#Credit

Articles about Credit — exploring patterns, best practices, and real-world implementations in production systems.

16 posts tagged with credit. ← Blog home

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Pratik Dhanave · ·8 min read

Credit and Position Monitoring — Watching the Thesis Over Time

Underwriting is the decision to do a deal; monitoring is everything after — watching whether the thesis still holds as the world changes, the borrower performs or deteriorates, and covenants tighten or breach. It's traditionally periodic, manual, and uneven, which means problems get noticed late. AI makes monitoring continuous: reading every update, tracking every covenant, and flagging variance from the original thesis the moment it appears. This post is about building the monitoring layer.

Monitoring is the long aftermath of a deal — watching whether the thesis still holds as the world changes. AI makes it continuous: reading every update, tracking every covenant, flagging variance from the thesis the moment it appears — attacking the classic failure of problems noticed too late. The make-or-break is signal over noise. (Educational, not financial advice.)

#AI Financial Engineering#Monitoring#Credit#Covenants#Quantitative Finance#AI Engineering#Credit Risk
Pratik Dhanave · ·8 min read

AI Underwriting — From Deal Materials to Auditable Judgment

Underwriting is where a lender or investor decides whether a deal is worth doing — reading the materials, assessing the risk, forming a thesis, and documenting the judgment for an investment committee. It's slow, manual, judgment-heavy work, and it's one of the highest-value places AI can help: turning raw deal materials into structured, pressure-tested, auditable analysis. This post is about building AI underwriting that accelerates the work without surrendering the judgment or the accountability.

Underwriting turns deal materials into a decision — slow, manual, judgment-heavy work and a flagship AI application. Turning raw materials into structured, pressure-tested, auditable analysis: reading the deal record, surfacing risks, pressure-testing against precedent, drafting IC docs — while judgment stays human and everything stays traceable. (Educational, not financial advice.)

#AI Financial Engineering#Underwriting#Credit#Document AI#Quantitative Finance#AI Engineering#Credit Risk#Testing
Pratik Dhanave · ·8 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 · ·8 min read

Explaining Credit Decisions: SHAP and Adverse-Action Reason Codes

How lenders turn a model's raw probability into the specific, legally required reasons a declined applicant must receive.

Turning model outputs into legally-required adverse-action reason codes. Global vs local explainability, SHAP values, mapping contributions to reason codes, and fairness checks (disparate impact) that regulators expect.

#Financial AI#Machine Learning#Explainability#Credit#Credit Risk
Pratik Dhanave · ·8 min read

Weight of Evidence and Information Value: Binning for Credit Models

How WOE binning turns raw variables into monotonic, interpretable predictors on a log-odds scale, and how Information Value ranks which features earn a place in a regulator-defensible scorecard.

How WOE binning turns raw variables into monotonic, interpretable predictors, how Information Value ranks feature predictive power, monotonic binning, handling missing/rare bins, and why this discipline underpins regulat…

#Financial AI#Machine Learning#Credit#Feature Engineering#Credit Risk
Pratik Dhanave · ·8 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 · ·6 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 · ·7 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 · ·8 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 · ·8 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 the blog or explore related topics using the tag cloud above.