decision.ezee
Accelerate Approvals with the AI-Driven
No-Code Credit Decisioning Software.
Launch in Weeks.
Master your risk logic. From simple rules to complex credit decision engine scoring, this credit decision engine software empowers business teams to iterate and deploy instantly.
30x Lower NPAs
Built for Every Role That
Owns the Credit Stack
decision.ezee is built for the roles that own credit speed, risk quality, and compliance readiness.
Chief Risk Officers
Decision traceability, version control, explainable outcomes. Audit-ready from day one.
Heads of Credit & Product
Launch scoring logic, eligibility rules, or pricing tiers without waiting on IT queues.
CIOs & CTOs
API-first, JSON-native, webhook-triggered. Fits your LOS and core banking stack.
CEOs & Business Heads
Faster time-to-revenue, stronger portfolio outcomes, lower operational drag.
From Rule Authoring to Live API
In One Screen
Watch how a credit policy moves from configuration to a live, callable API endpoint – no code, no dev ticket.
Author, Test, and Deploy
Credit Rules Without Code
Author, simulate, connect, and deploy. Every capability proven across 100+ institutions.
AI Rule Authoring Studio
Upload credit policy docs. AI suggests knockouts, eligibility logic, rule structure. Drag-and-drop builder.
Decision Flow Orchestration (DRD)
Chain knockouts, insights, decision tables into underwriting pipelines with conditional branching.
Expressions & Formula Engine
25+ functions for EMI, FOIR, DTI. Nested expressions, variable chaining — all configurable.
AI Scorecard Builder
Visual rule-tree scorecards. Bureau + bank statement + behavioural signals. Simulate, deploy as API.
Decision Tables & Grids
Matrix-format rate cards, fee structures, risk bands. Excel-compatible — import, edit, re-upload.
Data Source Marketplace
11+ pre-built connectors: CIBIL, Experian, Equifax, CRIF, GST, ITR, PAN, Aadhaar. Activate in minutes.
Full Control. Full Traceability.
Full Compliance
Enterprise-grade controls across the full rule lifecycle, from draft to production.
Testing & Simulation
In-UI simulation, bulk CSV, shadow/A-B testing. Validate before you switch.
API-First Architecture
Every rule = REST endpoint. JSON-native, webhook-triggered, sync + async.
Bulk & Batch Execution
Millions of records. Automated schedulers. Async queue for high-throughput jobs.
Analytics & Monitoring
Execution logs, funnel views, cohort analysis, per-entity performance metrics.
Maker-Checker Governance
4-eyes principle. Full version history. RBAC. Timestamped for regulators.
Conversational AI Decisioning
MCP exposes rules as AI-callable tools. Run decisions via plain-language requests.
Your Rules Get Sharper with
Every Decision Cycle
AI agents consume, execute, and refine your credit policies in real time, with full governance.
AI Live Policy Execution via MCP
Rules fire in milliseconds. Updates propagate instantly. No release windows.
AI Bidirectional Learning
Portfolio outcomes refine rules. Policies improve with every decision cycle.
AI Compliance & Audit Trail
Every invocation logged — timestamp, version, agent ID. RBI / APRA / MAS ready.
AI Context-Aware Decisioning
Eligibility thresholds and LTV caps delivered dynamically at every decision point.
AI Multi-Agent Consistency
One rule update propagates to underwriting, fraud, compliance, approval agents.
AI-Powered Rule Recommendations
AI suggests new rules and scoring weights from your credit documents and data.
10 Ways Institutions Turn
decision.ezee into Their Muscle
Each runs in production across banks, NBFCs, credit unions, and fintechs globally.
Credit & Risk
Loan Eligibility Rules
Knockout filters on age, income, score. Auto-reject or route in milliseconds.
Credit Scorecards
Weighted risk-tier scoring from bureau + bank statement + behavioural signals.
Fraud Detection
Document mismatches, geo-IP anomalies, velocity patterns flagged instantly.
Underwriting Flows
Full DRD pipeline: Knockout → Score → Verify → Decision Table → Credit Note.
Compliance Checks
Auto-enforce RBI, APRA, NCCP, MAS. AI flags gaps before go-live.
Automation & Ops
Interest Rate Slabs
Decision tables mapping tenure, amount, risk tier to rates. Excel-compatible.
EMI & Serviceability
FOIR, DTI, LTV computation — configurable across product types.
Product Recommendation
Auto-match borrowers to best-suited products by profile and eligibility.
Incentive & Payouts
Commission slabs, DSA payouts with approval workflows.
Pre-Approved Offers
Auto-approve on account behaviour, repayment history, AI risk scores.
Enterprise Security & Compliance
Meeting the most stringent regulatory requirements while enabling innovation
ISO 27001:2022
SOC 2 Type II
AES-256
GDPR Ready
RBAC
Deploy Your Way
SaaS
Cloud
On-Prem
Hybrid
Trusted by 100+ Banks & NBFCs Across Segments
9/28 RRBs in India Run on ezee.ai
Trusted. Proven. Recognized.
Real Implementations, Real Results
See how leading financial institutions launch credit products with unprecedented speed using ezee.ai
While You Evaluate, Competitors Accelerate
Built for Your Growth, Not Just
Your Current Size
Millions of Executions Monthly
Async queues, automated schedulers, batch execution for high-throughput without latency.
Multi-Lender, Multi-Tenant
Entity-based isolation. Each unit gets own rules, data, execution environment.
3x More Products Per Quarter
The bottleneck was never demand. It was the rule-change cycle.
