5 Top Machine Learning Use Cases in Finance and Banking Industry

9 min readNov 17, 2022

For some, a mention of machine learning summons scenes from sci-fi movies like Ex Machina, where robots conquer people with their unlimited intellect. But the reality is that today’s ML — the ability of machines to learn from experience and perform heavyweight tasks instead of humans — is already a reality full of possibilities to enrich user experiences in many areas of human life, from reading mailbox to managing personal finances.

Machine learning revolutionized the Finance and Banking industries — from public relations to investment decisions and beyond. But how exactly can Fintech companies incorporate this innovative technology in their products to drive results? We will discuss 5 top machine learning use cases in Finance and Banking industries to shed light on the opportunities ML provides for Fintech and some challenges to overcome.

Stay tuned. This article will be read much faster by robots than by humans.

In this article, we’ll discuss:

Quick Facts About Machine Learning in Finance Industry

But before we dive into the real-life cases of ML usage in Banking and Finance, let’s see some stats. The main takeaway we’ve got from the analytical reports is simple: ML in finance is maturing, bringing the potential for higher-complexity solutions that generate positive ROI across business segments.

Here, read some facts we’ve highlighted:

Fact #1: adoption of AI and ML solutions in Finance is becoming mainstream. Many financial services companies report that they’ve incorporated the technology in domains like risk management (56%) and revenue generation through new products and processes (52%) due to the Cambridge Centre for Alternative Finance and the World Economic Forum.

Fact #2: by 2023, an aggregate cost saving for banks from AI applications is expected to be $447 billion due to Insider Intelligence’s AI in Banking report.

Fact #3: Implementing artificial intelligence and machine learning will be critical for financial institutions to stay competitive and thrive on the market by 2024. Both web and mobile banking adoption among US consumers will increase, reaching 72.8% and 58.1%, respectively, due to Insider Intelligence.

And most importantly, ML and AI have become the Fintech industry most impactful trends:

*Source: Mediant — Mediant FinTech Trends Report 2020*

What we’ve also noticed is that ML technology is being rapidly implemented in the banking sector — 75% of respondents at banks with over $100 billion in assets say they’re currently deploying AI and ML technologies due to Insider Intelligence. AI applications offer the most significant cost savings opportunity across digital banking.

That is precisely why we will start discussing machine learning use cases for the financial services industry with ML opportunities for banks.

5 Top Machine Learning Use Cases in Finance and Banking

Machine learning for fraud detection

As hackers get more and more creative with their tactics, banks face increased pressure to stay ahead of criminals when fighting financial crime, especially fraud and money laundering. ML implementation for fraud detection helps banks identify malicious activity, quickly verify user identity, and immediately respond to cyber-attacks.

Machine learning algorithms can process large amounts of data in a matter of seconds. Moreover, the ability to learn from previous experience and improve models minimizes human input. With ML algorithms, the system can quickly recognize suspicious activity and send alerts to the security operations center or automatically decline the transaction in case of credit card fraud.

Apart from rule-based fraud detection, ML allows to scan large amounts of data in real-time and minimizes human involvement in the process. It also makes the user experience much better by simplifying the identity verification measures. Here, see what difference ML makes comparing to rule-based fraud detection:

Here are a few notable startups that use machine learning to help businesses outsmart hackers and financial criminals:

Resistant.ai is a Prague-based machine learning-powered startup that helps to protect AI systems from targeted manipulation, adversarial machine learning attacks, and advanced fraud.

Riskified uses machine learning algorithms to provide payment insights that enable businesses to accept or reject transactions.

Feedzai is a US startup that uses machine learning to develop risk management tools to prevent fraud and money laundering in transactions.

Summing up, here are some benefits of ML for security:

Minimizing the risk of data breaches and cyberattacks by effective fraud monitoring.
Real-time effortless security monitoring that doesn’t require a lot of human input.
Detecting and reacting to fraud transactions that usually can’t be identified by manually defined rules.

Machine learning for meeting compliance regulations

Financial businesses worldwide have to comply with regulations to enter new markets, grow and compete. However, compliance sometimes feels like running an ultramarathon — regulations change even before you comply with previous ones.

The top 3 challenges financial institutions face due to ever-changing compliance standards are:

Operational disruption
Regulatory disputes and fines
Reduced or slower innovation

Source: The Economist Compliance and Regulatory Disruption 2018 report

Quick fact: in 2020, financial institutions around the world paid $10.6 billion in penalties for breaching anti-money laundering regulations, know-your-client tests and sanctions, due to Fenergo. It is a record.

Here’s how much countries spend on non-compliance:

ML has become a salvation for many financial institutions trying to comply with unstable regulations. Machine learning algorithms can process large amounts of regulatory documentation and find correlations between the guidelines. ML-powered systems can automatically detect the changes in laws as they appear and immediately react to those.

