The buzz around AI in finance is undeniable as it captivates the attention of both professionals and students who are eager to expand on their knowledge of AI and Finance, but with so many courses out there, finding the right one can be a daunting and time-consuming task.
While there are many courses, discovering a program that makes AI accessible to learners at all levels can be a real challenge.
Fortunately, we’ve simplified the search for you. In this blog, we’ve explained and compared some of the AI in Finance courses available today. This list includes a course from CFTE aimed at Financial Institutions, Corporates, Industry Associations who have foundation level knowledge of the topic, there are also courses from many top universities such as: UCL, NYU, Texas McCombs and Montreal. However these courses are an intermediatory level and targeted to people with technical skills such as Python. State Bank of India and ACCA are also on this list but there course is also an intermediate level.
Our goal is to help you easily find the course that will enhance your knowledge about AI and how it will impact finance, no matter your technical background.
1. CFTE's AI in Finance
Features
Target Audience
Everyone from Senior Leaders and Executive Strategy to Risk Management and Security Teams
Format
Self-paced, fully online
Level
Foundational
Time to Complete
4 courses and 12 hours of expert content
Add-Ons
Live Sessions
Instructors
1. Huy Nguyen Trieu, Co Founder of CFTE
2. Ayesha Khanna, Founder and CEO of ADDO AI
3. Philip Watson, Head of The Global Investment Lab at CITI
4. Stephan Murur, Former Global CTO at UBS
5. Jon Ng, General Manager at New World Development Company Limited
6. Winnie Cheng, Director of the AI lab at PWC
7. Shameek Kundu, Head of Financial Services & Chief Strategy Officer at Truera
8. Jean-Philippe Desbiolles, Global VP for Data, Cognitive & AI at IBM
9. Siew Kai Choy, Director and Board Member at Factset
10. Ramneek Gupta, Founder & Managing Partner at Pruven Capital
11. David Hardoon, Senior Advisor for AI & Data at Union Bank
12. Frank Chen, Senior Software Engineer at Google Research
13. Erkin Adylov, CEO & Founder of Behavox
14. Cao Yang, Research Director at SenseTime
15. Gurjeet Singh, Founder & CEO of Thread Robotics
16. Janet Yuen, Global Head of Conversational Banking at HSBC
17. Marcin Detyniecki, Head of R&D at AXA
18. Randi Hindi, CEO of ZAMA
19. Diane Nolan, Managing Director at Accenture
20. Catherine Havasi, Co Founder and Board Advisor of Luminoso
21. Martin Markiewicz, Co founder & CEO of Silent Eight
22. Anant Bhardwaj, Founder & CEO of Instabase
23. Shonali Krishnaswamy, Chief Technology Officer at AIDA
24. Ned Philips, Founder & CEO of Bambu
Topics
1. Foundations of Artificial Intelligence: Applications and Trends
2. AI Technologies: Machine Learning Techniques, NLP and Recommendation Engines
3. Implementing AI in an Enterprise: Technology Skillsets and Regulation
4. Applications of AI in Finance: Use Cases from the Industry
Use Cases
25+ use cases from disruptive companies
Accreditation
IBF and CBD Accredited
Pricing
GBP 600 for a one off payment or GBP 200 in three installments
2. NYU's Guided Tour of Machine Learning in Finance
This course aims to provide an introductory and broad overview of the field of ML with the focus on applications on Finance. While this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialisation Machine Learning and Reinforcement Learning in Finance.
The goal of Guided Tour of Machine Learning in Finance course is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.
The course is designed for practitioners working at financial institutions such as banks, asset management firms or hedge funds, individuals interested in applications of ML for personal day trading, as well as current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics or Engineering.
Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Features
Target Audience
Everyone from executives and senior leaders to professionals in managerial positions
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
24 Hours
Add-Ons
Live Sessions
Instructors
Igor Halperin, Former Research Professor of Financial Machine Learning at NYU Tandon School of Engineering
Topics
1. Artificial Intelligence and Machine Learning
2. Mathematical Foundations of Machine Learning
3. Introduction to Supervised Learning
4. Supervised Learning in Finance
Use Cases
Linear Regression in TensorFlow, Neural Networks, Fundamental Analysis
Accreditation
NYU Accredited
Pricing
Free for 7 day trial then 38 GBP per month
3. UTAustinX Fintech's AI & Machine Learning in the Financial Industry
This is the third in a series of courses on financial technology, this course provides an overview of machine learning applications in finance.
The course covers equity crowdfunding and P2P or marketplace lending, what is AI, and the attempts to create machines and algorithms that can replicate, mimic, and replace human activities, as well as quantitative investments, robo-advising, and finance in social platforms.
Financial professionals are often required or encouraged to continue their education to practice their profession. For some associations, this program may be used for Continuing Education Credits. Please check with your local or national organisation if the program qualifies.
Features
Target Audience
Mainly Finance professionals
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
4 weeks, 5–6 hours per week
Add-Ons
4 Quizzes
Instructors
Cesare Fracassi, Associate Professor of Finance at the University of Texas
Topics
1. P2P Crowdfunding
2. Overview of Artificial Intelligence
3. Non-linear Machine Learning Models
4. Quantitative Investing
Use Cases
Crowdfunding, Robo-advising, Financial social platform, and the Democratisation of trading and investments
Accreditation
UOT Accredited
Pricing
GBP 617
4. ACCA's FinTech for Finance and Business Leaders
This course is part of the FinTech for finance and business leaders professional certificate program. It will provide a view of what lies under the surface of a machine learning output, help to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning.
Digital finance knowledge and skills are essential components of the technology transformation as business becomes increasingly customer focused and having the skills to understand how these technologies are deployed and integrated into a customer centric business strategy is essential.