Legacy BRE vs. decision.ezee
Where traditional rule engines fall short — and what changes with AI-native, no-code.
| Capability | Legacy / Traditional BRE |
|
|---|---|---|
| Rule Authoring | Code-heavy, IT-dependent | No-code, AI-assisted, business-owned |
| Deploy a Rule Change | Weeks (IT tickets + releases) | Minutes (author → test → publish) |
| Testing Before Go-Live | Manual, fragmented QA | In-UI simulation, CSV, shadow testing |
| Scorecard Management | Separate tool or spreadsheet | Built-in visual scorecard builder |
| Audit Trail | Partial or manual logging | 100% — every decision, timestamped |
| Data Sources | Custom dev per source | 11+ connectors, activate in minutes |
| AI Capabilities | None or bolted-on | Native — suggestions, learning, MCP |
| Multi-Tenant | Limited or absent | Entity-based isolation, multi-lender |
| Vendor Lock-In | High (COTS licensing) | Zero — your rules, your control |
| Excel Compatibility | Rare | Full — export, edit, re-import |
The Full Credit Lifecycle.
One Unified Ecosystem.
ORIGINATE
AI + No-Code LOS
Capture leads, run KYC, build journeys. 12 AI agents.
DECIDE
AI-Powered BRE
Author rules in minutes.
Sub-100ms. Audit-ready.
MANAGE
Servicing & Lifecycle
Real-time servicing.
Multi-product. NPA-ready.
RECOVER
Agentic AI Recovery
Predict, engage, resolve. Autonomously at scale.
20+ Loan Products. One Ecosystem.
ezee.ai in news
Lending Innovation, Explained Simply
Insights from the frontlines of digital lending transformation.
Frequently Asked Questions
What are the operational trade-offs between cloud-based and on-premise credit decisioning deployments?
| Aspect | Cloud-Based | On-Premise |
|---|---|---|
| Integration Time | Cuts setup by 70% via APIs; no hardware waits. | Slower due to server provisioning and custom configs. |
| Upfront Costs | Low; subscription model, no CapEx on hardware. | High; servers, licenses, and IT infrastructure. |
| Scalability | Effortless peak handling during loan campaigns. | Limited; requires hardware expansion. |
| Maintenance | Vendor-managed updates and security patches. | Demands dedicated IT for upgrades and backups. |
How does automated underwriting within a decision engine differ from traditional scorecard-only approaches?
Automated underwriting uses real-time data APIs and ML models for dynamic risk assessment, unlike scorecards’ static point-in-time snapshots.
Aspect Automated Underwriting Traditional Scorecards
Risk Assessment Dynamic ML models adapt to patterns Fixed scorecard points
SME Application Verifies current cashflow instantly Relies on outdated docs, fraud risk
Decision Speed Minutes via automation Days with manual review
How does automated credit decisioning shorten loan approval timelines without increasing risk exposure?
Automated credit decisioning slashes TAT by 70% through real-time data pulls from bureaus like CIBIL during KYC. It flags anomalies instantly in personal loan apps, maintaining accuracy via audit trails. Lenders see approvals in minutes without added defaults.
In what ways can rule-based and AI-driven decisioning reduce loan defaults over time?
Rule-based and AI decisioning cut defaults up to 15% by blending CIBIL checks with predictive borrower health signals in SME underwriting. Over collections, AI monitors transaction spikes for early intervention. “AI-based scoring reduces default rates by up to 15%,” notes Forrester-linked analysis.
How do lenders evaluate credit decisioning platforms for accuracy, explainability, and regulatory fit?
Lenders prioritize explainable AI with auditable logic for fair lending audits alongside ≥95% decision accuracy on post-loan performance. They test real-time CIBIL integrations for bias-free outputs in high-volume personal loans. Platforms must log every rule for compliance evidence.
What criteria do small banks and credit unions use when shortlisting credit decisioning software?
Small banks shortlist credit decisioning software based on these key criteria:
- API speed for seamless core banking handoffs and instant TAT cuts.
- Scalability without adding staff, handling growth effortlessly.
- 19% automated decision adoption aligned with compliance needs.
- Configurable rules for secured loans, no heavy IT overhead.
- Focus on TAT reductions and instant member approvals.
What regulatory requirements should credit decisioning software support in highly supervised lending environments?
Software must enable human oversight, transparent outputs, and cybersecurity for high-risk AI like credit underwriting per EU AI Act Annex III. In India, it logs CIBIL-derived decisions for RBI audits during disbursal. Changes track by authorized users only for audit-proof history.
How is credit decisioning software typically integrated into core banking, LOS, and data infrastructure?
Decisioning integrates via secure APIs pulling real-time CIBIL and CKYC data into LOS workflows for instant underwriting. It hands off approved personal loans to core banking for disbursal, with CRM syncs for collections. Modern setups create interconnected ecosystems without code rewrites.
How do lenders configure credit decisioning rules differently across personal, SME, and secured loan products?
Lenders set lighter KYC rules for low-value personal loans, heavier cashflow analytics for SMEs, and collateral checks for secured via configurable scorecards. SME rules flag transaction volatility; secured prioritize asset valuation of APIs. No-code engines adapt without recoding.
Why is API-first architecture critical for modern credit decisioning and underwriting workflows?
API-first enables seamless real-time pulls from bureaus and core systems, automating end-to-end from application to disbursal. It supports peak volumes in digital lending without latency, unlike rigid legacy setups. This cuts manual exceptions to under 15% in practice.
Is Your Decisioning Stack Ready for What's Next?
Each "yes" is a sign your current setup may be holding you back.
Select the statements that apply to your institution.
Your Next Credit Policy Could Be Live Before End of Day
See decision.ezee with your own rules, your own data, your own
compliance requirements.