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.
Machine learning belongs under the umbrella of artificial intelligence. 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 financial companies make better pricing, risk, and customer behavior decisions. The technology can build models that improve understanding large data sets and uncover patterns that facilitate new business systems and processes.
Machine learning and artificial intelligence 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.
Working in finance, the ability to streamline and automate various processes using machine learning has many benefits. Finance companies can 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 is another critical area where machine learning and AI can be used. IoT devices can generate considerable data useful for understanding customer behavior and preferences . 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 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.
Machine learning can be used to help create accurate predictive models that reduce error and risk. There are new applications and opportunities for the use of machine learning in the financial sector emerging all of the time. Below are some established ways this exciting technology is used in the finance industry.
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
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.
Each of the "big four" banks in the United States—JP Morgan Chase, Bank of America, Wells Fargo, and Citigroup—uses machine learning in various ways to improve its operations. Machine learning is used to detect and prevent fraud, better target marketing efforts, and streamline back-office processes.
In addition, they are using machine learning to develop new financial products and services, such as predictive analytics tools. This can help their customers to make better investment decisions.
Machine learning is relatively new in finance and other industries, and there is already high demand for qualified workers. Machine learning jobs fall under employment categories like computer programming, software development, and financial analysis. Jobs in the field fall primarily under the category of computer and information research analysts on the Bureau of Labor Statistics (BLS) website. The BLS projects that employment in this category will grow by 22 percent from 2020 to 2030  which is much faster than the average for all occupations.
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.
With machine learning skills and experience, there are many opportunities in the finance industry. 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.
Machine learning data analyst: $95,971 per year 
Quantitative research analyst: $102,430 per year 
Machine learning engineer: $124,572 per year 
Machine learning modeler: $130,492 per year 
Data scientist in finance: $122,545 per year 
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
Proficiency 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
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 various online resources devoted to teaching the basics of machine learning, 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.
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ...
89,360 already enrolled
Average time: 1 month(s)
Learn at your own pace
Skills you'll build:
Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification
1. Springer US. “How Artificial Intelligence Will Change the Future of Marketing, https://link.springer.com/article/10.1007/s11747-019-00696-0." Accessed July 26, 2022.
2. US Bureau of Labor Statistics (BLS). “Computer and information research scientists, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm.” Accessed May 22, 2022.
3. Glassdoor. “How much does a machine learning data analyst make? https://www.glassdoor.com/Salaries/machine-learning-data-analyst-salary-SRCH_KO0,29.htm.” Accessed May 22, 2022.
4. Glassdoor. “How much does a qualitative research analyst make? https://www.glassdoor.com/Salaries/us-quantitative-research-analyst-salary-SRCH_IL.0,2_IN1_KO3,32.htm.” Accessed May 22, 2022.
5. Glassdoor. “How much does a machine learning engineer make? https://www.glassdoor.com/Salaries/us-machine-learning-engineer-salary-SRCH_IL.0,2_IN1_KO3,28.htm.” Accessed May 22, 2022.
6. Glassdoor. “How much does a machine learning modeler make? https://www.glassdoor.com/Salaries/us-machine-learning-modeler-salary-SRCH_IL.0,2_IN1_KO3,27.htm.” Accessed May 22, 2022.
7. Glassdoor. “How much does a data scientist in finance make? https://www.glassdoor.com/Salaries/us-data-scientist-in-finance-salary-SRCH_IL.0,2_IN1_KO3,28.htm.” Accessed May 22, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.