Faster decisions. Stronger risk control. Audit-ready governance.
From application intake to post-disbursement monitoring — QCAP orchestrates 8 specialised AI agents to deliver consistent, explainable, and RBI-compliant credit decisions at scale.
Faster underwriting TAT
Lower analyst effort
Fewer rework loops
Pilot deployment ready
Each agent owns a distinct credit intelligence domain. Together they form a closed-loop underwriting system — from first data ingestion to continuous portfolio monitoring.
Conversation-led intake auto-structures borrower data and generates targeted gap queries — reducing back-and-forth by 60%.
Reconciles GST returns against ITR, validates bank statements, and flags vendor concentration — building cross-source data integrity.
Automated financial spreading computing DSCR, EBITDA, cash-flow stability, and working capital cycles across 3–5 year periods.
Graph Neural Network analysis uncovers circular transactions, related-party flows, and ownership structures invisible to traditional checks.
Enforces RBI norms, internal exposure limits and sector concentrations — with governed exception handling and full deviation logging.
Multi-dimensional sub-scores for PD, liquidity, industry risk, and behavioural patterns — combining XGBoost models with expert overlays.
Issues structured Approve/Conditional/Reject recommendations with fully drafted CAM reports, evidence links, and committee-ready narratives.
Real-time EWS, covenant breach detection, and data-drift alerts keep the credit team ahead of portfolio risk — not reacting to it.
Every day without an intelligent platform is a day of compounding inefficiency, inconsistency, and risk — costing banks and NBFCs time, capital, and competitive ground.
Analysts spend 60–70% of underwriting time on repetitive financial spreading, document retrieval, and manual data entry — a high-cost, low-value activity prone to transcription errors.
Critical credit intelligence is siloed across VDRs, LOS, CBS, and offline Excel trackers. There is no single source of truth — leading to duplicate work, version conflicts, and decision risk.
Manual overrides go untracked. Policy interpretation varies by analyst and deal team. Deviations compound into systemic risk — and create audit exposure under RBI inspection.
Monitoring only begins after a portfolio event surfaces. Early warning signals are non-standardised and missed. By the time risk is visible, resolution options are severely constrained.
Most credit AI tools automate individual tasks. QCAP orchestrates — agents plan, collaborate, reconcile, and reason together to produce audit-ready outcomes no pipeline can deliver.
QCAP connects natively to your existing technology stack — no rip-and-replace. Ingest from internal systems and enrich with external data bureaus through a single governed data layer.
Account & Transaction Data
Application & Credit Data
Document Repository
Financial & Operational
Cash & Liquidity Data
Policy & Compliance Data
Corporate Registry
Revenue Validation
Credit History & Scores
Market & Pledge Data
Tax Return Validation
KYC & Collateral Registry
QCAP is designed to create measurable value across every function in the credit decision chain — from deal origination to regulatory oversight.
A structured three-phase delivery model designed for enterprise risk appetite — foundations first, intelligent orchestration second, full production at scale by phase three.
LangGraph / CrewAI
GPT-4o / Llama 3
XGBoost / LightGBM
Graph Neural Networks
OCR / NLP Pipeline
Vector Database
Apache Kafka
Kubernetes (On-Premise / Hybrid)
From a 2-hour discovery workshop to a live pilot in 4–6 weeks — QCAP is engineered for fast, low-risk enterprise deployment with measurable ROI from day one.
A focused 2-hour session to map your current underwriting workflow, identify highest-impact automation opportunities, and define success metrics.
4–6 week on-site pilot on a representative credit portfolio. Live agents, real data, quantified analyst effort and TAT improvement benchmarked.
Full enterprise rollout across portfolios, business lines, and geographies — with continuous model governance and a dedicated success team.
No commitment required. Typical response within 1 business day.
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