Credit risk analysis for credit card applicants

Application to finance

The aim is to understand which are the key factors for a certain level of credit risk to occur. In addition, some ML models capable to predict the credit risk level for a company in an year - given past years data - have been built and compared.

Predicting how much loan will be approved

Reverse engineering of the FICO algorithm

The project involved developing a credit risk default model on Indian companies using the performance data of several companies to predict whether a company is going to default on upcoming loan payments.

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

In 2019, more than 19 million Americans had at least one unsecured personal loan. Personal lending is growing at an extremely fast rate, and FinTech firms need to go through an organize large amounts of data in order to optimize lending. Python will be used to evaluate several machine learning models to predict credit risk. Algorithms such as RandomOverSampler, SMOTE, and RandomForest will be used to analyze credit card datasets from a company (LendingClub) and use linear regression to both sample and predict data. This data can be used to determine the number of people who are predicted to be at high/low risk for credit risk.

Credit Risk Analysis - PD Modelling

Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can identify the creditworthiness of borrowers.

Bank-style Credit Risk Scorecard using Logistic Regression, IFRS-9 Expected Credit Loss, and an Interactive Streamlit Risk Dashboard for loan default prediction.

This project focuses on credit risk analysis using SQL, Python, and Power BI. We built an end-to-end pipeline that starts with raw loan applicant data and ends with an interactive dashboard for stakeholders to monitor loan defaults.

A data analysis project to classify whether an applicant is capable of paying a home loan by using 4 machine learning models (Logistic Regression, SVM, Random Forest and LGBM) and 1 deep learning model (DeepFM). We also drew some insights from the best model that can be useful for analysts in bank.

Machine Learning pipelines are deployed to accomplish the objective of credit risk analysis.

This project predicts credit risk based on user inputs and categorize loan applications into Poor, Average, Good, and Excellent categories. The project mirrors the CIBIL scoring system.

No description provided.

Credit risk analysis using scikit-learn and imbalanced-learn.

A data generator for credit risk data. The generator creates a dataset with dependent and independent variables.

Markov-chain simulation of corporate credit rating migrations, visualizing rating trajectories and default risk.

Financial analytics dashboard for credit risk assessment, loan decision intelligence, and portfolio risk monitoring.

Credit risk analysis determines a borrower's ability to meet debt obligations and the lender's aim when advancing credit. The goal is to identify patterns that indicate if a person is unlikely to repay the loan or labeled as a bad risk through automated machine learning algorithms.

All Main Projects

Credit risk modeling | EDA | Python | SQL | Model Validation and Tuning | Classifier

This repository contains projects related to data mining. Data mining finds valuable information hidden in large volumes of data and it is the analysis of data and the use of software techniques for finding patterns and regularities in sets of data.

Financial Risk Analysis identifies and mitigates threats to financial stability, including market volatility, credit risk, and operational failures, using data models and regulatory strategies to protect investments and drive growth.

This repository contains python code from scratch to develop the credit risk model for loan portfolio

Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?

Credit Risk Analysis to predict loan defaults using business metrics like approval rate, default capture rate, precision, and AUC to optimize loan approvals and reduce financial risks.

ML course project

Machine Learning project to classify loan status (default vs non-default) using Decision Tree, KNN, and Naive Bayes