Credit score analytics refers to the use of statistical and machine learning techniques to analyze credit data and assess an individual’s creditworthiness. In Nigeria, this approach is utilized by lenders, credit bureaus, and regulators to facilitate lending decisions, generate credit reports, and regulate lending practices. This process involves the analysis of various factors such as payment history, credit utilization, length of credit history, types of credit used, and recent credit inquiries. There is an increasing focus on creating more inclusive and ethical credit scoring systems that incorporate alternative data sources and socioeconomic factors.
One of the major issues with credit score analysis in Nigeria is the dependence on conventional credit scoring methods, which may not be impartial or equitable to all borrowers. These models might not consider alternative data sources and socioeconomic factors, which could result in prejudiced lending practices. By conducting an in-depth analysis and leveraging advanced techniques such as artificial intelligence, there is a possibility of developing more comprehensive and ethical credit scoring systems that can enhance the precision and dependability of credit scoring in Nigeria.
Research and Data Collection
Data Collection and Domain understanding.
Exploratory Data Analysis
Feature extraction and dashboard visualization.
Data Analysis, Project Management, Features Engineering, Analysis Reporting