The finance sector, including banking institutions, trading companies, and fintech companies, is progressively implementing machine algorithms to automate tedious, time-consuming tasks and provide a much more streamlined and customized consumer experience. Fraud detection, risk analysis, and stock forecasting are just some of the ways fintech organizations employ machine learning.
The global market for machine learning in banking will likely reach $21,270.46 million by 2031, growing at a CAGR of 32.2% from 2022 to 2031.
More automation increases the need for machine learning expertise, thereby increasing the demand for machine learning professionals. But where can one acquire such knowledge and expertise? Simple: Start working on some unique, cutting-edge, industry-level projects. The best way to hone your machine learning skills is to work on fintech machine learning projects. For instance, a project will help you analyze models for financial data science tasks like credit card fraud analysis, or you can explore the ins and outs of user behavior analysis.
Here are some of the top machine learning projects in finance that you must try.
Employing a long short-term memory neural network (LSTM) for time series forecasting is one of the best ways to forecast stock prices. Download stock price data from Yahoo Finance, prepare the dataframes for neural network libraries, train the neural network model, and then backtest it using past stock price data. For this project, you can use LSTM and multilayer perceptron neural networks.
Source Code: Stock Price Prediction
This is one of the most popular machine learning projects in the finance sector. For this project, you can apply a hybrid strategy that extends the feature set of a fraud detection classifier by using unsupervised outlier scores. To build the classification model, you can leverage machine learning classifiers like Support Vector, Random Forest, etc. This project can be created on a Jupyter notebook using Python 3+. Use the Kaggle credit card fraud detection dataset for this project.
Source Code: Credit Card Fraud Detection
This project aims to forecast a client's repayment ability so that financial institutions can expand financial inclusion for the unbanked population. Use any loan prediction dataset from Kaggle to work on this project. You can build your classification model using machine learning algorithms such as Decision trees, Random Forests, Logistic Regression, etc.
Source Code: Loan Approval Prediction
Customer churn occurs when a customer discontinues using a brand and paying for the services of a certain organization. It is one of the most useful applications of machine learning in finance. For this project, you can use the tree-based ensemble method and implement the Random Forest model to predict if a customer is likely to churn and deploy the model using the Flask web app.
Source Code: Customer Churn Prediction
The last and most interesting project idea is credit scoring and risk analysis. The credit scoring system uses numerical statistical approaches to evaluate a person's creditworthiness and credit risks. This project aims to predict borrowers' credit scores using logistic regression and propose a threshold cut-off. AUC score and KS-Statistic are other relevant evaluation metrics you can use for this project.
Source Code: Credit Scoring and Analysis
You can also check out platforms like GitHub and Kaggle for more project ideas around machine learning in finance.
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