Professor Lilia Maliar (GC/Economics) was awarded a $308,000, three-year grant from the National Science Foundation (NSF) for her research on using artificial intelligence to analyze complex and highly dimensional economic models.

Her research has interesting applications in FinTech and beyond.

Maliar is developing a deep learning framework that makes it possible to cast a broad class of economic models into a form that is suitable for intelligent machines — an approach that opens a new path in economic research. “AI has many impressive applications like image and speech recognition, composing music, and playing chess and Go”

Deep learning has the potential to answer questions which have been unanswered or unable to be predicted until now.

There are a variety of other machine learning algorithms, which can be used to find insights from data.

The financial sector has been using machine learning techniques for a long time in order to gain business growth through higher profit. Credit card fraud detection, stock market prediction, among others, are some of the popular machine learning approaches in this sector, which the companies have actively adopted to streamline their business operations.

Here’s an interesting read about predicting banking crises with AI.

In this article, we will discuss a deep learning technique — deep neural network — that can be deployed for predicting banks’ crisis. This experiment is based on the African economic, banking and systemic crisis data where inflation, currency crisis and bank crisis of 13 African countries between 1860 to 2014 is given. By predicting through a deep learning model, we will see that this model gives a high accuracy in this task.

FinTech is gaining more and more traction as a recognized industry.

The University of Toronto has launched a FinTech boot camp and applications are open.

The course, which is 24 weeks long and is composed of two night classes during the week and one class on the weekends.

“We felt this was another opportunity for us to move forward and be able to provide the skills that employers are looking for in this marketplace,” said MacDonald in an interview. “If you look at Toronto, it’s really becoming one of the fastest-growing financial tech centers in the world. By introducing the FinTech boot camp, we’re hoping to respond to some of that and capitalize on the evolving marketplace in the GTA.”

In this video, watch Siraj Raval build a cryptocurrency trading bot called GradientTrader, and he shows you the tools used to build it.It uses a graphical interface that lets you back-test on historical data, simulate paper trading, and implement a custom trading strategy for the real markets. The technique I used was a cutting edge Deep Reinforcement Learning strategy called Multi Agent Actor Critic.

In this follow up video to “How To Build An AI Startup With PyTorch,” the great Siraj Raval explores how to make money with TensorFlow 2.0.

From the video description:

I’ve built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving + Flask to create a simple web app around it. I’ll explain what the important parts you should know in Tensorflow 2.0 are, then I’ll guide you through my code & thought process of building an AI startup using it. Enjoy!

By the way, the code for this video is available on GitHub.

In this documentary from 2010, vpro explores the people at the intersection of advanced mathematics and finance: quants. Quants are the math wizards and computer programmers in the engine room of our global financial system who designed the financial products that almost crashed Wall street ten years ago. The credit crunch showed how the global financial system became increasingly dependent on mathematical models trying to quantify human/economic behavior.