AI Fundamentals in Financial Services
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AI Fundamentals in Financial Services
This course is part of AI in Financial Services: Foundations through future trends Specialization
Instructor: Martin Schmalz
7,870 already enrolled
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What you'll learn
Understand the core technologies behind AI and the central role of data in training AI systems.
Explore how AI is applied in financial services through use cases such as fraud detection, credit scoring, customer service, and algorithmic trading.
Critically assess the risks, limitations, and ethical considerations involved in using AI within the financial sector.
Skills you'll gain
- Data Ethics
- Algorithms
- AI Personalization
- Artificial Intelligence
- FinTech
- Financial Regulation
- Deep Learning
- Financial Data
- Data Management
- Reinforcement Learning
- Natural Language Processing
- Credit Risk
- Banking
- Machine Learning
- Financial Services
- Decision Intelligence
- Unsupervised Learning
- Responsible AI
- Machine Learning Methods
Tools you'll learn
Details to know
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- Gain a foundational understanding of a subject or tool
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- Earn a shareable career certificate
There are 4 modules in this course
Artificial Intelligence (AI) is rapidly reshaping the financial services landscape. From fraud detection and algorithmic trading to customer service chatbots and credit scoring, AI is at the heart of a new era in finance. This course is designed to give you a clear, practical understanding of how AI works, what it enables, and how it’s transforming the way financial institutions operate.
Through real-world examples, case studies, and engaging learning activities, you’ll gain insights into the key technologies that make AI possible, including machine learning, deep learning, and natural language processing, and see how they are applied across core functions in banking, fintech, and asset management. You’ll also explore how data drives these systems, the different learning methods AI uses, and the implications for strategy, governance, and ethics. Whether you’re a financial professional, policymaker, or simply curious about the future of finance, this course will equip you with the knowledge and confidence to engage in AI-related conversations and decision-making. No programming background is required, just an interest in how technology is shaping the future of financial services. By the end of the course, you will be able to: • Understand the foundational technologies that underpin Artificial Intelligence (AI), including Machine Learning, Natural Language Processing, and Deep Learning. • Explore the central role of data in powering AI systems, and the key learning methods used to train them. • Identify how AI is applied in financial services, including use cases such as fraud detection, credit scoring, customer service, and algorithmic trading. • Critically evaluate the risks, limitations, and ethical challenges associated with deploying AI in financial services. This course is the first in the AI in Financial Services: Foundations through Future Trends specialization. It provides the essential groundwork for understanding how AI works and why it matters in finance. After completing this course, we recommend continuing with 'Designing the Future of Finance' and 'Open Data and Intelligent Finance' courses to explore how AI intersects with Open Finance, embedded systems, and intelligent, ethical financial innovation.
This module provides a foundational introduction to Artificial Intelligence and its transformative role in financial services. It also offers an overview of the course structure, highlighting key topics and how each module will build your understanding. You'll explore key AI technologies: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotics, and Expert Systems. These insights will prepare you to recognise how AI is reshaping financial operations, services, and decision-making processes.
What's included
4 videos7 readings1 assignment4 plugins
4 videos•Total 6 minutes
- Welcome from Professor Martin Schmalz•2 minutes
- What is AI?•2 minutes
- How NLP powers language-based technology•1 minute
- How Expert Systems replicate human reasoning•1 minute
7 readings•Total 96 minutes
- Your learning journey •15 minutes
- Before you begin: reflect on your learning goals •10 minutes
- Important note about course communication•1 minute
- Introducing AI in Financial Services•10 minutes
- What is Natural Language Processing (NLP)•30 minutes
- What are Expert Systems•20 minutes
- Conclusion: Inside the AI Toolbox•10 minutes
1 assignment•Total 20 minutes
- Module quiz•20 minutes
4 plugins•Total 60 minutes
- What is Machine Learning•15 minutes
- What is Deep Learning•15 minutes
- What is Computer Vision•15 minutes
- What is Robotics and Robotic Process Automation (RPA)•15 minutes
This module explores the vital role of data in Artificial Intelligence and the different learning methods that AI systems use to generate insights and predictions. You’ll examine key data types, the characteristics of big data, and the core machine learning paradigms used in financial applications.
