A passbook is a ledger you're allowed to read. This is the same idea applied to a credit model — a demo eligibility engine that shows its SHAP contributions and plain-language reasoning for every decision, not just the verdict.
Four inputs are entered, weighed, and posted — the same order a credit officer would work through, made visible at every step.
Identity, employment type and tenure — the baseline signal for repayment stability before any loan terms enter the picture.
Amount, purpose and tenure. The model checks these against income before it ever looks at credit history.
CIBIL score, existing EMIs and prior defaults are combined into a debt-to-income figure — the single strongest predictor in most digital lending models.
A verdict is issued alongside a SHAP breakdown and a plain-language (LIME-style) explanation — debited and credited factors, laid out like a ledger balance.
Most credit-scoring models are correct more often than they are understood. Two techniques close that gap, and this demo uses a simplified version of both.
Each factor — credit score, DTI, income, tenure — is assigned a signed value showing exactly how much it pushed the decision toward approval or rejection, and by how much relative to the others.
The largest contributors are translated into sentences a loan officer — or an applicant — can actually act on, rather than a table of coefficients.
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