In countries where the financial markets are not very deep, people have difficulties getting loans easily. Due to insufficient or nonexistent credit history, they cannot get the loan easily. Home Credit is a financial company that strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. Home Credit B.V. is an international non-bank financial institution founded in 1997 in the Czech Republic and headquartered in the Netherlands. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data - including telco and transactional information - to predict their clients' repayment abilities. The company operates in 10 countries and focuses on installment lending primarily to people with little or no credit history. The Home Credit loan can be obtained from throughout 342.700 partnering retail shops and shopping malls in ten world markets as well as at the Home Credit's own online marketplaces (Russia, China, Philippines, and Vietnam). Home Credit is also presented online in its own mobile application or in third-party online stores. In selected markets you can pay with an "HC Pay", a QR code-based revolving credit, or with Home Credit credit or shopping cards. Home Credit finances both the purchase of consumer goods through a cash-less loan transaction or other commodities, such as language courses, travel, etc. through cash loans. The loans are always issued in the domestic currency of the market in which the brand operates.
The Problem
In this project, we try to contribute using Home Credit Data to forecast better for a credit application whether it will be paid or not. HC open a compatition on Kaggle 3 years ago. It relaesed 7 dataframes. You can find more details on Kaggle page. https://www.kaggle.com/c/home-credit-default-risk