Machine Learning in Finance: 10 Applications and Use Cases

The use of machine learning techniques in the financial industry is steadily evolving. Today, machine learning (ML) is used for everything from risk assessment to trading decisions. It has changed how the financial services industry operates and manages data. In the following article, learn more about advances in financial machine learning and how you can advance your career in the field.

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What is machine learning?

Machine learning, or ML, is a branch of computer science and artificial intelligence (AI). It is the design and development of algorithms capable of "learning" from data to make predictions. In other words, machine learning models can mimic the cognitive process by acquiring knowledge through data and using it to process and analyze information. It is used to automate cognitive tasks.

How can machine learning be used in finance?

Machine learning systems help people understand massive volumes of data and uncover important patterns within them. This information is used to enhance business processes, make informed decisions, and assist with prediction tasks. Financial services companies use it to offer better pricing, mitigate risks caused by human error, automate repetitive tasks, and understand customer behavior.

10 machine learning applications in finance 

Here are ten common applications of machine learning in financial markets.

1. Process automation in corporate finance

The ability to streamline and automate business processes benefits financial companies in several ways. For example, organizations can use these technologies to automate menial tasks such as data input and financial monitoring. This enables employees to focus on tasks that actually require human intervention. 

2. Enhanced customer relations

One of the most practical applications of machine learning in finance is in customer relations. Finance companies utilize ML technology like chatbots to improve the customer experience through on-demand help and real-time recommendations. Additionally, insurance firms often automate customer acquisition and onboarding to make the process faster and easier.

Customer engagement and the Internet of Things (IoT)

Customer engagement is another critical area for machine learning and AI utilization. IoT devices generate considerable data useful for understanding customer behavior and preferences [1]. The data can then be used to create personalized marketing campaigns or to improve customer service methods. Better overall customer experience typically leads to higher customer satisfaction rates and retention.

3. Security analysis and portfolio management (robo-advisors)

Robo-advisors are a notable example of machine learning use cases in finance. They can vary slightly depending on the financial company offering the service. However, the term "robo-advisor" typically refers to online services that provide investment advice and help users create and manage investment portfolios. Robo-advisors depend on a wide range of user input preferences. For example, risk preferences gauge user needs by collecting information about the decisions they would make in the face of unpredictable circumstances.

4. Stock market forecasting 

The finance industry often uses ML technology to predict stock prices and influence trading decisions. It works by using large historical data sets to make predictions about the future. Here are two types of trading that machine learning technology enables:

• Algorithmic trading: Identifying patterns and developing trading strategies with speed and accuracy

• High-frequency trading (HFT): Identifying trading opportunities and executing trades at high speeds

5. Fraud detection

Machine learning models learn by identifying patterns. These patterns help them understand normal behavior and make it easier to detect suspicious activities like money laundering or insider trading.

6. Online lending platforms and credit scoring

The finance industry uses machine learning tools to assess loan applications and calculate credit scores. Online lending platforms generate real-time reports and recommend accessible loans to users based on their financial history. 

7. Risk management and prevention

ML technology is often used in finance to support investment decisions by identifying risks based on historical data and probability statistics. It can also be used to weigh possible outcomes and develop risk management strategies. 

8. Unstructured and big data analysis

Machine learning in finance has made extracting and analyzing unstructured data from documents like contracts or financial reports easier.

Big data analysis for a competitive edge

Big data analysis has become essential for understanding customer behavior and trends. Machine learning and AI can help you make sense of large data sets, identify patterns, and make predictions. This can help to gain a competitive edge by making better decisions faster than your competitors.

9. Trade settlement process automation

The trade settlement process can be time-consuming and error-prone. At times, trades can even fail. Prior to the introduction of machine learning in finance, office staff at financial institutions would need to process the trade failure, identify the reason, and resolve the issues. This labor-intensive process has been simplified by using ML tools that automatically flag issues and offer recommendations for resolution.

10. Asset valuation and management

Asset managers use ML and AI to value and manage assets, including stocks and bonds. Data-driven decision-making helps eliminate human error caused by confirmation bias or loss aversion. 

How to use machine learning in finance to advance your career

Businesses in the finance sector increasingly rely on data-driven decision-making. As the field of machine learning evolves, there will be new opportunities for those with machine learning expertise to apply their skills in the finance sector.

Job outlook for machine learning professionals in finance

There is a high demand for qualified workers with machine learning expertise. According to the Bureau of Labor Statistics (BLS) website, machine learning jobs fall under the employment category of computer and information research analysts. The BLS projects that employment in this category will grow by 20 percent from 2024 to 2034 [2], much faster than the average for all occupations. 

Relevant job titles and salaries for machine learning in banking and finance

Banks, hedge funds, and other financial firms seek machine learning talent, and there is significant demand for machine learning professionals in finance with very competitive pay. Here are a few examples of machine learning careers in finance with their respective salaries:

*Note: All salary information was sourced from Glassdoor in September 2025. Figures represent the median total yearly salary, which includes base pay and additional pay such as commissions and bonuses.

• Machine learning data analyst: $125,000

• Quantitative research analyst: $182,000

• Machine learning engineer: $157,000

• Machine learning modeler: $148,000

• Data scientist in finance: $140,000

• Machine learning scientist: $187,000

• Principal data scientist: $274,000

• Machine learning architect: $178,000

Required skills for ML professionals in finance

There are various types of machine learning jobs out there, each requiring different qualifications and skills. For example, a machine learning engineer will need strong engineering and programming skills, while a machine learning scientist will need strong mathematical and statistical skills. Some of the common criteria for applying for machine learning jobs include:

• A four-year degree in computer science or a related field. Fifty-one percent of data scientists have a bachelor's degree, 34 percent have a master's degree, and 13 percent have a doctorate [3]

• Proficiency in using programming languages, including Python, R, and Java

• Experience with statistical analysis and machine learning algorithms

• Ability to effectively communicate results of data analysis to non-technical audiences

• Ability to work with large data sets

Read more: Machine Learning Skills: Your Guide to Getting Started

If you're new to the field, consider learning the basics from an industry leader like Google. In this self-paced, online, beginner-friendly course, you'll gain foundational knowledge about data visualization, ethics, and analysis. Upon completion, you'll earn a Professional Certificate for your resume.

Navigate your next career move with confidence

Exploring your career options? Discover expert advice and the latest insights by joining our LinkedIn newsletter, Career Chat. You can also keep building your machine learning skills with our free resources:

• Take the Quiz: Which Machine Learning Course Should You Take? 

• Watch on YouTube: Career Spotlight: Machine Learning Engineer, or Beginners: Start Your AI Journey with These 3 Skills 

• Hear from an industry professional: Becoming an AI Engineer: 7 Questions with an IBMer

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