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Description
We created this system for an African-based SMB that provides small loans to mostly the people without banks. The company asked for a simple streamlining system for loan approvals based on the data source that is more credible than the credit bureau’s data. This streamlined system allowed them to expand their business without worrying about the credibility of loan approval.
Challenge
• Creating a robust credit scoring system with the help of Machine Learning (ML) and Natural Language Processing (NLP) to help SMBs like our client as well as the financial institutions to process loans in real-time without much of a hassle and with minimum default rate. • The most challenging part of this project was to find a reliable data source for the client that is better than Credit Bureau data. • The second challenge we faced was defining the set of parameters for calculating the credit score in the system.
Solution
• Built a non-traditional data source using mobile device data extraction. • Extracted data like income, investments, expenses, mobile device types, installed apps, etc., using NLP. • Passed NLP-accumulated information to the credit score system and set parameters. • Introduced Machine Learning to automate credit score generation due to high data volume.