How AI is Used in Finance, With Examples

Artificial intelligence (AI) is being used in finance to analyze patterns from large data sets, solve complex problems, and supplement decision-making processes. It can help streamline processes, improve customer service, and tighten data protection and security systems.

In this guide, we’ll explore the role of AI in finance and how it’s used today. You’ll learn how AI benefits financial institutions, the challenges AI brings, and examples of AI used in finance.

Importance of AI in the financial services industry

In 2019, research firm Autonomous Next estimated that AI could save the banking sector $447 billion by the end of 2023. And by 2020, approximately 70% of all financial service firms were leveraging machine learning to predict cash flow events, fine-tune credit scores, and detect fraud.

With the success of large language models (LLMs) like ChatGPT being released in late 2022, the opportunities in finance have only increased. The financial sector is seeing improved work flows and cost savings in a number of areas, including:

• Automation and efficiency. One of the most significant advantages of AI for financial tasks is automation. Financial professionals have to analyze a lot of data to make critical decisions. Professionals can now feed their data into an AI-based system and focus on more complex tasks.
• Enhanced decision-making. AI tools can process vast amounts of data quickly and uncover patterns and trends that might be hard for humans to discern. This information provides insights to help finance professionals make better data-driven decisions.
• Risk management and fraud detection. Risk has always been a concern in finance. Today’s criminals have access to the same generative AI tools as the finance sector, and are applying them in novel ways. Businesses have to be able to spot abnormalities in enormous amounts of data. The patterns AI picks up can help financial companies detect fraud more quickly and efficiently.
• Better customer experiences. Advances in generative AI and natural language processing (NLP) allow AI chatbots to offer personalized experiences to financial customers. They can provide 24/7 customer service, offer recommendations based on customer data, and handle routine transactions efficiently.
• Improved forecasting and predictive analytics. AI tools can analyze data to make predictions. For example, AI can find patterns in historical financial data and use those patterns to forecast future trends in the financial world—including stock prices, interest rates, and business income. Predictive models can help analysts determine what’s likely to happen and make more strategic decisions.
• Reduced costs. Financial services require human input at many points. The problem is that manual input from people leads to mistakes—something AI is less likely to do. Fewer mistakes and more automation mean fewer costs due to human errors.

Challenges of financial AI

AI tools offer a lot of value for the finance sector, but it doesn’t come without challenges. If businesses use AI incorrectly, they may make mistakes that cause them to get poor-quality information and make bad decisions.

But understanding those challenges gives companies a chance to anticipate and mitigate the problems that may arise.

• Data quality. AI models rely heavily on the quality of the data they’re trained on. Garbage in, garbage out (GIGO) is a phrase often used to emphasize that AI and machine learning models can’t give high-quality output if their input is of poor quality. Incomplete, outdated, or inaccurate data can lead to flawed results.
• Bias. AI learns from historical data—much of which is from the decisions and content generated by people. AI models can unintentionally perpetuate or even amplify existing biases in the data they’re trained on. This could lead to discriminatory outcomes in areas like lending or insurance if not carefully managed.
• Regulatory issues. Many AI models, called black bloxes, are trained with limited human interaction and are so complex that they can’t be fully explained—which is an issue in an industry that has to explain its decisions to regulators.  Financial institutions must ensure their AI systems comply with all relevant regulations, including those related to data privacy, security, transparency, and fairness.
• Data security. As AI systems often process large amounts of sensitive data, ensuring data security is paramount. Financial institutions must have strong safeguards in place to protect against data breaches and misuse of data.
• Project failure. Implementing AI solutions can be complex and challenging, with a significant risk of project failure if not properly managed. Financial institutions should start with a clear understanding of what AI can and can’t do, a well-defined use case, and a realistic implementation plan.

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How is AI being used in finance?

The use of AI in financial technology (fintech) presents both advantages and challenges that call for thoughtful navigation. In this section, we delve into the specific ways this technology is currently being harnessed within the realm of finance.

Real-time data analysis

One of the primary uses of AI in finance is real-time data analytics. With the quantity of data moving through financial systems, businesses may find it challenging to keep track of everything. A well-trained AI that’s designed to look at patterns in big data can handle the job and notify humans about behavior they should know about.

This ability has several uses. Individuals can lean on AI services to learn more about markets and make more informed decisions, and businesses can use AI to manage credit decisions and investment analysis to calculate risks more efficiently.

Real-time analysis is also useful for people and companies that trade. AI can monitor large volumes of trading activity at once and use algorithmic trading to make decisions—whether in the traditional stock market or the crypto market, where prices can fluctuate rapidly.

However, while AI can support decision-making, human oversight is necessary to ensure that decisions are sound, ethical, and in line with the individual’s or institution’s goals and risk tolerance.

Customer service

AI chatbots have transformed customer service by providing round-the-clock assistance with limited human intervention. These bots are equipped to handle a range of queries, including account inquiries, transaction details, and general financial information.

