Credit Analyst Project

 Objective:

The goal of the project was to develop a data-driven model to assess the creditworthiness of loan applicants and reduce the risk of default across the lending portfolio. (or)

To build a predictive credit scoring model and optimize the loan approval process to minimize default risk while supporting business growth.

Role and Responsibilities:

• Collected and cleaned customer financial data including income, credit history, employment details, and past repayment behavior.

• My Role:

  • Analyzed borrower data including credit history, income, loan purpose, and past payment behavior.

  • Identified key risk indicators through data profiling and statistical analysis.

  • Developed a logistic regression model to predict default probability; tested alternative models like decision trees for better accuracy.

  • Segmented customers into risk tiers (low, medium, high) to guide lending decisions.

  • Created interactive dashboards in Power BI to track risk KPIs like default rate, exposure at default (EAD), and debt-to-income ratio (DTI).

  • Collaborated with credit policy and underwriting teams to implement data-backed risk thresholds.

  Tools Used:

  • SQL for data extraction, Python (Pandas, Scikit-learn) for modeling, Power BI for visualization.

  Business Impact:

  • Reduced loan defaults by 15% through more accurate credit scoring.

  • Improved loan approval efficiency, increasing approvals in moderate-risk segments by 10%.

  • Provided leadership with real-time insights into portfolio risk.

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  Let me know if you'd like a version for a specific industry (e.g., fintech, banking, or SME credit).

  Here's a scenario-based explanation of the Credit Analyst project, ideal for interviews where you're expected to show real-world thinking and business impact:

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  🎯 Scenario-Based Explanation: Credit Analyst Project

  Scenario:
  At my previous company (or during a project), we noticed a rising trend in loan defaults, especially among new applicants in the unsecured personal loan segment. The risk team wanted to understand what was driving these defaults and how we could better assess applicants before loan approval.

  Business Problem:
  Our existing credit approval system was rule-based and heavily dependent on credit scores from bureaus. It lacked deeper behavioral insights, and as a result, we were approving some risky applicants and rejecting potentially good ones. This was affecting both the default rate and loan growth.

  My Role:
  As a Credit Analyst, I led a project to develop a data-driven credit risk model. I started by pulling 2 years of historical applicant data using SQL—from the loan origination system, repayment history, and bureau reports.

  I cleaned and analyzed this data using Python. Key variables included:

  • Past defaults or delinquencies

  • Debt-to-Income Ratio

  • Credit utilization rate

  • Employment and income stability

  • Loan purpose and size

  Using this, I built a logistic regression model to estimate the probability of default. I also tested decision trees and random forests to compare accuracy. We used AUC-ROC and confusion matrix to evaluate the models.

  Once finalized, we implemented a risk tier system:

  • Low-risk: Pre-approved

  • Medium-risk: Manual review

  • High-risk: Auto-rejected or offered secured options

  I also developed a Power BI dashboard showing portfolio risk trends, approval rates, and performance of each risk segment. This helped the management and credit teams make data-driven lending decisions.

  Outcome:

  • Default rate reduced by 15%

  • Loan approval rate increased by 10% in safe and moderate-risk categories

  • The credit team had clear visibility into risk, which supported confident lending expansion

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  This scenario clearly shows problem, action, and result (PAR)—a structure interviewers appreciate.
  
  

  Sure! Here's another realistic Credit Analyst project scenario, this time focused on SME lending (small and medium enterprises), which is a common area in credit analysis:

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  📘 Scenario 2: Credit Risk Evaluation for SME Lending

  Scenario:
  A regional bank was planning to expand its SME lending portfolio, but lacked a structured way to evaluate business credit risk beyond traditional financial statements. The challenge was to design a scoring system that could predict default risk using both financial and non-financial indicators.

  Business Problem:
  Unlike retail loans, SME clients often lacked standardized credit histories. Many small businesses had irregular cash flows or limited documentation, which made it difficult to assess their creditworthiness with traditional methods. This led to high turnaround times and inconsistent credit decisions.

  My Role:
  As a Credit Analyst, I was tasked with creating a more robust, data-informed SME credit scoring model.

  Here’s what I did:

  • Collected historical loan data, SME profiles, repayment history, and financial statement data (like revenue, profit margins, liquidity ratios).

  • Incorporated alternative data such as business age, industry risk score, GST filings, and bank statement trends.

  • Conducted EDA to identify top predictors of SME defaults (e.g., frequent overdrafts, declining sales, low interest coverage ratio).

  • Built a logistic regression model, later improved with gradient boosting (XGBoost) to better handle non-linear patterns.

  • Designed a scoring system on a 0–100 scale and assigned credit grades (A to D).

  • Developed Excel and Power BI dashboards to automate risk reports for credit officers.

  Tools Used:
  SQL, Python (Pandas, XGBoost), Excel, Power BI

  Outcome:

  • Reduced loan processing time by 30% through faster risk evaluation.

  • Enabled safe expansion into SME segments, growing the loan book by 20% over 6 months.

  • Improved default prediction accuracy, reducing early-stage delinquencies by 12%.

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  This scenario highlights how you applied data modeling to a more complex, less standardized segment.
  Would you like a version focused on credit limit recommendation or portfolio stress testing next?

  Absolutely! Here's another strong scenario, this time focused on credit limit assessment and portfolio risk monitoring, which is a key responsibility in many credit analyst roles:

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  📗 Scenario 3: Credit Limit Optimization and Portfolio Monitoring

  Scenario:
  At a financial services firm, we noticed that several existing clients had either been overextended on their credit limits or had significantly underutilized limits. This imbalance created unnecessary risk exposure on one end and revenue loss on the other. Leadership wanted a system to dynamically optimize credit limits based on client behavior and portfolio performance.

  Business Problem:
  The company used a fixed credit limit strategy, which didn’t adapt to clients' changing financial health. This led to:

  • Overexposure to high-risk clients

  • Missed revenue opportunities from low-risk clients with strong repayment records

  My Role:
  I led the initiative to design a credit limit optimization framework.

  Key Steps:

  • Pulled historical data of active accounts, utilization patterns, repayment history, income trends, and delinquencies.

  • Calculated key metrics such as utilization rate, days past due (DPD), repayment-to-income ratio, and exposure at default (EAD).

  • Built a scoring model using Python to categorize clients as:

    • Eligible for limit increase

    • Stable – no change

    • At risk – limit reduction or review required

  • Integrated the model outputs with the loan servicing system to flag accounts for manual review or automatic adjustment.

  • Designed a Power BI dashboard to monitor:

    • Portfolio exposure

    • Limit utilization trends

    • Delinquency levels by credit tier

  Tools Used:
  SQL, Python (Pandas, Scikit-learn), Power BI, Excel

  Outcome:

  • Reduced credit risk exposure by 18% by proactively lowering limits for high-risk accounts.

  • Increased credit limits for strong clients, boosting interest income by 12% over 3 quarters.

  • Gave management real-time visibility into exposure metrics and risk-adjusted return.