spending. This suggests that banks have large budgets, with estimates showing that leading banks might spend several percent of their non-interest expenses on AI. Firms that look at different spending scenarios find that investing more in AI leads to greater efficiency. For example, banks that invest heavily in AI can see cost improvements of 15–25%.
The industry expects that competition will increase. S&P Ratings warns that banks that effectively use AI will earn more revenue and reduce costs faster than others. In other words, successfully adopting AI could help banks gain a larger market share and improve profitability.
AI is changing the finance industry in big ways. For finance professionals, this means constant innovation with new tools for analysing data and engaging with customers, along with new oversight responsibilities. As one banking report noted, banks that understand and embrace the potential of AI will lead the future of finance. In the near future, these professionals will need to balance the efficiency gains from AI with careful risk management. Those who learn to use AI tools, update their workflows, and follow best practices for governance will find finance more powerful and exciting than ever, while still focusing on trust and sound decision-making.
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