Where lenders want the risk
Why lending is a business of collections.
Mayank Jain
When consent meets collateral
If you've seen The Odyssey, you know it's a story about a long journey home, with very different worlds colliding along the way. India's credit market has a similar contrast playing out right now.
A good borrower can still be a risky loan
The Reserve Bank of India just made risk pricing more accountable and risk factors more visible.
Case-study
How IIFL Replaced Hard-Coded Rules with FinBox Sentinel to Move at the Speed of Risk
IIFL's digital lending arm swapped a slow, IT-dependent rules engine for Sentinel's no-code decisioning system, turning weeks-long policy changes into a task risk teams complete themselves in minutes.
DeviceConnect
Which Device Data Credit Scoring Vendors Are Credible for Indian Lenders? Evaluation Criteria and Vendor Landscape
Credibility in device alternative-data credit scoring rests on factors: depth of India-specific data rail integration, explainability, model validation via metrics like Gini Coefficient & RBI framework alignment. talks of specialised vendors FinBox DeviceConnect with retrofitted global platforms.
BankConnect
Bank Statement Analysis Providers in India: What Lending Teams Should Evaluate Before Choosing One
Before shortlisting a bank statement analysis provider, separate parsing from Account Aggregator pulls. Evaluate on parsing accuracy, categorisation, fraud detection, AA-framework compliance, TAT at scale, and LOS integration. Compares Perfios, Signzy, Finvu, Anumati and FinBox BankConnect.
RBI
RBI Cybersecurity Framework 2026: a plain-English guide for banks and their technology partners
RBI's 2026 Cybersecurity Directions for commercial banks: key requirements, the 6-hour rule, vendor controls, and a compliance checklist.
Cost of careful
The cost of careful
Why slow is no longer safe in the era of agentic and AI lending?
Sentinel
How to Configure Credit Policy Changes in a No-Code BRE (Without Engineering Tickets)
A no-code business rules engine (BRE) lets risk and credit teams build, test, and deploy lending policy changes—cutoffs, waterfall logic, exclusion rules—without writing code or filing engineering tickets. With a drag-and-drop rule builder, policy changes that traditionally took 4-6 weeks
Sentinel
Who Owns the Credit Decisioning Platform — Risk or IT — and How CROs Use It to Cut NPA
Credit decisioning platform ownership is typically shared: Risk/Credit teams own policy logic, rules, and scorecards, while IT/Engineering owns integration, uptime, and data pipelines, with governance sitting under the CRO or CTO depending on org maturity. CROs use decisioning platforms to
Sentinel
Components of a Credit Decisioning Stack: Decision Engine, Rules, Tables & Scorecards Explained
A credit decisioning platform automates loan underwriting by combining a decision engine, a business rules engine (BRE), decision tables, scorecards, and data integration layers. Rule-based decisioning applies deterministic, auditable logic (e.g., FOIR limits, eligibility checks), while ML
Sentinel
Rule-Based vs ML-Based Credit Decisioning: Can BRE and ML Models Work Together?
Rule-based decisioning uses deterministic if-then logic (decision tables, policy thresholds) that produces identical outputs for identical inputs and is easy to audit. ML-based decisioning uses statistical models trained on historical repayment data to score risk probabilistically and adap