CredXplain is explainable credit decisioning built for Indian banking. ULI-ready. EASE-aligned. Every decision explainable, every outcome fair, and every near-miss applicant guided from amber to green.
Without governed credxplain, these problems compound silently with every new data source and AI deployment.
Mid-tier banks and RRBs rely on manual evaluation — slow, inconsistent, and unable to scale to the MSME and agricultural credit demand.
73% of banking executives cite explainability as their top barrier to AI adoption in lending — without it, automated decisions cannot withstand regulatory scrutiny.
Most credit scoring models are not monitored for fairness across protected categories — creating regulatory and ethical exposure that compounds with scale.
Application arrives — multi-source data fusion begins across CBS, ULI, bureaus, and alt data
Product-specific scorecard applied — segment-appropriate risk factors evaluated
Bias monitoring across protected categories — fairness evaluated before any decision
Three-level explanation generated — data, model, and decision level for each stakeholder
Approve, decline, or amber-to-green guidance delivered with full audit trail
CBS, LOS, ULI data services, credit bureaus, GST returns, satellite data — federated in real time. No batch extraction, no stale data.
Agri, KCC, SHG/JLG, MSME, Retail — purpose-built scoring models for each segment, not one generic model applied universally.
Data-level, model-level, and decision-level explanations — human-readable for the borrower, the credit officer, and the regulator.
Near-threshold applicants receive a structured improvement pathway — what to address, by how much, for a different outcome.
See CredXplain evaluate credit applications across CBS, ULI, and bureau data — with explainability, bias monitoring, and amber-to-green guidance built in.
CredXplain is Tantor's explainable credit decisioning agent built for Indian banking — ULI-ready, bias-monitored, and grounded in multi-source data. Every credit decision is explainable at three levels, and near-threshold applicants receive amber-to-green improvement guidance.
CredXplain generates three-level explainability — data-level, model-level, and decision-level — in human-readable language for the borrower, the credit officer, and the regulator. This satisfies explainability requirements that block most AI adoption in lending.
CredXplain fuses data from core banking, loan origination systems, ULI data services, credit bureaus, GST returns, and alternative signals in real time through federation — no batch extraction, no stale data — enabling inclusive credit decisioning for thin-file borrowers.
Yes. CredXplain evaluates fairness across protected categories before every decision, addressing the bias-monitoring gap in most credit scoring models. Combined with product-specific scorecards for MSME, agri, and retail segments, it makes credit decisioning fair and auditable.