FSI (Financial Stress Index) Analysis​_Team 26

The detailed analysis of the Personal Finance Case.

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Inspiration

Financial stress is a multidimensional issue influenced by debt burden, savings, and financial behavior. Traditional metrics often focus only on debt ratios, ignoring liquidity and payment behavior. Our goal was to design a more comprehensive Financial Stress Index (FSI) that captures both structural and behavioral financial risk and visualize how financial stress evolves across different age groups. We were also inspired by real-world economic questions, such as: Why do some age groups experience more financial pressure than others? How do savings and debt interact to influence stress? Can behavioral signals like skipped payments improve stress prediction?

What it does

Our project builds a Financial Stress Index (FSI) that quantifies an individual’s financial pressure using three components: Debt Score (DTI), Liquidity Score, and Behavior Score. This index allows us to: measure financial stress numerically, compare stress across demographic groups, identify key drivers of financial pressure, visualize trends across age groups, correlation matrices, PCA, and trend plots help validate and interpret the index.

How we built it

Data processing

  • Cleaned survey-based personal finance dataset
  • Normalized variables by after-tax income
  • Created derived features (DTI, Liquidity Score, Behavior Score, and FSI) Statistical validation
  • Computed Pearson correlation matrix
  • Built rank correlation heatmaps
  • Performed Principal Component Analysis (PCA) Visualization
  • Rank correlation heatmaps
  • Line plots showing FSI across age groups
  • Standardized trend comparisons Interpretation
  • We observed an inverted-U lifecycle pattern in financial stress, peaking in early-mid adulthood.

Challenges we ran into

  1. Feature engineering
  2. Needed to design a realistic financial stress formula
  3. Required balancing debt, savings, and behavior
  4. Choosing appropriate weights
  5. Arbitrary weights could bias the index
  6. Used PCA and correlation analysis to justify weight selection
  7. Data normalization
  8. Raw debt values vary widely
  9. Had to scale relative to income
  10. Interpretation of correlations
  11. Some variables were weakly correlated
  12. Needed statistical validation to ensure meaningful index design

Accomplishments that we're proud of

  • Designed a new quantitative Financial Stress Index
  • Validated it using correlation analysis and PCA
  • Built clear visualizations showing lifecycle financial stress trends
  • Demonstrated that financial stress is multidimensional
  • Connected statistical modeling to real-world financial economics

What we learned

Technical

  • How to construct composite indices from multiple financial indicators
  • How PCA helps justify feature importance and weights
  • How to visualize high-dimensional financial data Statistical
  • Correlation does not imply causation but helps validate relationships
  • Standardizing variables is critical for fair comparison Conceptual
  • Financial stress depends on both debt and liquidity
  • Behavioral indicators add additional predictive power
  • Financial stress follows a lifecycle pattern

What's next for FSI (Financial Stress Index) Analysis​

  1. Predict financial risk: build models to predict default risk, missed payments, and financial instability.
  2. Build an interactive dashboard: using Tableau and Power BI to allow real-time visualization.
  3. Apply to real-world policy: FSI could help banks assess risk, governments monitor household stress, and Researchers study economic inequality.

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