10 Best Use Cases of Machine Learning in Finance

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Find out more about top use cases of machine learning for finance and the prospects that the application of AI and ML opens.

The demand and value of fast and high-quality financial engineering, financial analysis, and forecasting have increased over the last few years as we enter a new customer experience level. The clear benefits of using artificial intelligence and machine learning in finance, banking, and business analytics are difficult to overestimate. The advantages have been confirmed in practice by many successful cases.

Machine learning applications in banking enable companies to automate time-consuming, mundane processes, offering a far more streamlined and personalized customer experience. They also allow you to work more productively with large databases, significantly improving the quality of asset valuation, forecasting financial performance, and solving many critical issues of financial security of data.

This post outlines key messages on the role of machine learning in FinTech and deep learning use cases in banking. The article also focuses on the most practical use cases of AI and ML to optimize financial services.

The Role of Machine Learning in Finance

Investment in new technologies and primarily in AI in FinTech is a prerequisite for developing and systematically improving the quality of work with clients and in terms of finance, data, and cybersecurity.

An expert team from Mediant Inc. cites interesting statistics that give more than a specific forecast of the FinTech industry’s investment trends in the next few years. Based on statistical research data, artificial intelligence technology and deep learning in finance will become the leading investment in the next five years, along with institutional tools to unify and manage existing processes.

The main functional destination of ML algorithms is to prominently identify work patterns and correlations among vast amounts of information, events, operations, and sequences. Thus, ML is successfully used today in process automation, security issues, customer support optimization, credit offerings, portfolio optimization, personal finance, and many other sectors.

In reality, you are dealing with the work of machine learning banking use cases if you are a client of a bank, insurance service, or any FinTech company. Some experts ironically call the introduction of AI into the financial market white magic because it’s almost invisible, yet it still changes the interaction of the customer and the company for the better.

For example, the leading commercial bank in Ukraine, PrivatBank, has effectively practiced a technology with chatbot assistants in web platforms and mobile applications. AI-based chatbots optimized the processing time of general query resolution significantly.

The worldwide known company PayPal invests in deep learning in security terms to improve its financial monitoring and fraud detection.

So, if you’re looking for technology like the ones mentioned earlier — check Fayrix’s technical competence in development to find out how we can help.

10 Use Cases of Machine Learning for Finance

We have already found out that the subject of discussion is an indispensable tool for the FinTech industry today. AI technologies can be widely used both for more general business functions (from detecting spam to document categorization) and for more specialized needs of financial companies (from stock technical analysis to credit scoring). Here, you can check Fayrix’s custom software development service to learn more.

Next, we will detail some of the most popular cases of putting this technology to practice. Let’s explore the advantages of machine learning in banking and finance!

Machine learning for customer experience in financial services

The highest level of customer support is the primary marker of the quality of the financial services provided. And this is precisely the factor in which leading financial companies are fighting for leadership. ML helps organizations improve customer experience, services, and optimize budgets. In most cases, process automation replaces routine manual work, automates tasks, and makes their realization more productive.

Among the most striking examples of automation of processes to increase customer service level in finance are the automation of paperwork and call centers, and the use of chatbots.

One of the ‘big four’ US banks Wells Fargo, a holding company providing financial, banking, and insurance services in the USA, Canada, and Puerto Rico, constantly invests in improving customer support. It successfully uses an AI-based chatbot to communicate with users more productively and provide supportive assistance using accounts and codes.

Customer onboarding

Customer onboarding is the complete process that users go through when they act as clients of a bank or FinTech company. Onboarding experience can truly determine the customer’s current relationship with the organization. To visualize the results of applying ML for client onboarding, try to look at the interface of any popular social network.

Any slightest change in the start page’s design or an application shortcut on your desktop, any change in the algorithm, and functional innovation is not just happening at the developer’s whim. Artificial intelligence studies the patterns of users on the web and, based on the analysis of the behavior of millions of customers, changes, and improvements are created.

Among the examples of successful cases in this direction is the experience of German challenger N26 Bank. Being fully digital enables customers to manage their financial lives directly from their smartphones from day one. It invites customers to open an account in minutes, right from the smartphone, manage it on the go, spend, and set aside money in real-time. This offer makes total sense for the customer. Nowadays, previously complex processes like onboarding can happen anywhere in minutes.

Fraud detection and prevention

As long as the number of transactions, real clients, and integrations grows, security threats will come along. This is when machine learning algorithms come in handy when banks and other institutions require special fraud detection.

Banking organizations can use it to monitor a considerable amount of transactional parameters at once for every account in real time. The algorithm examines historical payment data and analyses every cardholder’s action. Such models can be highly prominent and prevent suspicious behavior with great precision.

A global payment system Payoneer provides financial services and online money transfers worldwide. Accordingly, the company’s customer database is estimated in millions. Since the company is a registered MasterCard provider worldwide, transaction security would fail without ML use cases in banking.

