Local Chapter Benin City, Nigeria Chapter
Coordinated by,
Status: Completed
Project Duration: 14 May 2023 - 17 Jun 2023
Open Source resources available from this project
Project background.
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.
The problem.
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.
Project goals.
Project plan.
Week 1
Research and Data Collection
Week 2
Data Collection and Domain understanding.
Week 3
Exploratory Data Analysis
Week 4
Data preprocessing
Week 5
Feature extraction and dashboard visualization.
Learning outcomes.
Data Analysis, Project Management, Features Engineering, Analysis Reporting