Features
Target Audience
For professional accountants working in all sectors, from financial services to healthcare and from banks to startups
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
3 months, 3 – 5 hours per week
Add-Ons
3 skill-building courses
Instructors
1. John Sandall, CEO and Principal Data Scientist
at Coefficient
2. Narayanan Vaidyanathan, Head of Business Insights
at ACCA
3. Tze Chem, Executive Director at International bank
4. Clive Webb, Head of Business Management
at ACCA
5. Mike Hughes, Company Director at Prism RA
Topics
1. P2P Crowdfunding
2. Overview of Artificial Intelligence
3. Non-linear Machine Learning Models
4. Quantitative Investing
Use Cases
Excel, Robo-advising, Financial social platform, Python, Big Data
Accreditation
ACCA Accredited
Pricing
GBP 417
5. UMontrealX's Machine Learning Use Cases in Finance
In this course, you will first be presented a review of some of the applications of machine learning and deep learning. You will then be able to illustrate their use in financial applications through concrete examples that we have seen spark interest in the industry. The examples in the course will explain how AI can add value through ad hoc construction of architectures rather than a simple exercise of replacing classical models with more complex ones, such as multi-layer networks.
The course is primarily intended for industry professionals and academics with intermediate knowledge of mathematics and programming, ideally Python. Graduate students in data science and quantitative finance may also find this content instructive and compelling. The content of this course will also be of great use to whomever uses or is interested in AI. Previous experience in the financial industry is not necessary to follow this course.
Features
Target Audience
Professionals and academics with a background in mathematics and programming
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
3 months, 3 – 5 hours per week
Add-Ons
Information extraction and ESG metrics
Instructors
1. Manuel Morales, Ph.D. Associate Professor at Department of Mathematics and Statistics, Université de Montréal
2. Rheia Khalaf, M.Sc. Director, Collaborative Research & Partnerships at IVADO
3. Alexandre Nguyen, M. Sc., Instructor, at Fin-ML Network
4.Frederik Wenkel, Ph.D. Candidate at University of Montreal
5. Elham Kheradmand, Postdoctoral Fellow at University of Montreal
6. Marie-Ève Malette, M.Sc., Director of Development and Partnerships at JACOBB – Centre d’intelligence artificielle appliquée
Topics
Module 1 – Introduction and Background
Module 2 – Reminder Machine Learning and Deep Learning
Module 3 – GNN in Finance
Module 4 – ESG Evaluation
Module 5 – Portfolio Design using Reinforcement Learning
Module 6 – Conclusion
Use Cases
Finance, Information Extraction, Machine Learning, Artificial Intelligence, Research, Bitcoin, Deep Learning, Basic Math, Mathematical Finance, Computer Vision, Natural Language Processing, Data Science, Financial Services, Financial Market
Accreditation
UOM Accredited
Pricing
GBP 147
6. State Bank of India's Introduction to Machine Learning for Finance
This course will explore the foundational concepts of machine learning in banking, dive into data analysis techniques tailored for financial data, and learn to apply supervised and unsupervised learning methods to real-world banking and finance challenges. Discover how Natural Language Processing (NLP) is changing the way banks interact with customers and gain essential skills in time series analysis and forecasting for financial markets.
The course also covers model evaluation, interpretability, and ethical considerations in AI, ensuring you’re well-equipped to navigate the unique challenges of the banking industry. Learn from use cases of successful machine learning integration in banks and gain practical insights to drive innovation in financial institutions.
Whether you’re a beginner or an experienced professional, this course offers valuable knowledge and insights that can enhance your career prospects in banking and finance domain.
Features
Target Audience
Beginner or an experienced professional with an interest in banking and AI
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
4 weeks, 2–4 hours per week
Add-Ons
Information extraction and ESG metrics
Instructors
- Shaji Neelakandan, AGM, Faculty at State Bank of India
- Satish Kumar S, AGM Faculty at State Bank of India
Topics
Module 1 – Introduction to Machine Learning Fundamentals
Module 2 – Different Learning Models for Banking Applications
Module 3 – Natural Language Processing (NLP) & Time Series Analysis in Finance
Module 4 – Model Evaluation, Interpretability, and Ethical Considerations
Use Cases
Interest rate forecasting models, ethical challenges, Exploratory data analysis (EDA) techniques for banking datasets
Accreditation
SBI Accredited
Pricing
GBP 147
7. UCL's Introduction to Machine Learning in Quantitative Finance
On this course, you’ll be presented an overview of supervised learning, as well as linear and non-linear regression with regularisation and classification. This will enable you to learn other new supervised learning algorithms in a systematic manner. The course will also introduce you to neural networks and understand how deep learning can be used to analyse large datasets and create accurate financial predictions. At the end of the course, you’ll put your learning into practice by tackling an empirical financial data problem using machine learning end-to-end.
Features
Target Audience
Anyone interested in machine learning and quantitative finance with a basic background in probability and Python programming.
Format
Self-paced, fully online
Level
Intermediate
Time to Complete
4 weeks, 3 hours per week
Add-Ons
Tests to validate your learning and includes articles, videos, peer reviews and quizzes
Instructors
1. Hao Ni, Professor of Mathematics at University College London.
2. Camilo Garcia Trillos, Lecturer at the Department of Mathematics at UCL .
3. Alex Tse, lecturer at the Financial Mathematics group of University College London.
4. Weiguan Wang, lecturer in Finance at Shanghai University, Ph.D. in Mathematics from the London School of Economics.
Topics
Week 1 – Introduction to the course
Week 2 – Supervised Learning in financial applications
Week 3 – Learning derivative pricing via Deep Neural Networks
Week 4 – Limit order book prediction via Recurrent Neural Networks
Use Cases
Python, linear regression and neural networks models
Accreditation
UCL Accredited
Pricing
GBP 250