What's included
6 videos9 readings1 assignment4 plugins
6 videos•Total 10 minutes
- Data: the fuel that powers AI •2 minutes
- Challenges of working with large-scale data in finance•1 minute
- Comparing traditional computing and AI•3 minutes
- Supervised Learning: a loan repayment example•1 minute
- Unsupervised Learning: a segmentation example•1 minute
- Reinforcement Learning: a customer engagement example•1 minute
9 readings•Total 100 minutes
- Introduction•10 minutes
- Conclusion and reflection•20 minutes
- Traditional computing approaches •15 minutes
- Artificial Intelligence approaches •10 minutes
- Comparing traditional computing and AI - summary•10 minutes
- Conclusion and reflection•20 minutes
- Supervised Learning•5 minutes
- Unsupervised Learning •5 minutes
- Reinforcement Learning•5 minutes
1 assignment•Total 20 minutes
- Module quiz•20 minutes
4 plugins•Total 70 minutes
- Data Paradigms and Data Types •20 minutes
- Challenges of collecting, managing and using large data sets •15 minutes
- Choose the right computing approach •15 minutes
- Which type of learning is it? •20 minutes
This module demonstrates how AI is applied across four core areas of financial services: fraud detection, credit scoring, customer service, and algorithmic trading. You’ll explore real-world use cases to understand how AI adds value, the models and data behind it, and the challenges institutions face when deploying these technologies.
What's included
5 videos9 readings1 assignment4 plugins
5 videos•Total 6 minutes
- Applying AI in Financial Services•2 minutes
- How AI enhances fraud detection•1 minute
- Why credit scoring needs a rethink•1 minute
- Personalisation and the future of customer service•1 minute
- AI at speed - the rise of algorithmic trading•1 minute
9 readings•Total 175 minutes
- Reflect on your own experience •15 minutes
- How AI-based fraud detection works •20 minutes
- Real-world examples of AI-based fraud detection •20 minutes
- How AI models assess credit risk •20 minutes
- Real-world examples of AI credit scoring •20 minutes
- How chatbots are driving increased personalisation •20 minutes
- Real-world examples of AI driven personalisation in finance •20 minutes
- How Algorithmic Trading works •20 minutes
- Real-world examples of AI in Algorithmic Trading •20 minutes
1 assignment•Total 20 minutes
- Module quiz•20 minutes
4 plugins•Total 60 minutes
- Challenges and considerations for AI-based fraud detection •15 minutes
- Challenges and considerations for AI-based credit scoring •15 minutes
- Challenges and considerations for chatbots in finance •15 minutes
- Challenges and considerations for AI in Algorithmic Trading •15 minutes
In this final module, you’ll consolidate your learning and apply your knowledge through a peer-reviewed written assignment. You’ll reflect on key concepts, revisit course highlights, and explore the real-world implications of AI in financial services.
What's included
1 video4 readings1 assignment1 peer review
1 video•Total 2 minutes
- Summary•2 minutes
4 readings•Total 55 minutes
- Key takeaways and reflection•15 minutes
- Bibliography and further reading•10 minutes
- Written assignment information•20 minutes
- Next steps•10 minutes
1 assignment•Total 40 minutes
- Course quiz•40 minutes
1 peer review•Total 120 minutes
- Written assignment submission•120 minutes
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Saïd Business School, University of Oxford
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Reviewed on Jan 4, 2026
Very good introduction for AI in Financial services. Excellent way of teaching and quizzes
Reviewed on Jun 1, 2026
Excellent beginners course in AI and its applications in Finance.
Reviewed on Dec 6, 2025
I really enjoyed this course, interesting yet knowledgeable
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