#Lending

Lending platform engineering requires double-entry accounting, regulatory compliance, and fraud prevention. Posts cover PostgreSQL schema design for lending operations, KYC and AML integration, RBAC for financial data, and the architectural patterns specific to digital lending services.

14 posts tagged with lending. ← All posts

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Pratik Dhanave · ·7 min read

Reject Inference: Modeling the Applicants You Declined

Why a scorecard trained only on funded loans quietly overstates its own accuracy, and the inference techniques that pull the declined population back into view.

Why credit models trained only on approved-and-funded loans are biased, and the reject-inference techniques (reclassification, parceling, augmentation) that correct for the applicants you never observed.

#Financial AI#Machine Learning#Credit#Lending#Credit Risk
Pratik Dhanave · ·6 min read

Building a Credit Scorecard: From Raw Data to a Score

How lenders turn application and bureau data into a single, defensible number — and why the interpretable scorecard still wins in regulated lending.

The end-to-end credit-scoring pipeline: target definition (bad = 90+ dpd), feature engineering, WOE transformation, a logistic-regression scorecard, points scaling (PDO), and validation (KS, Gini, AUC).

#Financial AI#Machine Learning#Credit#Lending#Credit Risk
Pratik Dhanave · ·5 min read

Delinquency as a State Machine: DPD Buckets, SMA, and NPA Classification

Model days-past-due transitions, SMA/NPA classification, provisioning triggers, and cure logic as one deterministic engine driven by time and repayment events.

Teaches how to model delinquency as a state machine: days-past-due bucket transitions, SMA/NPA classification rules, provisioning triggers, and cure/roll-forward/roll-back logic driven by repayment events.

#Fintech#Lending#Credit#Financial Systems#Credit Risk
Pratik Dhanave · ·6 min read

Building a Credit Decisioning Engine

How to assemble features from bureau, bank, and alt data, serve a scorecard or model, apply policy cutoffs, and emit adverse-action reason codes with a full audit trail.

Teaches how to build a decisioning engine: feature assembly from bureau/bank/alt data, scorecard vs ML model serving, policy rules and cutoffs, and adverse-action reason-code generation with an audit trail.

#Fintech#Lending#Credit#Financial Systems#Credit Risk
Pratik Dhanave · ·7 min read

Building a Deterministic Amortization Schedule Engine

Computing loan amortization you can reproduce to the cent — annuity math, day-count conventions, the per-installment principal/interest split, and prepayment recomputation.

Teaches how to compute loan amortization deterministically: EMI/annuity vs reducing-balance formulas, day-count conventions, principal/interest split per installment, and prepayment/rescheduling recalculation.

#Fintech#Lending#Credit#Financial Systems#Credit Risk
Pratik Dhanave · ·7 min read

Building a Daily Interest Accrual Engine

How to compute interest that earns every day, tracks the accrued-but-unbilled balance, survives mid-period rate changes, and posts to the ledger with idempotent replay-safe jobs.

Teaches how to build a daily interest accrual engine: accrual basis and compounding, accrued-but-unbilled tracking, rate changes mid-period, and idempotent daily accrual jobs that post to the ledger.

#Fintech#Lending#Credit#Financial Systems#Credit Risk

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.