A framework for analysing financial products in personalized contexts

LendingClub API interface

Python package designed to provide a lightweight and flexible tool to perform mortgage/loan calculations.

This project aims to predict credit risk for individuals applying for loans, classifying whether they will default based on features such as age, income, employment length, loan amount, interest rate, percentage of income, credit length, home ownership, and loan intent.

Example contract of ruler protocol impemented on Neo N3 blockchain. Please be patient with RPC testing. Compiler: neo3-boa v0.8.2. Python 3.8 recommended for tests based on neo3vm.

This repository contains the code and scripts to reproduce the results from our paper "HARE: Human-in-the-Loop Algorithmic Recourse", accepted at TMLR 2025.

API to provide optimal payment allocation for credit cards - optimal means less accrued interest rate

Loan management in odoo 10

Brief tutorial with code where you can automatically create a dictionary with ~10k German loan words for import into espeak-ng as additional phonemic improvement or extension. This is, for instance, useful with Text-to-Speech (TTS) tasks in order to improve preprocessing.

The Loan Approval Prediction application is a web-based tool that utilizes machine learning algorithms to assess the likelihood of loan approval based on user-provided financial information.

Calculates annuity and differential payments.

Proof of concept unbiased loan calculator.

Malaysia Car Loan Calculator

Bank Account Management System with Loan Feature

This project analyzes a bank's loan application data to uncover patterns in approvals, income levels, risk profiles, and demographics. It uses Python for processing, MySQL for queries, and Power BI for interactive dashboards.

This program is mainly used to figure out loan schedules for clients and can be used both by clients and loan providers to both show and save a document that has the loan schedule on it.

Automate the loan eligibility process by understanding the relation between the collected information and the chance of paying back the loan. More specifically, fitting a machine learning model for which given information about the application the model predicts whether the corresponding applicant will pay the loan or not.

This project aims to develop an AI/ML model to predict loan repayment failure using a historical dataset of borrowers, their features, and loan characteristics. The problem is significant as it can help financial institutions identify potential defaulters and take preventive measures to minimize losses.

Gestor de Biblioteca hecho en Python como Trabajo Práctico Final del Curso de Python Intermedio.