• Base Credit Score: Credit score normalization and weighting
• Risk Factors:
  • Income Factor
  • Debt-to-Income Ratio Factor
  • Payment History Factor
• Default Probability Calculation: Using sigmoid function
• Risk Categorization: LOW, MEDIUM, HIGH, VERY_HIGH

• Suggested credit limits
• Recommended interest rates
• Credit terms
• Specific recommendations by risk factor

• CSV data reading
• Configurable chunk processing
• Database and CSV file writing
• Error handling and retries

• Actuator endpoints for monitoring
• Prometheus integration
• Detailed logging
• Performance metrics

• Java 17 or higher
• Maven 3.6 or higher
• Spring Boot 3.x
• Spring Batch 5.x
• H2 Database
• Caffeine Cache

src/main/java/com/example/creditrisk/
├── config/
│   └── BatchConfig.java         # Batch job configuration
├── model/
│   └── CreditRiskData.java      # Data model
├── processor/
│   └── CreditRiskProcessor.java # Risk processor
├── reader/
│   └── CreditRiskItemReader.java # Data reader
├── service/
│   └── RiskAnalysisService.java # Risk analysis service
├── writer/
│   └── CreditRiskFileWriter.java # Results writer
└── CreditRiskBatchApplication.java

1. Clone the repository:

git clone [repository-url]
cd credit-risk-batch

2. Build the project:

mvn clean install

3. Run the application:

mvn spring-boot:run

The input file must be in CSV format with the following columns:

• customerId: Unique customer identifier
• customerName: Customer name
• birthDate: Birth date (YYYY-MM-DD)
• age: Customer age
• maritalStatus: Marital status (SINGLE, MARRIED, DIVORCED)
• educationLevel: Education level (HIGH_SCHOOL, BACHELORS, MASTERS, DOCTORATE)

• creditScore: Credit score (0-850)
• income: Annual income
• debtToIncomeRatio: Debt-to-income ratio (%)
• monthlyExpenses: Monthly expenses
• savingsBalance: Savings balance
• propertyValue: Property value (if applicable)

• employmentType: Employment type (FULL_TIME, PART_TIME)
• employmentYears: Years of employment
• industry: Industry/Sector

• paymentHistory: Payment history (0-100)
• creditHistoryYears: Years of credit history
• numberOfCreditCards: Number of credit cards
• creditCardUtilization: Credit card utilization (%)
• hasBankruptcy: Bankruptcy indicator
• bankruptcyYearsAgo: Years since bankruptcy (if applicable)
• hasForeclosure: Foreclosure indicator
• foreclosureYearsAgo: Years since foreclosure (if applicable)

• loanAmount: Loan amount
• loanPurpose: Loan purpose (MORTGAGE, CAR, PERSONAL, BUSINESS, EDUCATION, CONSOLIDATION, HOME_IMPROVEMENT, INVESTMENT)
• existingLoans: Number of existing loans
• loanTerm: Loan term (SHORT_TERM, MEDIUM_TERM, LONG_TERM)
• interestRate: Interest rate (%)
• collateralType: Collateral type (NONE, REAL_ESTATE, VEHICLE, BUSINESS)
• collateralValue: Collateral value

• residenceType: Residence type (OWN, RENT)
• yearsAtCurrentAddress: Years at current address

• guarantorStatus: Guarantor status (NONE, REQUIRED)
• guarantorCreditScore: Guarantor credit score
• guarantorIncome: Guarantor income
• guarantorRelationship: Relationship with guarantor (FAMILY, FRIEND, NA)

• riskCategory: Risk category (LOW, MEDIUM, HIGH, VERY_HIGH)
• status: Application status (ACTIVE, INACTIVE)
• additionalInfo: Additional information
• recommendations: Specific recommendations

