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FairMoney Loan Defaulters' Analysis

  • Writer: Michael Olaniyi Jeremiah
    Michael Olaniyi Jeremiah
  • Sep 17, 2024
  • 2 min read

Updated: Aug 15

By Michael Olaniyi Jeremiah

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Introduction:

This project aims to develop a foundational understanding of risk analytics within FairMoney Financial Services, specifically focusing on minimizing the risk of financial loss while lending to customers. The analysis explores the dataset to uncover customer loan default behaviours. Understanding loan defaulters is critical for financial institutions to assess risk and make informed lending decisions. By utilizing a dataset containing information about past borrowers, including their demographics, financial history, and loan details, we aim to explore default patterns and provide actionable recommendations.

Tools:

  • Excel

  • Power Query

  • Power Pivot

The FairMoney Loan Defaulters analysis aims to identify defaulters and examine the relationships between defaulters' characteristics such as gender, education, occupation, income type, age bracket, and marital status.

Dataset:

This is a publicly available dataset containing historical data on loan applicants. The dataset includes features and whether the borrower defaulted on the loan or not.

Data Processing with Power Query and Power Pivot:

  • Removal of duplicates

  • Data normalization

  • Create new columns Age, Age Bin

  • Data connection

  • Analysis with pivot tables


Loan Applicants Cleaned dataset:

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FairMoney Loan Defaulters' Analysis Dashboard:

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Key Findings:

  • There are 307,511 distinct loan borrowers, with females constituting 66% and males 34%. The total loan amount disbursed is ₦184,207,084,196, with an average borrowed amount of ₦45,000.

  • The total number of defaulters is 24,825, with females making up 57% and males 43%.

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  • Borrowers are categorized into 18 occupations, with one group unspecified. The unspecified group has the highest borrowed amount at ₦55,360,217,255, followed by labourers at ₦31,491,124,705.


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  • Analysis of defaulters by marital status shows that married females have a higher default rate compared to married males.


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  • Borrowers aged 30-49, particularly those with secondary level education, have the highest loan amounts, ranging between ₦47,287,004,336 and ₦51,586,502,473.


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Recommendations:

1. Targeted Loan Offerings:

  • Focus loan offerings on borrowers aged 29 to 50, who demonstrate a higher likelihood of repayment and substantial borrowing potential.


2. Enhanced Loan Recovery Measures:

  • Implement stricter loan recovery protocols, such as obtaining pre-authorization to debit the borrower's credit or debit card upon loan due dates to ensure quicker loan recovery.


3. Strategic Allocation of Loan Recovery Resources:

  • Allocate resources efficiently by targeting the recovery of the 2.6% of the total loan amount held by defaulters while continuing to offer more loans to reliable borrowers who pay on time.


4. Data Collection Improvements:

  • Improve data collection accuracy by ensuring all occupation and age data are accurately recorded, particularly addressing the issue of unspecified occupations.


These recommendations aim to enhance FairMoney's loan disbursement strategy, improve recovery rates, and ensure accurate data collection for more informed decision-making.




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Thank you for taking the time to review this project. I welcome your comments, suggestions, and feedback.


For any project discussions or job opportunities, please feel free to contact me at:

Phone: 234706664402

 
 
 

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