Business impact Banks & Financial Institutions
Apply the same intelligence foundation to the decisions and workflows that matter most to this segment.
AI-Powered Bank Statement Analysis for Faster, Safer Credit Decisions
Convert scanned or digital bank statements, Account Aggregator JSON and banking feeds into consolidated cash-flow intelligence. Identify income, obligations, spend patterns, bounces, related-party flows, anomalies and document risks across multiple accounts.
- Multi-account consolidation
- AI-OCR + transaction intelligence
- Ready-to-use credit reports
Document intelligence by 
within the Cygnet Finalyze experience
within the Cygnet Finalyze experience
Challenges this solution is built to solve
A bank statement can reveal repayment capacity and financial behavior, but manual review becomes slow and inconsistent when borrowers submit multiple accounts, long transaction histories and mixed document formats.
- Multiple accounts, no single view
Analysts manually merge transactions and may miss related-party or inter-account movements. - Inconsistent and scanned formats
Statements, passbooks, images, and e-PDFs vary by bank, layout, language, and quality. - Manual transaction classification
Income, EMIs, returns, charges, cash deposits, and risky spends require line-by-line interpretation. - Fraud and anomaly risk
Tampering, synthetic income infusion, circular transactions, and unusual cash patterns may remain hidden.
HOW IT WORKS From raw data to a governed business decision
The bank statement analyzer combines GLIB.ai document intelligence with Cygnet Finalyze risk workflows to move from raw statements to review-ready credit insights.
1
Secure Intake
Accept digital PDFs, scans, passbooks, images, AA JSON, or direct banking feeds.
2
AI Extraction
Read account details, balances, and every transaction using OCR, layout understanding, and validation.
3
Transaction Intelligence
Categorize income, spends, EMIs, transfers, bounces, cash activity, and other financial behavior.
4
Reconcile & Flag
Consolidate accounts, identify duplicates and related flows, and prioritize anomalies or risk indicators.
5
Report & Integrate
Generate configurable Excel, JSON, or XML outputs for credit assessment and downstream decisioning.
AI-first architecture Intelligence that understands, evaluates and acts
AI is used where it creates measurable value: structuring data, identifying context, detecting risk, explaining findings and routing exceptions, while keeping people and policy in control.
Layer 01 Document & Authenticity Layer
Processes physical and digital statements while checking format, sufficiency, vintage and possible tampering indicators.
- Scans & e-PDF
- OCR validation
- Document checks
Layer 02 Transaction Intelligence Layer
Understands transaction context to identify income, obligations, expenses, bounces, cash flow and behavior.
- Income verification
- Spend analysis
- EMI detection
- Cash-flow metrics
Layer 03 Credit Action Layer
Converts findings into product-specific reports, risk signals and reviewer actions for lending workflows.
- Risk flags
- Custom reports
- Policy mapping
- API outputs
Published impact from automated bank statement processing
Focus teams on decisions, exceptions and customer outcomes rather than repetitive data preparation.
Up to 98%
Reduction in processing time cited on the current Cygnet bank statement analysis page.
80% TAT reduction
Published Cygnet customer outcome for a finance company’s loan-processing journey.
Seconds, not hours
Automated extraction and analytics reduce repetitive manual review for standard cases.
Designed for BFSI, NBFCs and corporates
Use one intelligence foundation across lending, risk monitoring and counterparty assessment, with the workflow and controls required by each segment.
Where it helpsPriority use cases
- Underwrite retail, SME, home, auto, and secured loans
- Verify income and identify financial obligations consistently
- Support insurance underwriting and financial profiling
- Strengthen fraud review and credit monitoring
Operating modelWhat changes for teams
- Fewer manual preparation steps
- Consistent evidence and risk logic
- Exception-led analyst review
- Structured integration into existing systems
Business impact NBFCs & Digital Lenders
Apply the same intelligence foundation to the decisions and workflows that matter most to this segment.
Where it helpsPriority use cases
- Handle high-volume statements without proportional operations growth
- Enable self-service and Account Aggregator journeys
- Generate product-specific CAM and risk outputs
- Improve straight-through processing for clean cases
Operating modelWhat changes for teams
- Fewer manual preparation steps
- Consistent evidence and risk logic
- Exception-led analyst review
- Structured integration into existing systems
Business impact Corporates & Large Enterprises
Apply the same intelligence foundation to the decisions and workflows that matter most to this segment.
Where it helpsPriority use cases
- Assess customer or distributor creditworthiness
- Review vendor cash-flow health before exposure
- Support supply-chain finance and channel lending
- Analyze corporate expense and treasury patterns
Operating modelWhat changes for teams
- Fewer manual preparation steps
- Consistent evidence and risk logic
- Exception-led analyst review
- Structured integration into existing systems
Why Cygnet Finalyze Move beyond disconnected data tools
The differentiator is not AI as a label. It is verified data, specialized document intelligence, explainable risk logic and integration into the decisions your teams already make.
Traditional approach
- OCR that stops at raw extraction
- Manual consolidation across bank accounts
- Generic output without lending context
- Limited cross-document validation
- Analyst review of every transaction
AI-native Cygnet approach
- Context-aware transaction categorization
- Multi-account consolidation and flow detection
- Income, obligation, cash-flow and anomaly intelligence
- Cross-validation with GST, ITR, bureau and payslips
- Exception-led review with configurable reports
Governed AI Governed AI Enterprise control around every AI-assisted output
Support faster decisions without turning models into a black box. Review, override, trace and integrate outputs within your own governance model.
Human Review Controls
Analysts can validate and override low-confidence or high-risk classifications.
Configurable Validations
Set required vintage, sufficiency, account ownership and product rules.
Data Protection
Support encryption, masking and configurable retention or purging requirements.
Explainable Signals
Show the transactions and patterns behind each material risk indicator.
Audit Trail
Retain input, extraction, categorization, review and output history.
Deployment Flexibility
ntegrate through APIs and align deployment to enterprise security requirements.
- Account Aggregator
- LOS / LMS
- Core Banking
- Credit Bureau
- GST & ITR data
- Payslip analysis
- CRM / customer portal
- Excel, JSON, XML
Frequently asked questions
It can process digital and scanned bank statements, passbooks, smartphone images, e-PDFs, AA JSON and supported direct banking feeds.
It goes beyond extraction to provide multi-account analysis, income verification, spend categorization, obligation detection, cash-flow metrics, bounces, anomaly signals and configurable reports.
Yes. Related accounts can be combined into a consolidated view while inter-account transfers and duplicate flows are identified to avoid overstating income or turnover.
GLIB.ai provides the document and transaction intelligence used to process bank statements. Cygnet Finalyze connects those insights with broader credit, GST, ITR and risk workflows.
Yes. API-led integration can return structured data and reports to LOS, LMS, core banking, CRM or custom underwriting systems.
Low-confidence classifications, missing periods, anomalous transactions and policy-sensitive signals can be routed to analysts for review and override.
Explore the platform Related Cygnet Finalyze offerings
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