Important points
Machine learning in finance enhances business processes, supports informed decision-making, and assists with predictive tasks.
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Machine learning applications in finance include process automation, customer relationship management, security analysis, stock market prediction, and fraud detection.
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Working with machine learning in the finance field primarily requires a degree in computer science or a related field, as well as skills in programming languages, statistical analysis, and communication.
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You can fill a variety of machine learning roles in the financial industry, including quantitative research analysts, machine learning engineers, machine learning modelers, and machine learning architects.
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What is machine learning?
Machine learning (ML) is a branch of computer science and artificial intelligence (AI). This is the design and development of algorithms that can “learn” from data to make predictions. In other words, machine learning models can imitate cognitive processes by acquiring knowledge through data and using it to process and analyze information. It is used to automate cognitive tasks.
How is machine learning used in the financial sector?
Machine learning systems help people make sense of large amounts of data and uncover important patterns within it. This information is used to enhance business processes, make informed decisions, and assist with predictive tasks. Financial services companies use it to provide better pricing, reduce risks caused by human error, automate repetitive tasks, and understand customer behavior.
10 use cases for machine learning in finance
Here are 10 common applications of machine learning in financial markets.
1. Process automation in corporate finance
The ability to streamline and automate business processes offers a number of benefits to financial companies. For example, organizations can use these technologies to automate simple tasks such as data entry and financial monitoring. This frees up employees to focus on tasks that actually require human intervention.
2. Strengthening customer relationships
One of the most practical applications of machine learning in finance is customer interaction. Financial companies are using ML technologies like chatbots to improve customer experience through on-demand help and real-time recommendations. Additionally, insurance companies 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 important area for leveraging machine learning and AI. IoT devices generate large amounts of data that helps understand customer behavior and preferences [1]. The data can be used to create personalized marketing campaigns and improve the way you serve your customers. Improving the overall customer experience typically increases customer satisfaction and customer retention.
3. Securities analysis and portfolio management (robo advisor)
Robo-advisors are a notable example of a machine learning use case in finance. It may vary slightly depending on the financial company providing the service. However, the term “robo-advisor” typically refers to online services that provide investment advice and allow users to create and manage investment portfolios. Robo-advisors rely on a wide range of user input settings. For example, risk appetite assesses users’ needs by gathering information about the decisions they would make when faced with unpredictable situations.
4. Stock market prediction
The financial industry often uses ML technology to predict stock prices and influence trading decisions. It works by using large historical datasets to make predictions about the future. Here are two types of trading enabled by machine learning technology:
5. Fraud Detection
Machine learning models learn by identifying patterns. These patterns help you understand normal behavior and make it easier to detect suspicious activity such as money laundering or insider trading.
6. Online lending platforms and credit scoring
The financial industry uses machine learning tools to evaluate loan applications and calculate credit scores. The online lending platform generates real-time reports and recommends available loans based on the user’s financial history.
7. Risk management and prevention
ML technology is often used in the financial sector 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 data and big data analysis
Machine learning in finance has made it easier to extract and analyze unstructured data from documents such as contracts and financial reports.
Big data analysis for competitive advantage
Big data analytics has become essential to understanding customer behavior and trends. Machine learning and AI can help you understand large datasets, identify patterns, and make predictions. This allows you to make better decisions faster than your competitors, giving you a competitive advantage.
9. Automating the trade settlement process
The transaction settlement process is time-consuming and error-prone. In some cases, the transaction may fail. Before the introduction of machine learning in the financial sector, administrative staff at financial institutions were required to handle transaction failures, identify the reason, and resolve the issue. This labor-intensive process has been simplified using ML tools that automatically flag issues and provide recommendations for resolution.
10. Asset Valuation and Management
Asset managers use ML and AI to value and manage assets such as stocks and bonds. Data-driven decision-making helps eliminate human error caused by confirmation bias and loss aversion.
How to use machine learning in finance to advance your career
Companies in the financial sector are increasingly relying on data-driven decision-making. As the field of machine learning evolves, new opportunities will emerge for those with machine learning expertise to apply their skills in the financial sector.
Job prospects for machine learning professionals in the financial industry
Qualified workers with machine learning expertise will be in high demand. According to the Bureau of Labor Statistics (BLS) website, machine learning jobs fall under the computer and information research analyst employment category. The BLS projects that employment in this category will increase by 20% from 2024 to 2034. [2]much faster than the average for all occupations.
Job titles and salaries related to machine learning in banking and finance
Banks, hedge funds, and other financial companies are looking for machine learning talent, and there is a huge demand for machine learning professionals in the financial industry with very competitive salaries. Below are some examples of machine learning careers in the financial industry and their respective salaries.
*All salary information was obtained from Glassdoor in May 2026 and represents average annual base salary. Additional salaries such as commissions and bonuses are not included.
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Machine learning data analyst: $101,000
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Quantitative Research Analyst: $119,000
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Machine learning engineer: $126,000
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Machine learning modeler: $118,000
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Data scientists in the financial field: $105,000
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Machine learning scientist: $138,000
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Lead Data Scientist: $171,000
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Machine learning architect: $141,000
Skills required for ML professionals in finance
There are many different types of machine learning jobs, each requiring different qualifications and skills. For example, machine learning engineers require advanced engineering and programming skills, while machine learning scientists require advanced math and statistical skills. Common criteria for applying for a machine learning job include:
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Proficient in using programming languages such as Python, R, and Java.
read more: Machine Learning Skills: An Getting Started Guide
If you’re new to this field, consider learning the basics from an industry leader like Google. This self-paced, beginner-friendly online course provides foundational knowledge in data visualization, ethics, and analysis. Once completed, you will receive a professional certification for your resume.
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