customerId,customerName,creditScore,income,debtToIncomeRatio,paymentHistory,employmentYears,loanAmount,loanPurpose,existingLoans,propertyValue,maritalStatus,educationLevel,industry,riskCategory,status,additionalInfo,recommendations,birthDate,age,employmentType,monthlyExpenses,savingsBalance,creditHistoryYears,numberOfCreditCards,creditCardUtilization,hasBankruptcy,bankruptcyYearsAgo,hasForeclosure,foreclosureYearsAgo,residenceType,yearsAtCurrentAddress,loanTerm,interestRate,collateralType,collateralValue,guarantorStatus,guarantorCreditScore,guarantorIncome,guarantorRelationship
CUST001,John Smith,720,85000,0.35,95,8,150000,Mortgage,1,350000,Married,Bachelors,Technology,Low,Approved,Stable employment history,Consider refinancing in 2 years,1980-05-15,43,Full-time,3500,50000,15,2,0.25,false,0,false,0,Mortgage,5,Long-term,4.5,Real Estate,350000,None,,,,
CUST002,Maria Garcia,680,65000,0.45,88,5,120000,Car,2,250000,Single,Masters,Healthcare,Medium,Approved,Recent job change,Monitor debt ratio,1988-08-22,35,Full-time,2800,25000,8,3,0.45,false,0,false,0,Rent,2,Medium-term,5.2,Vehicle,45000,None,,,,
CUST003,Robert Johnson,580,45000,0.65,75,3,80000,Personal,3,180000,Divorced,High School,Retail,High,Rejected,Multiple late payments,Improve credit score,1975-11-30,48,Part-time,2200,5000,5,4,0.85,true,3,false,0,Rent,1,Short-term,7.5,None,0,Required,650,55000,Parent
CUST004,Sarah Williams,750,120000,0.25,98,12,200000,Business,0,500000,Married,PhD,Finance,Low,Approved,Excellent credit history,Consider investment opportunities,1972-03-10,51,Self-employed,4500,150000,20,2,0.15,false,0,false,0,Own,10,Long-term,4.0,Real Estate,500000,None,,,,
CUST005,Michael Brown,620,55000,0.55,82,4,95000,Education,1,220000,Single,Bachelors,Education,Medium,Approved,Student loan debt,Consolidate loans,1990-07-18,33,Full-time,2300,15000,6,2,0.60,false,0,false,0,Rent,3,Medium-term,5.8,None,0,None,,,,
CUST006,Lisa Chen,710,95000,0.30,92,7,180000,Home Improvement,2,400000,Married,Masters,Engineering,Low,Approved,High property value,Consider home equity,1985-12-05,38,Full-time,3800,75000,12,3,0.30,false,0,false,0,Mortgage,6,Long-term,4.2,Real Estate,400000,None,,,,
CUST007,David Miller,590,48000,0.60,78,2,70000,Debt Consolidation,4,150000,Single,Associates,Manufacturing,High,Rejected,High debt load,Debt management plan,1982-09-25,41,Contract,2100,8000,4,5,0.90,false,0,true,2,Rent,1,Short-term,8.0,None,0,Required,680,60000,Sibling
CUST008,Emma Wilson,730,110000,0.28,96,10,250000,Investment,1,600000,Married,Bachelors,Real Estate,Low,Approved,Investment property,Portfolio diversification,1978-04-12,45,Self-employed,4200,200000,18,2,0.20,false,0,false,0,Own,8,Long-term,4.8,Real Estate,600000,None,,,,
CUST009,James Taylor,650,72000,0.40,85,6,130000,Medical,2,280000,Married,Bachelors,Healthcare,Medium,Approved,Medical expenses,Health insurance review,1987-06-30,36,Full-time,3000,35000,9,3,0.50,false,0,false,0,Mortgage,4,Medium-term,5.5,Real Estate,280000,None,,,,
CUST010,Sophia Martinez,670,68000,0.42,87,5,115000,Wedding,1,260000,Engaged,Bachelors,Marketing,Medium,Approved,Upcoming wedding,Budget planning,1992-02-14,31,Full-time,2900,20000,7,2,0.40,false,0,false,0,Rent,2,Short-term,6.0,None,0,Provided,700,75000,Fiance

{
  "customerId": "CUST001",
  "riskCategory": "LOW",
  "baseScore": 88.2,
  "incomeRiskFactor": 0.2,
  "debtRiskFactor": 0.15,
  "paymentRiskFactor": 0.05,
  "finalScore": 85.0,
  "defaultProbability": 0.12,
  "recommendations": {
    "creditLimit": "Increase credit limit",
    "interestRate": "Offer preferred rate",
    "terms": "Flexible terms"
  }
}

{
  "customerId": "CUST002",
  "riskCategory": "MEDIUM",
  "baseScore": 80.0,
  "incomeRiskFactor": 0.35,
  "debtRiskFactor": 0.25,
  "paymentRiskFactor": 0.15,
  "finalScore": 65.0,
  "defaultProbability": 0.28,
  "recommendations": {
    "creditLimit": "Maintain current limit",
    "interestRate": "Standard rate",
    "terms": "Standard terms",
    "payment": "Suggest structured payment plan"
  }
}

{
  "customerId": "CUST003",
  "riskCategory": "HIGH",
  "baseScore": 68.2,
  "incomeRiskFactor": 0.55,
  "debtRiskFactor": 0.45,
  "paymentRiskFactor": 0.30,
  "finalScore": 45.0,
  "defaultProbability": 0.62,
  "recommendations": {
    "creditLimit": "Reduce credit limit",
    "interestRate": "Higher rate",
    "terms": "Stricter terms",
    "income": "Request additional income verification",
    "debt": "Recommend debt reduction",
    "payment": "Suggest structured payment plan"
  }
}

• Normalization: (creditScore / 850) * 100
• Weight: 40% of final score
• Example: 750 points → 88.2 normalized points

• Normalization: min(income / 200000, 1.0)
• Weight: 30% of final score
• Example: $120,000 → 0.6 normalized

• Normalization: min(debtToIncomeRatio / 100, 1.0)
• Weight: 20% of final score
• Example: 0.35 (35%) → 0.35 normalized

• Normalization: paymentHistory / 100
• Weight: 10% of final score
• Example: 95% → 0.95 normalized

The application generates two types of output:

• Table: credit_risk_data
• Location: Memory (H2 in-memory database)
• Access: http://localhost:8080/h2-console
• Credentials:
  • JDBC URL: jdbc:h2:mem:creditriskdb
  • Username: sa
  • Password: (empty)

• Location: src/main/resources/output/credit-risk-results.csv
• Format: CSV with the following columns:

• All fields from the input file

• baseScore: Normalized base score (0-100)
• incomeRiskFactor: Income risk factor (0-1)
• debtRiskFactor: Debt risk factor (0-1)
• paymentRiskFactor: Payment history risk factor (0-1)
• finalScore: Weighted final score (0-100)
• defaultProbability: Default probability (0-1)
• riskCategory: Risk category (LOW, MEDIUM, HIGH, VERY_HIGH)

• creditLimitRecommendation: Credit limit recommendation
• interestRateRecommendation: Interest rate recommendation
• termsRecommendation: Terms recommendation
• incomeRecommendation: Income-related recommendation
• debtRecommendation: Debt-related recommendation
• paymentRecommendation: Payment-related recommendation

The application exposes several monitoring endpoints:

• /actuator/health: Application status
• /actuator/metrics: Performance metrics
• /actuator/prometheus: Prometheus format metrics

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is under the MIT License. See the LICENSE file for more details.