Portfolio management

Portfolio management is an online wealth management service that uses statistical points of the issue as well as automated algorithms to optimize the performance of clients’ assets. Customers fill in their financial goals, for example, to save some amount of money during a certain period of time. The robot advisor then assigns current assets to investment variants and opportunities. Portfolio management involves creating and overseeing selected investments that align with the investor’s long-term financial goals and risk tolerance.

One of the world’s largest investment management firms, BlackRock Investment Company, offers Aladdin, an operating system created and adapted for investment managers’ needs. The company claims that Aladdin can use machine learning in FinTech to provide investment managers in financial institutions with risk analytics and portfolio management software tools to make more informed investment decisions and operate more efficiently.

Assessment and management of credit risks

Credit risk is the economic loss that emanates from a counterparty’s failure to fulfill its contractual obligations or the increased risk of default during the transaction term. The increased complexity of assessing credit risks has opened the door to deep learning in finance. This is evident in the growing credit default swap market, where there are many uncertain elements — involving determining both the likelihood of an event of credit default and estimating the cost if a default occurs.

The other side of ML implementation in risk management is the term of security. Citi’s Treasury and Trade Solutions Group has entered into a strategic partnership with Feedzai, an artificial intelligence leader for real-time risk management across banking and commerce.

Citi integrated Feedzai’s transaction monitoring platform powered by advanced technology into its own proprietary services and platforms to provide clients with enhanced control and risk management for payments transactions. The ML solution compares all the possible data points in current and preceding transactions to detect suspicious transactions with compliance concerns.

Customer churn prediction

Customer churn forecasting is one of the most popular big data use cases in business. It lies in detecting customers who can cancel the regular subscription. The scope for implementing the methods is enormous — from sales funnels in commercial mailings to tailoring various loyalty programs for customers.

Any large telecommunications company or mobile operator can be cited as an example of machine learning’s practical application in predicting customer churn. This category includes almost any business that sells subscriptions.

Video streaming giant Netflix had a total net income of over 1.86 billion U.S. dollars in 2019, while the company’s annual revenue reached 20.15 billion U.S. dollars. The number of Netflix’s streaming subscribers worldwide has continued to grow in recent years, reaching 167 million in the fourth quarter of 2019. If you’re interested in developing a mobile application — check Fayrix’s competencies to find out more.

Asset valuation and management

Asset management for digital assets or distributed industrial assets are applications where voluminous data about the assets is already being recorded, making it ripe for automation through AI.

Asset and wealth management firms are exploring the potential AI solutions for improving their investment decisions and using their troves of historical data. Approximately 13.5% of the AI vendors in banking are for wealth and asset management solutions.

Stock market forecasting

Predictions of stock market fluctuations are often underestimated in the trading sector and even considered pseudoscientific. Some old-school traders still think so, too, and study tons of stock charts with Japanese candlesticks every day.

However, businesses today can make estimated guesses and informed forecasts based on the information they have in the present and the past regarding any stock. An estimated guess from past movements and patterns in stock price is called stock technical analysis, and it is used to predict a stock’s price direction. At the same time, the most prominent technique involves the use of artificial neural networks and algorithms.

Algorithms and their usage in the stock market and trading

Financial machine learning helps to solve tasks with the winning trading decisions in the algorithmic trading sphere. A mathematical model monitors the latest updates of the market information and trade results in real time. A special algorithm was created to detect patterns that can impact the dynamic of stock prices in their increasing or decreasing course. It can then act proactively to sell, hold, or buy stocks, using factual information related to forecasts.

Machine learning algorithms can analyze tons of data sources and market conditions simultaneously. And, understandably, human traders cannot physically achieve it because of the massive amount of information.

Underwriting and credit scoring

The so-called credit scoring system assesses a person’s creditworthiness and credit risks based on numerical statistical methods. This technology is often used in fast lending for small amounts when registering consumer express loans in real stores by credit companies in the business of mobile operators or insurance companies.

Scoring is the assignment of points by filling out a questionnaire developed by credit risk assessors. Based on the results of the points gained, the system automatically decides to approve or refuse to issue a loan.

The data for scoring systems is obtained from the probabilities of loan repayment by individual groups of borrowers, which was received from the analysis of the credit history of thousands of people. It’s believed that there is a correlation between certain social characteristics of a particular client, including having children, marital status, the level of education, and the conscientiousness of the borrower.

For example, one of the largest credit history bureaus in the United States with more than a century-long history, Equifax, successfully implements machine learning and FinTech technology. They use them in credit scoring to provide credit ratings and demographics to businesses and offer commercial credit monitoring and fraud prevention services directly to consumers.

Examples of Successful Startups in the Finance Industry

Below are some outstanding solutions leveraging artificial intelligence and machine learning to help both banks and ordinary users better deal with finance management.


This is one of the most popular machine learning startups in the banking industry. What’s more, the solution is created on the verge of banking and eCommerce since its main task is to identify fraud at the checkout stage. Using the technologies of deep learning, the solution verifies the identity of the customers, plus concludes on the payment intent, taking into account the previous transactions and user behaviour patterns.