Here are several startups worth mentioning:

Zest.ai helps lenders make better decisions and better loans — increasing revenue, reducing risk, and automating compliance. Zest.ai has raised $232 million in funding over seven rounds.

ComplyAdvantage is a UK-based startup that provides AI-driven financial crime risk data and detection technology to comply with security regulations.

Sym is a newly established startup based in San Francisco that solves the intent-to-execution gap between policies and workflows.

To sum up, here are some benefits of ML for meeting the regulatory requirements:

Automated compliance regulations change monitoring.
Minimizing time consumption for regulatory work by automating manual processes.

Machine learning for customer retention

Imagine a $100B bank having to deal with hundreds of support requests every hour. Was my transaction processed? How do I increase my credit limit? How do I change the password?

The fact is that those requests are usually quite similar, and only some cases are exceptional and need real-time input from the support officer. Financial institutions can use machine learning to automate and fasten the support processes. With machine learning’s ability to delve into petabytes of data to find out exactly what matters to a particular client, financial institutions can benefit from providing personalized assistance and offers. What’s more — ML-powered solutions learn from previous experience and improve over time as they process more complex customer data.

One remarkable example of a machine learning use case in banking is chatbots. ML-powered chatbots provide real-time, client-oriented, and human-like assistance that enhances user experience and saves human and organizational resources of the company. Moreover, those chatbots learn from each request; this way, conversations become more and more personalized and helpful over time. As a result, both small and large Finance businesses can benefit from incorporating chatbots — with less need of running customer support departments.

While chatbots can primarily benefit a company of any industry, Finance and Banking are the ones that can make the most out of their ML-powered “employees.”

Here are a few notable chatbot machine learning use cases in banking:

Wells Fargo was the first US bank to create an ML-driven customer assistant for Facebook Messenger. It was one of the first to develop machine learning applications in banking.

Bank of America was one of the first banks to introduce a virtual ML-powered assistant within a mobile app. Due to the pandemic, Erica added 1 million users a month from March through May 2020, bringing its user count to 14 million.

Why the time is right to deploy chatbots:

The need for speed.
The need for personalization.
The need for data.

Machine learning in stock market forecasting

Just like the weather, stock market dynamics can be predicted. With lots of historical data available, there is an excellent opportunity for developing machine learning-driven solutions to forecast stock markets. Large historical datasets combined with real-time data sources can be a powerful tool for predicting the stock market dynamics using ML. From determining future risks to predicting stock prices, machine learning can be used for any kind of financial modeling. The only thing left is building a system that would enable algorithmic trading. Here’s how it works:

There are many machine learning models developed to forecast the prices on stock markets. Many of them are designed to forecast cryptocurrencies, like SARIMA and FB Prophet. While some might find it extremely difficult to predict the future of Bitcoin, we can understand where it might go with a high degree of confidence with machine learning.

See some of the machine learning algorithms use cases for stock prediction:

Walnut Algorithms is a France-based startup that utilized AI and ML finance solutions for investment management.

QARA utilizes the latest deep learning technology to analyze and forecast the financial markets. The company also developed a mobile application.

Summing up, here are the key benefits of machine learning for stock forecasting:

Machine learning can efficiently process large amounts of stock data of different periods, as well as monitor real-time situations.
Machine learning algorithms can notice even insignificant correlations to predict price changes.
The reliability of the forecast is much higher than the prediction made by humans.

Machine learning for risk assessment

According to the McKinsey Global Institute, machine learning for risk assessment solutions could generate more than $250 billion in the banking industry.

For banks, machine learning can significantly fasten and lower risks for the loan approval process. Credit scoring is one of the most useful deep learning use cases in banking.

Credit scoring applications analyze data from rent payments, social profiles, telecommunication companies, bank history, and tax payments — all to make a complete picture of an individual requesting a loan in a few minutes. The algorithms compare the data of other customers and generate a credit score to indicate the risk.

Here are a few machine learning banking use cases for credit scoring:

Kreditech is a German startup that helps the company determine the creditworthiness of potential borrowers who do not have an extensive banking history using predictive analytics and likely natural language processing.

Aire provides credit assessment services that give people a new credit score to help them qualify for essential financial products.

Consider KindGeek Your Trusted Machine Learning Development Partner

Fintech is one of our main domains. In 2016, we started as a company that delivers custom software development services for the Finance industry and grown into a trusted finance software development provider. Read about our Fintech projects here.

Final Thoughts

It’s already clear that AI is set to be revolutionary for Fintech and Banking. More so, those technologies are on their way to becoming irreplaceable for any financial institution to grow and compete on the local and global scale. Security and fraud detection, compliance, stock forecasting, risk assessment, client retention — all of that and beyond can be successfully done with ML.