Financial technology, or fintech, refers to the use of digital tools, data and automation to transform and speed up operations within banks and the finance industry. It also includes the software and apps that consumers use to access financial services, including tools that help create budgets, track spending, buy and sell stocks, apply for mortgages. Fintech innovations are helping banks keep pace with the rate of digital transformation within the financial industry while artificial intelligence is helping expedite fintech automation.

Banks and financial institutions have been automating and digitizing processes gradually since the late 20th century. From the first ATM in 1967 to digital deposits and apps such as Venmo and Zelle in the 2000s, technology has dramatically changed the way people transact financially. It transformed how they transfer money, buy insurance, get loans and make investments.

Fintech has expanded access to banking products and services, and it has streamlined many mundane business processes. Existing fintech is delivered in the form of software that uses a combination of application programming interfaces (APIs), mobile applications and web-based services. These components enable banks to share sensitive customer data securely while offering customers a seamless and engaging user experience.

In the fintech industry, many startup fintech companies focus on software development, and then they collaborate with large banks, investment firms and payment companies in the financial sector.

As the financial sector became more digital, the amount of data produced by transactions and other services also grew. AI can help streamline financial processes and enhance business partnerships by surfacing and presenting relevant information. It can help calculate risk, forecast future conditions and optimize financial analyses, planning and organization.

There are several top-line categories that fintech offerings fall into: digital banks and wallets, digital payments, personal finance, investing and lending. As AI becomes more commonplace in finance, AI-powered apps and machine learning algorithms make it easier to analyze datasets, automate tasks and improve data-driven decision-making.

AI-enhanced fintech can be useful for all kinds of users that interact with the financial organizations in some way. These users include everyday customers, developers, industry analysts, strategists and risk managers for financial organizations such retail banks, commercial banks, investment banks, trading platforms, e-commerce platforms and businesses with a digital presence.

There are a few different ways that AI systems might be integrated with fintech software. Here are some example use cases for AI in fintech:

• Credit risk assessment and management
• Fraud detection
• Virtual assistants
• AI-based personal finance tools and services
• Algorithmic trading and portfolio management

Banking can come with certain risks. Credit risk is one of them. In the past, financial organizations came up with credit risk modeling to predict how likely customers were to repay loans.

Risk management is one area where AI can make a substantial contribution. By analyzing vast amounts of data, AI algorithms can identify patterns and trends that might indicate potential risks. For instance, AI can help identify customers who are more likely to default on loans, which can enable financial institutions to make more informed decisions and mitigate risks more effectively.

AI algorithms can replace traditional statistical models for credit score calculation. It can quickly analyze income, transactions, credit history, work experience and factor in real-time changes and the most up-to-date information from online activities to make assessments of creditworthiness more accurate. Using AI technologies can reduce the time and effort required to prepare and summarize reports. It can streamline the credit approval process.

Another risk banks often face is fraud. AI models and deep learning are great tools for identifying patterns and finding anomalies. They can be trained to spot fraudulent activities by analyzing transactions in near real-time and monitoring behavior patterns and spending habits from users.

For example, AI can help detect credit card fraud by identifying unusual spending patterns or transactions that occur outside of the customer's typical behavior.

AI can also account for multiple variables such as purchase frequencies, number of transactions, geographic locations of users and the amount spent on a given purchase.

In addition to detecting fraud in customer accounts, financial institutions can also implement AI-powered solutions¹ in their cybersecurity framework to quickly detect cyberthreats and vulnerabilities in the network.

AI-powered assistants can use natural language processing (NLP) and natural language understanding to interact with customers through a chatbot interface. They can use conversational AI, user account information and information related to how to handle the bank’s tech infrastructure to tailor a more personalized support approach. These customer support chatbots can respond to common queries and requests 24x7 through natural conversation.

They can also guide customers through new features and services and offer personalized recommendations for products and services that would be helpful to the customer’s business or financial situation. AI-driven interactions require less human intervention compared to conventional chatbots without NLP abilities. These AI applications can lead to more customer satisfaction, and in turn, increased earnings² for companies.

On the enterprise side, these AI-powered chatbots can also help banks improve their operational efficiency. AI provides process automation for tedious clerical tasks such as data entry, invoicing, payment processing and sorting and analyzing financial data³. It can assist with customer research and underwriting loans and investments, and verify submitted documents. It can also analyze data on customer interactions and the performance of existing fintech solutions to provide customer insights and suggestions for revenue optimization, expense management, cost-saving and risk management.

For consumers, AI-powered personal financial tools and services have the potential to further enhance customer experience. By using AI to analyze spending habits, investment preferences and interaction patterns, financial institutions can tailor their offerings to meet individual needs.

AI applications can also act as a robo-advisor to help consumers make smarter budgets based on their needs, maintain their financial records, track their personal spending, bills, assets and liabilities, and suggest saving strategies.

AI can provide valuable insights and forecast changes in market trends, exchange rates or investments. AI applications⁴ use data analytics that account for news, the current state of financial markets, sentiments across social media, economic indicators and historic financial data. They can assist in automated trading and portfolio management by offering risk-versus-return calculations and financial advice.

These technologies can be customized to individual risk profiles based on past investment decisions and financial goals to suggest actionable insights or inform investment strategies. For example, HSBC is using AI to boost its predictive analytics to identify potential high-growth stocks.

The future of AI in fintech holds immense potential for transforming the financial services industry. AI can have greater impact in various aspects of fintech, including risk management, fraud detection, customer service and personalized financial advice.

As AI agents and AI assistants improve, they'll offer more powerful ways for fintech companies to integrate them into their business models, stay competitive, work at market speed and provide better services to their customers.

Integrating AI in the fintech sector might lead to cost saving⁵ by decreasing operational costs spent on customer service, fraud prevention, clerical tasks and more. It can also improve customer experience by performing in-depth analysis on their individual data points to arrive at solutions or suggestions. AI-powered financial advisors are also more accessible and more affordable for consumers to access compared to human advisors.

AI might also reduce the rate of human error⁶ and bias in interpreting data, which can enhance financial strategies. However, to accomplish this, AI models must have good data governance and transparency so human managers can see how the AI worked through the problem to arrive at a certain decision or solution. The adaptability of AI means that it can be used to bolster a wide range of fintech tools.

The financial sector is highly regulated.⁷ That means that any innovations in the fintech market need to adhere to regulatory compliance with current federal policies. In most cases, regulatory frameworks are not yet in place⁸ due to the speed of technological change.

In general, algorithmic bias,⁹ data privacy and data protection continue to be a concern. And because most financial organizations might not have the appropriate tech infrastructure or finance professions with tech expertise, there is a reliance on third-party IT infrastructure and data. This third-party involvement can expose institutions to financial, legal and security risks.

According to a 2024 report¹ from the US Department of the Treasury, “Generative AI models are still developing, currently very costly to implement and very difficult to validate for high-assurance applications.” As a result, most of the financial firms they researched for their report have opted for enterprise solutions rather than a generative AI provider that allows public access or use a public application programming interface (API).