HyperScience is one of the AI and machine learning startups in banking aimed at improving productivity, eliminating human mistakes, and streamlining routine tasks. Being powered by the technology of Intelligent Document Processing, the solution turns handwritten invoices into accurate data.


This is another fascinating machine learning startup in the finance industry created for an automated back-office audit. The platform analyzes contracts and invoices to find mistakes, frauds, and spending patterns. The solution is also equipped with Natural Language Processing (NLP) and advanced computer vision.

Zest Finance

This platform is one of the examples of ML startups in the banking industry aimed at improving credit scoring. With the help of advanced credit modelling, the solution identifies the possible risks and helps lenders make informed decisions. Zest Finance also supports the concepts of open finance, analyzing all the available data on customer solvency to get a full picture.

Future Advisor

ML technologies in finance startup projects go beyond data gathering and analysis. Future Advisor is one of them, powered by predictive analytics. The platform suggests data-driven investment decisions and wealth-management tactics depending on the goal, plus considers the opportunities for taxation optimization.


Using AI technologies in banking startups to solve the bank’s routine and more sophisticated tasks isn’t the only opportunity. Machine learning in finance startups can serve ordinary users as well. For example, Cleo is a smart budgeting application that analyzes spending habits and patterns, provides budgeting tips, suggests setting financial goals, and helps the users achieve them. The intuitive design and funny user experience are the features that make Cleo even more outstanding.

Our Experience

Managing your startup project is always challenging, especially if you are going to disrupt the FinTech market with your outstanding idea. However, there are a lot of technical and compliance issues to solve in advance, and Fayrix is here to help you with both of the tasks.

We are an offshore software development company providing web and mobile design services for startups. What’s more, we perfectly understand their needs for the highest-end quality of the final project, reasonably matched with the development cost. To optimize the development process as much as possible, we set up remote and dedicated tech teams with the opportunity to relocate them at a certain stage of startup growth.

Our extensive experience in software development for startups covers but isn’t limited to creating FinTech solutions for demand forecasting, warehouse optimization, customer churn prediction, and credit scoring.

Get in touch with us right now to discuss your idea and outline the development strategy for your future FinTech project!

Fayrix has extensive experience in beneficial cases of machine learning for finance and specific tasks in business. Ready-made solutions can be applied in various areas, from demand forecasting and warehouse optimization to customer churn prediction and credit scoring.

Among our customers are the largest banks in various countries. We had successful cooperation with US Santander Bank. We helped build a predictive model for determining customer consumption of bank products. The main goal of the method implementation was to increase the conversion rate in sales campaigns for a specific banking product aimed at a narrow target audience.

One more successful machine learning use case in banking was with the most prominent Russian bank — Sberbank. Fayrix’s team had to build a prediction model of the total and individual customers’ spending. We have developed an ensemble of models to solve the problem: forecasting time series, linear regression, and decision trees. The goals of this solution were to increase sales and loyalty through relevant personal offers. The essence of the implemented method was to predict the client’s transactions with a plastic card.

Revoleto is an e-learning platform that helps users discover the world of investments and trading. The company aims to develop an e-learning platform for traders to allow beginners to dive into the trading market. Revoleto’s wide range of helpful investment and trading tools helps trading enthusiasts deepen their understanding and learn how to analyze the market correctly.

Final thoughts

ML is more prevalent in finance than any other industry because of the available computer power and new machine learning tools. The greatest benefits of machine learning in finance include simplifying product marketing and helping with accurate sales forecasts.

Machine learning algorithms can deal with many tasks that exceed human capacity and eliminate human error. As the algorithms constantly learn, they serve as a bridge to a completely flawless automated financial system. If your business needs such an advantage to outrun the competitors, contact the Fayrix team. We will be happy to discuss your project ideas!

If you are still unsure which technology you will need in your future project, check Fayrix’s technological competencies and choose the best solution to solve all your business goals.


What are the main use cases of machine learning in finance?

In most cases, ML technologies in finance startup projects open up many opportunities to analyze customers’ data and behavior patterns, discover spending habits, evaluate actual earnings, make less risky lending and investment decisions, improve the customer experience, prevent fraud, and automate document processing.

What are the most popular projects developed with ML for the finance industry?

The most popular AI and machine learning startups in banking are Sygnifyd (a solution that detects fraud when making online payments), HyperScience (the platform that automates invoicing processing), Future Advisor (the AI decision-making solution that suggests beneficial investing options), and Cleo (the AI-powered personal budgeting and money management application).

Will AI replace finance jobs?

There are a lot of promising startups in the banking industry, and some of them have already eliminated the need for human participation, for example, those apps automating routine tasks. Indeed, AI is likely to replace some finance jobs because of this technology’s massive potential and COVID-19 impact. However, there are still tasks that require the human mind, intuition, experience, and creativity, and the jobs related to them are unlikely to be entirely replaced with AI.