This project provides a comprehensive big data–driven analysis of loan applications, approvals, and financial risk patterns. By combining the Hadoop ecosystem for data processing with Power BI for interactive visualization, the study evaluates how demographic, economic, and geographic factors influence loan approvals across 600+ applicants.
- Cloudera Hadoop QuickStart VM: Hosting the Hadoop ecosystem and HDFS.
- HDFS (Hadoop Distributed File System): For storing and managing structured loan application data.
- Apache Hive: For SQL-like querying and schema design on the ingested dataset.
- Hive LLAP: For seamless integration between Hive and Power BI.
- Power BI Desktop: For visualizing approval patterns, demographic risk, and financial behaviors.
- Format: CSV
- Size: ~10 MB
- Records: 614 loan applications
- Key Features:
- Applicant & Co-applicant Income
- Loan Amount & Term
- Education, Gender, Marital Status
- Credit History
- Employment Type
- Property Area
- Loan Status (Approved / Rejected)
- Total Applications: 614
- Approval Rate: 69% (422 approvals)
- Rejection Rate: 31%
- Average Loan Amount: $145.72K
- Key Insights:
- Strong correlation between credit history and approvals.
- Male and married applicants had higher approval chances.
- Semi-urban regions showed the highest approval volumes.
- Credit history was the dominant approval factor.
- Applicants with 3+ dependents requested higher loan amounts.
- Self-employed individuals faced lower approval odds despite similar incomes.
- Semi-urban property areas led in both approval volume and loan sum.
- Married applicants had better approval rates and loan amounts than single applicants.
| Risk Factor | Observation | Recommended Response |
|---|---|---|
| Credit History | 77% approvals were linked to good credit | Promote credit education & building tools |
| Self-Employment | Approval rate significantly lower | Enhance financial profiling and support docs |
| Gender Disparity | Male applicants approved more often | Audit and enforce gender-neutral assessments |
| Region Dependence | Semiurban areas dominate | Diversify into rural and urban zones |
| Household Size | More dependents = higher loan asks | Stricter income vetting for large families |
- Deploy real-time risk scoring using machine learning for smarter approval decisions.
- Launch mobile-first loan application tools targeting rural and underserved areas.
- Automate rejection notifications with personalized improvement tips.
- Promote joint-loan products to improve approval odds for families.
- Partner with fintechs for alternative scoring and financial literacy, especially for self-employed applicants.
This end-to-end analytical solution demonstrates how integrating Big Data (Hadoop + Hive) with BI tools (Power BI) can uncover powerful insights in loan processing and risk management. The analysis not only reveals approval trends but also pinpoints demographic and behavioral patterns that can guide smarter, fairer, and more profitable lending strategies.
Future enhancements include:
- Integrating Apache Spark for real-time loan scoring.
- Building predictive models for default probability.
- Developing mobile workflows for inclusive digital lending.