A Guide to Machine Learning in Finance

Is machine learning the key to efficient financial operations? Machine learning applications can be used for everything from risk assessment to asset management, using data for critical insights and streamlining various processes while optimizing results.

Using machine learning in financial applications is an evolving practice utilized in various ways throughout the industry. The diverse applications of machine learning in finance have also opened up many new machine learning finance jobs. But first, it helps to understand machine learning in finance and how it can be used to build a career.

What exactly is machine learning in finance?

Machine learning belongs under the umbrella of artificial intelligence (AI). It deals with designing and developing algorithms that can learn from and make predictions based on data. Machine learning models provide the technology to automate cognitive tasks. Various financial tasks utilize machine learning technology, including credit scoring, investment monitoring and recommendations, fraud detection, and algorithmic trading. 

Machine learning can help finance companies make better pricing, risk, and customer behaviour decisions. The technology can build models that improve understanding large data sets and uncover patterns that facilitate new business systems and processes.

Why finance companies are turning to machine learning and AI

Machine learning and AI can speed up processes and improve how raw data is used. In turn, it can save time and money while improving business outcomes. Finance companies are turning to machine learning and AI more and more to automate repetitive tasks, provide better customer service and experiences, and gain an edge over their competitors.

Streamlining and process automation

Working in finance, the ability to streamline and automate various processes using machine learning has many benefits. Finance companies use these technologies to automate tasks such as paperwork, calculations, data monitoring, and claims processing. This can free up employees to focus on more value-added activities.

Customer engagement and personalization: The Internet of Things

Customer engagement is another critical area where machine learning and AI can be used. Internet of Things (IoT) devices can generate considerable data useful for understanding customer behaviour and preferences [1]. The data can then be used to create personalized marketing campaigns or to improve customer service. Overall, better customer service and improved customer experiences typically lead to more sales and higher customer satisfaction rates.

Big data analysis for a competitive edge

Big data analysis has become essential for understanding customer behaviour 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 and faster decisions over your competitors.

15 applications of machine learning in finance

Machine learning can be used to help create accurate predictive models that reduce error and risk. New machine learning applications and opportunities are always emerging in the financial sector. Below are some established ways this exciting technology is used in finance.

• Data input, monitoring, and update process automation: Automating repetitive and time-consuming tasks.

• Security portfolio management (Robo-advisors): Creating and managing investment portfolios

• 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.

• Fraud detection: Detecting fraudulent activities like money laundering and insider trading.

• Loan, underwriting, and credit scoring: Assessing loan applications and the creditworthiness of borrowers.

• Risk management: Identifying risks and developing risk management strategies.

• Chatbots: Creating chatbots that provide customer support or financial advice.

• Document and unstructured data analysis: Extracting information from documents, including contracts and financial reports.

• Trade settlements: Automating the trade settlement process.

• RegTech: Responding to changes in the regulatory landscape.

• Customer experience: Improving the customer experience through personalization and recommendations.

• Customer acquisition and onboarding: Automating the process of customer acquisition and onboarding.

• Asset valuation and management: Value and manage assets, including stocks and bonds.

• Stock market forecasting: Predicting future movements in the stock market.

Machine learning use cases in banking

Many of the biggest banks in Canada—like RBC Royal Bank, TD Bank, and Scotiabank—use machine learning in various ways to improve their operations. Machine learning detects and prevents fraud, better targets marketing efforts, and streames back-office processes. 

In addition, they use machine learning to develop new financial products and services, such as predictive analytics tools. This can help their customers to make better investment decisions.

Careers in machine learning in the finance sector

Machine learning is relatively new in finance and other industries, and there is already a high demand for qualified employees. Machine learning jobs fall under employment categories like computer programming, software development, and financial analysis. The finance sector brings in the second largest income out of all sectors of the Canadian economy [2]; leaders in this sector recognize the importance and potential of machine learning to improve operations. 

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.

Jobs titles in machine learning in finance (with salaries)

With machine learning skills and experience, there are many opportunities in finance. Banks, hedge funds, and other financial services firms seek machine learning talent; there is significant demand for machine learning professionals in finance with very competitive pay in Canada.

• Quantitative research analyst: $62,079 per year [3]

• Machine learning engineer: $111,763 per year [4]

• Machine learning modeller: $105,190 per year [5]

• Data scientist in finance: $98,569 per year [6]

Criteria for applying for machine learning jobs

Various types of machine learning jobs are available, 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 to machine learning jobs include:

• A four-year degree in computer science, mathematics, or a related field, and, in some cases, a graduate degree in a related area

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

• Up-to-date certifications and proficiency in relevant software

• 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

Get started in machine learning with an online course.

As you begin your career in machine learning, you’ll need to familiarize yourself with the foundational concepts, including models, regression, neural networks, training data sets, and computing resources. If you're not already familiar with the basics of machine learning, it's essential to spend some time getting up to speed.

You can find several online resources devoted to teaching the basics of machine learning on Coursera, including courses from leading universities such as Stanford and MIT. For example, Stanford offers a machine learning course. The course enables you to learn and practice the fundamentals of machine learning. It might be a good starting point on your journey, as it has been for nearly five million other learners.