Credit Scoring
Credit scoring is a statistical method used by lenders to assess the creditworthiness of individuals or businesses, typically predicting the likelihood of default on loans or other credit obligations. It involves analyzing historical data (e.g., payment history, debt levels, credit history length) to generate a numerical score that quantifies risk, with higher scores indicating lower risk. This process is widely used in banking, finance, and fintech to automate and standardize credit decisions, such as approving loans, setting interest rates, or determining credit limits.
Developers should learn credit scoring when building applications in financial technology (fintech), banking, lending platforms, or risk management systems, as it enables data-driven decision-making for credit approvals and risk assessment. It is essential for roles involving predictive modeling, machine learning, or data analysis in finance, helping to comply with regulations (e.g., fair lending laws) and improve operational efficiency by reducing manual underwriting. Use cases include developing credit scoring algorithms for online loan applications, integrating with credit bureaus like Experian or Equifax, or creating fraud detection systems in e-commerce.