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In the new era of Finance field, by leveraging the power of data we are able to evaluate and find anomalies in different finance sub-sectors. Although these anomalies doesn't occur much but in this sensitive field, when they happen they put too much pressure on banks, credit companies, etc. One of the new and attractive fields which has been developed because of expanding data science in modern world, is MicroFinance.
Microfinance refers to the provision of financial services, such as loans, savings, and insurance, to individuals or small businesses who typically lack access to traditional banking services. These services are aimed at empowering low-income individuals and communities, especially in developing countries, by providing them with the means to start or expand businesses, invest in education, or cope with emergencies. Microfinance institutions (MFIs) typically offer small loans without collateral requirements.
In recent years, data science has emerged as a powerful tool to enhance the efficiency and effectiveness of microfinance operations. By leveraging advanced analytics and machine learning algorithms, MFIs can analyze vast amounts of data collected from loan applications, borrower profiles, repayment histories, and economic indicators to make informed decisions and manage risk more effectively. Specifically, data science can be leveraged to identify patterns and predict repayment behavior, including the likelihood of late payments or default.
This repository is an analysis on a dataset provided on kaggle website on this link. The aim of the provided notebook and codes are to find the probability of a delayed payment based on the provided data for each loan applicant. If you have any difficulties openning the notebook in github you can find it in KAGGLE
This repository is licensed under the MIT License. Feel free to use and modify the contents for educational and non-commercial purposes, with appropriate attribution.
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Disclaimer: This repository is for educational and informational purposes only. The information provided here does not constitute financial advice or recommendations. Users are encouraged to conduct their own research and consult with financial professionals before making any decisions based on the information provided.