Credit Risk Analytics
9 stories tagged Credit Risk Analytics on Learning Fanatic.
- Lesson 2.2: Univariate analysis of credit variables Learn to profile each credit variable on its own: distributions, spikes, special codes, rare categories, missing values and outliers, before any modelling.
- Lesson 1.4: Defining default and the observation window Learn how lenders define default, why the threshold and the time window change the default rate, and how to label loans good, bad or indeterminate.
- Lesson 2.1: Building a credit modelling dataset Learn how to turn loan tables into one modelling dataset: choosing the grain, the observation date, features, the target, exclusions and a sample size.
- Lesson 10.3: RBI's expected credit loss framework and the 2027 transition Learn how the RBI's expected credit loss rules work: three stages, provision floors, the 30-day trigger debate, scenarios and the 2027 transition.
- Lesson 1.1: What credit risk is: PD, LGD, EAD and expected loss Learn what credit risk is and how lenders split it into PD, LGD and EAD to calculate expected loss, price loans and plan for a bad year.
- Lesson 1.2: The loan lifecycle and the data it leaves behind Follow a loan from application to closure and learn which tables it creates, how days past due are counted, and why rejected applicants have no outcome.
- Lesson 1.3: Loan amortisation schedules and the maths of the EMI Learn how an EMI is calculated, how each instalment splits into interest and principal, how the balance (your EAD) falls, and why flat rates mislead.
- Lesson 2.3: Bivariate analysis and finding risk drivers Learn to relate each credit variable to default with risk tables, lift and two-way tables, and to avoid confounding traps such as Simpson's paradox.
- Lesson 2.4: Delinquency buckets, NPAs and portfolio KPIs Learn the buckets, ratios and flows lenders report monthly: SMA stages, gross and net NPA, provision coverage, slippage, credit cost and collections.