Introduction

This dashboard and analysis are based on a representative sample of a microfinance institution’s operational dataset, focusing exclusively on the year 2024. The dataset includes key performance indicators (KPIs) such as member and borrower counts, loan amounts, income, deposit balances, and portfolio risk metrics, along with geographical dimensions including region, province, and branch.

The primary goal of this analysis is to provide actionable insights into the financial and operational performance of the institution. To ensure compatibility with R Pubs and maintain data privacy, a stratified sample was used that retains the variability and structure of the original data.

The following business questions are addressed in this dashboard and exploratory analysis:

  1. What is the average loan amount per borrower, per region, per branch, and per province?
  2. What is the trend of new members across months in 2024?
  3. How has the number of active members changed month-over-month in 2024?
  4. What is the total income and deposit balance across branches?
  5. Which branches or regions have experienced the highest growth in membership and borrowing?
  6. How is the portfolio at risk distributed across different regions?
  7. Are there identifiable clusters of high-risk loans or high growth concentrated in specific regions?

By answering these questions through interactive visualizations and data tables, this dashboard aims to support strategic decision-making for branch performance, member engagement, and portfolio risk management within the organization. Moreover, a 20% simple random sample was selected to retain data structure while reducing size for R Pubs. Stratified sampling was considered, but exploratory checks showed key group proportions (e.g., by region) were adequately preserved.

Average Loan Amount per Borrower per Region/Branch/Province

New Members Trend Over Months (2024)

Analysis:

The trend helps assess membership growth. If the line rises over time, it signals successful outreach or product attractiveness. If flat or declining, it may indicate saturation or outreach issues.The bar chart illustrates the monthly trend of new member acquisitions for a microfinance institution in 2024. The institution experienced its highest influx of new members in May, followed closely by October. While there were fluctuations throughout the year, most months saw new member additions exceeding 100,000, indicating a generally strong performance in member acquisition for 2024.

Borrowers Trend by Month

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Analysis:

There appears to be a relatively stable or possibly slightly declining trend. Any spikes or dips might correspond to seasonal changes or promotional campaigns. It gives insight into borrower engagement consistency. The chart highlights the dynamic nature of the microfinance institution’s lending activity throughout the year. May and October were particularly strong months for borrower acquisition, while January, March, and November saw lower active borrower numbers. Understanding the drivers behind these monthly variations is crucial for optimizing lending strategies and managing portfolio growth.

Income and Deposits Across Branches

Analysis:

Branches with high income but low deposits may focus more on lending, whereas the opposite suggests strong savings programs. This comparison identifies top-performing vs. underperforming branches.San Carlos City is the top-performing branch, boasting the highest monthly income of over 3.97 million and a substantial deposit balance exceeding 350 million followed by Rosario follows closely with over 3.92 million in monthly income and a deposit balance greater than 359 million. The top 10 branches shown exhibit a significant range in both income and deposits, with monthly incomes ranging from approximately 2.96 million to 3.97 million and deposit balances from around 215 million to 359 million.

Growth in Membership and Borrowing (Top Branches)

Analysis:

Branches with high ratios of borrowers to members are more credit-active. Those with high membership but low borrowing may need further financial education or lending incentives. The data shows that San Carlos City is the leading branch, with the highest number of total members (10,743) and total borrowers (8,601), which indicates it’s a very active and successful branch in terms of outreach and loan disbursement. This data is followed by Rosario as the second highest, with 9,528 total members and 8,835 total borrowers.

Portfolio by Region

Analysis:

High-performing regions can be used as benchmarks. Areas with high risk or low penetration may need further support or restructuring. The chart shows the distribution of the Portfolio at Risk (PAR) across different regions, indicating the concentration of overdue loans in each area. A higher PAR value signifies a greater proportion of the loan portfolio in that region is at risk of default. BARMM stands out with the highest Portfolio at Risk, indicating the largest proportion of its loan portfolio is considered delinquent or at risk of not being repaid. Region X and the Cordillera Administrative Region also exhibit significantly high PAR values, following BARMM. Region 4-B, and the National Capital Region (NCR) have the lowest Portfolio at Risk, suggesting better loan repayment performance in these areas.

Regional Risk & Growth Clustering

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Analysis:

Clusters in the top-right with dark/red coloring represent high-growth, high-risk regions. Ideal regions fall in the high-growth, low-risk quadrant. This visualization supports strategic allocation of risk mitigation or investment efforts. The data shows a strong positive correlation between members and borrowers. Critically, it identifies regions with both high growth (many members and borrowers) and high average risk (darker red points), necessitating targeted risk management. Conversely, it also highlights high-growth regions with lower risk (lighter red points), which can serve as benchmarks. The plot aids in categorizing regions for strategic decision-making, allowing the institution to identify areas for intensive risk mitigation versus those for continued growth and potential scaling.

Branch Map of Portfolio

Analysis:

The map provides geographic context to portfolio distribution and highlights operational spread. Densely clustered points may suggest urban concentrations or overlapping markets. Each point represents a branch, with its color indicating the magnitude of its “Portfolio_at_risk” (darker blue signifies higher risk, lighter blue indicates lower risk). The map visually identifies areas where the microfinance institution has branches and highlights the geographical distribution and concentration of its loan portfolio risk.

Conclusion:

In conclusion, the microfinance institution experienced robust growth in members and borrowers in 2024, particularly in the mid-year and early Q4. This growth, however, is not uniform in its risk profile. While some high-performing branches and regions contribute significantly to income and growth, others, especially BARMM, Region X, and the Cordillera Administrative Region, pose significant challenges due to their high Portfolio at Risk. The analysis strongly suggests a need for targeted risk management strategies in high-growth, high-risk regions, while simultaneously leveraging the best practices from high-growth, low-risk areas to ensure sustainable expansion and maintain portfolio quality across the institution’s operations. The geographical insights from the branch map further enable precise, localized interventions.