Applied AI in Fintech: Predictive Risk & RegTech Systems

Master Machine Learning, Neural Networks, and Generative Data to automate risk assessment and optimize compliance

What you’ll learn
✓ Preprocess complex financial data (e.g., German Credit Dataset) using R and tidyverse packages like dplyr to prepare for predictive modeling
✓ Architect and train Single-Layer and Multi-Layer Perceptrons (Neural Networks) using sigmoid and ReLU activation functions for financial prediction.
✓ Analyze the difference between exogenous and endogenous systemic risks, and understand how algorithmic trading can amplify prosyclical risk.
✓ Distinguish between RegTech and SupTech, evaluating how AI and ML are leveraged by financial supervisors and institutions for regulatory compliance.
✓ Implement Machine Learning models (SVMs, Decision Trees, Multivariate Regression) to accurately forecast credit ratings and default probabilities.

Requirements
● Basic familiarity with programming concepts, preferably in R (as we utilize data frames, tibbles, and dplyr for data manipulation).
● A foundational understanding of basic statistics (mean, variance, linear combinations, and dummy variables) is helpful but not strictly required.
● No prior Machine Learning or Deep Learning experience is necessary; we build the architecture of Neural Networks and predictive models from the ground up.

Description
Welcome to Applied AI in Fintech: Predictive Risk & RegTech Systems. If you want to understand how artificial intelligence and machine learning are actually used in the real world of finance today, you are in the right place.

In this course, we start by looking at why AI has become so important in finance. Financial decisions should never be based on human emotion. They need to be backed by hard facts, fast data processing, and objective rules. By learning how to use these modern algorithms, you can help financial institutions reduce costs, increase transaction speeds, and make much smarter lending and investment choices.

We believe the best way to learn is by doing. You will start by looking at real financial data, like the famous German Credit Dataset, to see how we clean and prepare information so a computer can understand it. From there, we will explore practical machine learning models. You will learn how to build systems that automatically predict credit default risk using simple regression and decision trees.

Once you understand the basics, we will dive into deep learning. You will see how artificial neural networks are built to mimic the human brain, allowing computers to find hidden patterns in massive amounts of financial data.

Finally, because the financial world is heavily regulated, we will look at the big picture. You will learn about RegTech and SupTech, which show how regulators use AI themselves. We will also discuss the ethical side of AI, and how relying too much on automated machines can sometimes cause systemic risks in the market.

By the end of this course, you will have a clear, practical understanding of how to build AI models for finance, and how to use them safely and ethically.

Who this course is for
■ Data scientists and programmers looking to specialize in technical financial risk modeling, predictive analytics, and RegTech/SupTech applications.
■ Financial analysts, risk managers, and quantitative economists wanting to transition from traditional statistics to modern Machine Learning and Deep Learning frameworks.

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