One of the key advantages of AI chatbots is their availability. They can provide 24/7 service, which can greatly increase customer satisfaction by reducing wait times. Moreover, they can handle multiple questions simultaneously, increasing efficiency.

AI chatbots also have access to user-specific data (with necessary permissions and privacy safeguards), allowing them to provide personalized advice and support. They can answer specific account-related queries, provide personalized financial advice, and guide users through various financial processes.

And while AI chatbots can handle many tasks independently, they are often set up to refer more complex or sensitive issues to a human representative. This ensures that customers receive the appropriate level of support for their needs.

Fraud detection

The data analytics capabilities of AI make it a good candidate to help with fraud detection. AI trained on the right data, and with the appropriate machine learning algorithms, can analyze vast quantities of financial transactions and identify patterns of normal behavior. They can learn from historical transaction data, which aids in establishing a baseline of “normal” activity for each customer.

Once a model of normal behavior is established, the AI system can monitor ongoing transactions in real time and identify any activities that deviate significantly from this norm, flagging them as potential fraud. This could include unusually large transactions, a rapid succession of transactions, transactions from a new location, and more.

AI systems can then notify human analysts or appropriate parties about suspicious activity, enabling faster response times and potentially preventing fraudulent transactions from being completed.

However, while AI significantly enhances fraud detection capabilities, it’s not infallible. False positives (legitimate transactions flagged as fraudulent) can occur, and new, unrecognized forms of fraudulent behavior might not be immediately identified. So, human oversight and continuous model training and adjustment are critical for fraud detection.

Anti-money laundering (AML) and regulatory compliance

Many traditional AML processes are manual and can be tedious, time-consuming, and prone to error. AI can automate many of these tasks, improving accuracy and efficiency. For instance, AI can be used in Know Your Customer (KYC) processes to quickly verify customer identities and conduct background checks.

These tasks are possible because AI can screen documents, analyze large databases, and monitor suspicious activity reports—tasks companies need to perform at scale to comply with regulatory obligations. If anything looks suspicious, that activity gets flagged for human review and verification.

AI can also help by automating compliance checks and maintaining up-to-date records of all transactions and activities. This can significantly reduce the risk of regulatory breaches and penalties.

Credit scoring and underwriting

AI helps financing companies understand the people who are applying for loans and credit cards to determine the credit risk they pose. AI can analyze datasets from several sources—from bank records to credit history—to help determine how likely it is that an individual will repay a loan.

These tools allow underwriters and other loan professionals to accurately and quickly vet potential customers. These insights can help lenders make smarter decisions more quickly—getting borrowers the money they need and automating much of the underwriting process.

Forecasting and budgeting

AI can sift through large volumes of past performance data, including market trends, economic indicators, and company-specific factors, and detect patterns that might be difficult or time-consuming for humans to spot. These patterns can then be used to make predictions about future trends or outcomes.

By providing more accurate and detailed forecasts, AI can better inform budgeting decisions. Businesses can use these forecasts to plan their budgets more accurately, allocate resources more efficiently, and mitigate potential financial risks.

And as new data becomes available, AI models can quickly incorporate this data to refine their forecasts, helping financial institutions stay ahead of changes and make timely adjustments to their strategies.

Personalized product, service, and investment suggestions

Personalized recommendations and services are important to shoppers today—with 62% of business leaders finding that personalization leads to increased customer retention. AI makes personalization easier by analyzing customer purchases, browsing behavior, and other factors.

Financial institutions can use this data to help determine the financial products and services that people will likely buy. AI can recommend budgeting software, investment options, and advice tailored to someone’s unique situation.

Social media analysis

Social media is a treasure trove of information for financial institutions. AI provides a simple way to take the information gathered from social websites and correlate it with trends and consumer sentiment.

One of the key uses of AI for financial companies is sentiment analysis. Businesses can gather user feedback on social media platforms and ask AI to classify the sentiment as positive, negative, or neutral. This information helps financial organizations understand how people feel about their products and services—and discover problems they need to address.

Examples of financial AI use

Many companies are using AI to help manage their back-end operations and provide better customer service. We cover some examples of AI in the finance industry today.

• Aladdin by BlackRock. Aladdin is a risk management program provided by BlackRock for use by both clients and BlackRock. It uses complex mathematical models to analyze data from different sources to offer insights into investment risks. Aladdin uses AI to help analyze data and provide better and faster results for users.
• Eno by Capital One. Eno is an AI-powered tool Capital One offers to help users manage financial tasks. It uses NLP to understand customer requests to help them manage their finances. Eno provides account information, helps users complete tasks, and offers advice based on customers’ current financial situation.
• Falcon by FICO. FICO’s Falcon software uses AI to learn about financial patterns to identify patterns of normal behavior. It then analyzes real-time data to look for abnormal transfers that indicate fraud.
• Zest underwriting. This AI application helps financial institutions build customer underwriting AI models to power their processes. Zest uses proprietary customer data and advanced models to help companies make more profitable lending decisions and find borrowers they may have otherwise rejected.

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Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.