Data science and machine learning have numerous applications in finance. Some of the most common ways fintech companies use machine learning include fraud detection, risk analysis, and stock predictions.
Fintech machine learning projects are the best way to gain hands-on experience with these techniques. For example, a project will help you compare models for financial data science tasks like credit card risk analysis, or you can learn the ins and outs of customer behavior analysis. Here are some of the best fintech machine learning projects you can try in various categories:
Machine learning projects are widely used in finance to make forecasts and market predictions. In particular, fintech professionals use ML and AI to make short-term stock predictions using three techniques:
1. Fundamental analysis - Analysis of a company’s performance
2. Technical analysis - Analyzing market trends using time-series analysis, exponential moving average, KNN, or decision trees.
3. Technological analysis - Machine-learning-based analysis with algorithms and techniques like neural networks or text mining.
This beginner fintech project requires you to make stock price predictions based on newspaper headlines. You can follow this tutorial, which includes the source code for the project.
Using sentiment analysis, you’ll make predictions about common stocks. Another option: You can re-create this project with the Sentiment Analysis for Financial News dataset on Kaggle, which includes financial news data for retail investors.
This stock market machine learning project is an excellent premier on performing time series analysis to predict stock prices short-term.
In particular, this tutorial teaches how to use Recurrent Neural Networks or Long Short Term Memory models to make short-term predictions. You can use the included dataset or Netflix stock price data from Yahoo! Finance for this project.
You’ll also find some helpful ideas in this tutorial for using Keras’ LSTM models for predicting Google stock prices. as in this tutorial for using Keras’ LSTM models for predicting Google stock prices.
This project uses the open-source Facebook Prophet model for time-series modeling. Prophet was developed for quick and accessible time-series modeling to make it possible for professionals to quickly productionalize and scale time-series models.
This tutorial from the Clever Programmer walks you through how to prepare TESLA stock data for Prophet. Also, you can see this Kaggle notebook for making predictions with Prophet.
Many fintech firms include a take-home challenge during the interview process. Traditionally, fintech take-homes are condensed machine learning projects that require 3-6 hours to complete.
These challenges need you to perform market analysis, model, or make a prediction based on available data. You can practice with these finance data science take-homes:
This StepStone take-home challenge asks you to use unsupervised learning techniques to cluster customer inquiries related to loans and financial products.
StepStone is financial management and advising firm, and this challenge simulates a data science task you’d likely face on the job. Ultimately, you’re asked to provide reasoning for your chosen clustering algorithm.
This data engineering take-home from Invitae asks candidates to develop a model to monitor crypto prices and the conversation on Twitter about coins of interest.
The main objective of this Invitae data science challenge is to build a model that shows the historical correlation of Twitter sentiment to coin price, with a working code or a technical discussion of the work that needs to be done.
This Goodwater Capital take-home challenge provides a dataset of successful e-commerce businesses like Dollar Shave Club and Stitch Fix.
Your goal with this challenge is to identify patterns and characteristics that these “winners” share and which could then be used to pick successful up-and-coming e-commerce businesses.
In addition, the project asks you to build a 12-month sales forecast for the emerging e-commerce business, Brandless.
This Stripe take-home asks you to take a few flagship Stripe products dataset and create a short presentation about their performance.
The dataset includes information about product usage and the customer segments who use Stripe products. Some of the guiding questions for the assignment include:
Fintech companies widely use fraud analytics to detect and prevent fraud and perform risk analysis. Fraud and risk analysis machine learning projects allow you to practice working on data science classification projects to detect fraud or classifiers to gauge bankruptcy risk. Here are some fraud analytics projects to try:
This Kaggle competition includes a financial dataset with over 100,000 loan records. To complete the project, you must clean the data and build a model for predicting loan repayment or default.
Another option: You can use the Credit Risk Classification Dataset to construct a classifier to determine loan repayment. This dataset is smaller, which makes it an excellent choice for a beginner credit risk project.
This fraud detection project uses the Bank Note Authentication Dataset from UCI. The financial dataset features images of authentic and forged bank notes, and there are numerous approaches you can use.
For example, you could build a neural network to authenticate the images, or a tutorial on using logistic regression. You can also apply what you learn to other datasets, including the Forgery Image Dataset.
This more advanced project features a challenging large-scale dataset from the IEEE Computational Intelligence Society.
In this IEEE challenge, you’ll evaluate various models for detecting fraud in e-commerce payments using data from the Vesta Corporation. See this notebook for analyzing the split points used in decision trees.
The idea is that by analyzing split points, you can derive insights into what indicates fraud and help in smoothing and binning the data.
Market and customer analysis projects ask you to use machine learning and modeling to analyze customer behavior, market trends, or company performance. This analysis is often used to predict a business’s price changes or sales figures.
Customer Behavior Analysis Behavior analysis is commonly used in finance product development to determine core customer segments’ specific needs and concerns.
For this project, you’ll analyze a dataset of 2,000+ customers, use indicators like purchase type, marital status, age, and educational level and determine how these factors affect the amount spent.
This tutorial will also find some helpful ideas and tips for clustering segmentation with machine learning.
One typical finance machine learning project would be to make predictions about customers. Companies can use these predictions to personalize products or enhance fraud detection systems.
This project uses the Santander Value Prediction Dataset to predict customer transaction values. Because transaction value is a continuous variable, this is a regression problem. Taking numerous approaches would help; however, you might start with a simple linear regression algorithm.
In fintech and banking, bankruptcy prediction has long been a machine learning problem, and in that industry, numerous free fintech datasets can be used for this type of project.
Check out the Company Bankruptcy Prediction Dataset on Kaggle to get started. You can then perform EDA to define the correlation between attributes.
Ultimately, this is a classification problem, and you can test various classification algorithms, including Support Vector Machines and K-Nearest Neighbors.
We widely use machine learning to automate trading decisions and identify arbitrage opportunities. The most common machine learning techniques in trading include ensemble algorithms, Support Vector Machines, and Long Short Term Memory Networks.
Machine learning techniques can help you identify arbitrage opportunities in various markets, including stocks.
The idea of arbitrage is that an investor can buy stock in one market and sell it to another at a profit. One option would be to use regression or time-delay neural networks to identify these opportunities.
If you want to learn more about machine learning and arbitrage, see this guide, which includes tips for data management, feature engineering, and model training, and you can apply what you learn to a variety of investment opportunities.
Another resource to see would be The Best Python Packages for Algorithmic Trading.
This quick tutorial will teach you how to automate stock portfolio analysis using Python, including metrics like cumulative returns over time and incremental gains.
After following the guide, you’ll have created an in-depth Jupyter notebook, which you can use to evaluate your portfolio of active holdings. You’ll also gain experience using the Yahoo! Finance API for importing stock market data.