Machine Learning for Financial Services from Pluralsight

This course will explore the conceptual aspects of applying machine learning to problems in the financial services industry and discuss case studies of machine learning used in financial services.

Analytical and statistical models are already an integral part of the finance industry and the use of machine learning builds on a strong foundation in this industry. The financial services industry is uniquely positioned to leverage machine learning because of the vast quantities of high-quality data already available.

In this course, Machine Learning for Financial Services, you will explore machine learning techniques currently applied in the financial services industry. First, you will look at some examples and cases of where ML is already being used in financial services - for investment predictions, loan automation, process automation, and fraud detection. Then, you will develop an intuitive understanding of how recurrent neural networks

Next, you will explore two ML case studies from research papers - the first focusing on assessing and quantifying the return on investment and the second exploring how classification and clustering models can help detect money laundering.

Finally, you will get hands-on coding and see how you can use a classification model for fraud detection on a synthetically generated dataset.

When you are finished with this course, you will have the awareness of how machine learning can be applied in the financial services industry and hands-on experience working with financial data.

What's inside

Syllabus

Course Overview

Exploring Applications of Machine Learning in Financial Services

Case Study: Quantifying Risk and Return of Investment Opportunities

Case Study: Extracting Insights for Fraud Detection

Applying Machine Learning Techniques to Financial Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Geared toward existing analysts with a background in finance, this course assumes familiarity with analytical and statistical models

Led by industry practitioners Janani Ravi

Builds awareness of machine learning's applications in the financial services sector

Incorporates case studies that demonstrate real-world usage

Includes hands-on exercises to reinforce concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning for Financial Services with these activities:

Review Basic Data Analysis

Show steps

Refresh foundational skills in data analysis to prepare for advanced topics in machine learning techniques in financial services.

Browse courses on Descriptive Statistics

Show steps

Revise statistical concepts like mean, median, mode and standard deviation.
Review methods of data visualization like histograms, scatterplots and box plots.

Review financial services modeling basics

Show steps

Review the basics of financial services modeling to strengthen foundational knowledge and enhance understanding of the course material.

Browse courses on Financial Modeling

Show steps

Read introductory articles or textbooks on financial services modeling.
Review notes or summaries of past courses or self-study materials on financial modeling.

Join a study group for peer support

Show steps

Connect with fellow students to form a study group for peer support, knowledge sharing, and collaborative problem-solving to enhance learning and retention.

Show steps

Reach out to classmates or online forums to find interested individuals for a study group.
Establish regular meeting times and a platform for communication.
Collaborate on assignments, share resources, and engage in discussions to strengthen understanding.

Show all three activities

Career center

Learners who complete Machine Learning for Financial Services will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist combines programming knowledge with a deep understanding of statistics and machine learning. They study data using advanced analytic techniques. Many Data Scientists work in the financial industry where they use machine learning to make predictions about investments, automate processes, and detect fraud. This course will teach you how to do all of these things and can help you land a job as a Data Scientist in financial services.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

A Quantitative Analyst (Quant) develops and implements mathematical models to solve problems in the financial industry. They may work on projects such as pricing financial instruments, developing trading strategies, or managing risk. Machine learning is a core skill for Quants and this course may help you qualify for roles in the financial services industry.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

A Financial Analyst uses their understanding of finance and economics to advise individuals and organizations on investment decisions. Their work involves forecasting trends in the economy as well as analyzing data to assess the performance of companies. Machine learning is increasingly used by Financial Analysts to help with this analysis. This course will help you develop the skills you need to become a successful Financial Analyst in the financial services industry.

See salaries and explore the career path for Financial Analyst

Risk Analyst

A Risk Analyst assesses and manages financial risk for companies. They use various techniques to identify and measure risk, and then develop strategies to mitigate those risks. Machine learning is increasingly used by Risk Analysts to automate and improve this process. This course can help you build the skills you need to become a successful Risk Analyst in the financial services industry.

See salaries and explore the career path for Risk Analyst

Product Manager

A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring a product to market. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Product Manager in the financial services industry.

See salaries and explore the career path for Product Manager

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. They work on a variety of projects, including developing new models, improving existing models, and integrating models into production systems. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Machine Learning Engineer in the financial services industry.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software applications. They work on a variety of projects, including developing new features, fixing bugs, and improving performance. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Software Engineer in the financial services industry.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, cleans, and analyzes data to help businesses make informed decisions. They work on a variety of projects, including developing reports, creating visualizations, and identifying trends. Financial services companies are increasingly using machine learning to automate and improve this process. This course can help you build the skills you need to become a Data Analyst in the financial services industry.

See salaries and explore the career path for Data Analyst

Investment Analyst

An Investment Analyst researches and analyzes investment opportunities for individuals and organizations. They make recommendations on which investments to buy, sell, or hold. Machine learning is increasingly used by Investment Analysts to help with this analysis. This course will help you develop the skills you need to become a successful Investment Analyst in the financial services industry.

See salaries and explore the career path for Investment Analyst

Business Analyst

A Business Analyst analyzes business processes and systems to identify areas for improvement. They work with stakeholders to develop and implement solutions that improve efficiency and effectiveness. Financial services companies are increasingly using machine learning to automate and improve their business processes. This course can help you build the skills you need to become a successful Business Analyst in the financial services industry.

See salaries and explore the career path for Business Analyst

Regulatory Affairs Specialist

A Regulatory Affairs Specialist ensures that a company complies with all applicable regulations. They work with a variety of departments to develop and implement regulatory compliance policies and procedures. Machine learning is increasingly used by Regulatory Affairs Specialists to automate and improve this process. This course may help you build the skills you need to become a successful Regulatory Affairs Specialist in the financial services industry.

See salaries and explore the career path for Regulatory Affairs Specialist

Financial Advisor

A Financial Advisor provides financial advice to individuals and organizations. They help clients with a variety of financial planning needs, such as retirement planning, investment management, and estate planning. Machine learning is increasingly used by Financial Advisors to help with this analysis. This course may help you develop the skills you need to become a successful Financial Advisor in the financial services industry.

See salaries and explore the career path for Financial Advisor

Fraud Investigator

A Fraud Investigator investigates cases of fraud and embezzlement. They work with law enforcement to identify and apprehend fraudsters. Machine learning is increasingly used by Fraud Investigators to automate and improve this process. This course may help you build the skills you need to become a successful Fraud Investigator in the financial services industry.

See salaries and explore the career path for Fraud Investigator

Compliance Officer

A Compliance Officer ensures that a company complies with all applicable laws and regulations. They work with a variety of departments to develop and implement compliance policies and procedures. Machine learning is increasingly used by Compliance Officers to automate and improve this process. This course may help you build the skills you need to become a successful Compliance Officer in the financial services industry.

See salaries and explore the career path for Compliance Officer

Actuary

An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They work on a variety of projects, including pricing insurance policies, developing pension plans, and managing financial risk. Machine learning is increasingly used by Actuaries to automate and improve this process. This course may help you build the skills you need to become a successful Actuary in the financial services industry.

See salaries and explore the career path for Actuary