Indian lenders choose LOS from four categories: enterprise core-banking suites (Nucleus, TCS BaNCS), verification vendors (Perfios, Signzy), modular API-first platforms (FinBox LOS, Lentra), or in-house builds trading configurability and speed-to-launch against single vendor, end-to-end coverage.
Device data and alternative-data credit scoring help lenders underwrite thin-file and NTC borrowers by inferring creditworthiness signals from smartphone metadata. Before selecting a provider, risk teams must confirm alignment with RBI's DPDP norms and Google Play's Android permissions policy.
You don't need to rip out your core banking system to go agentic—layering AI on top is a strong first step.
A risk signals API in lending ingests device-level and behavioural data and converts it into structured risk indicators that plug into an underwriting decision engine.
A rules engine for NBFCs is software that lets credit and risk teams codify, test, and modify lending policies without depending on engineering for every change. Legacy hard-coded rules engines create operational bottlenecks because policy changes require developer involvement.
NBFCs evaluating AI underwriting software should focus on five factors: alternate/bureau data integration, explainable credit decisions, configurable rules and ML models, fast API integration with LOS/LMS, and built-in fraud and KYC intelligence.
Loan decisioning software automates credit policy execution — combining BRE, ML-based scoring, and data integrations to approve, decline, or refer loan applications. For Indian lenders, the category has evolved to require India-first data plumbing & built-in explainability.
India's lending tech vendors split into four categories: API/data aggregation, credit decisioning, LOS/LMS, and modular infrastructure. Here's what each builds, where FinBox fits, and the five criteria banks and NBFCs should weigh before choosing a partner.
A bank statement analyser parses, categorises, and scores a borrower's transaction history — from PDF uploads or the Account Aggregator framework — for credit decisions. Here's how AA and PDF sourcing differ, and what credit teams should evaluate before choosing one.
An agentic credit decision platform uses AI agents capable of autonomous, multi-step reasoning — pulling data, applying policy logic, resolving exceptions, and recommending or executing a credit decision with minimal manual intervention — as opposed to a static rules engine.
Looking for the best AI decisioning platform? This guide compares the leading solutions helping digital lenders automate credit risk assessment by combining rule engines, ML/statistical models, and real-time data orchestration to approve, reject, or price loans.
A bank statement analyser in India ingests bank statements — either as uploaded PDFs/scanned copies or pulled directly via the RBI-backed Account Aggregator (AA) framework — and converts unstructured transaction data into structured, categorised cash flow signals for underwriting.
