The Problem Landscape

Manual loan processing is slow, error-prone, and vulnerable to fraud—leading to high customer drop-off and rising operational inefficiencies, especially in emerging credit markets

Operational Delays Systemic Vulnerabilities to Fraud and Data Inconsistencies

Surge in Fraudulent Activities: According to the Reserve Bank of India (RBI),

Turnaround Time (TAT): Traditional banks and NBFCs typically take 2–5
the total amount involved in bank frauds increased threefold to ₹36,014
days to manually verify loan documents, leading to operational
crore in FY2024-25, up from ₹12,230 crore in the previous fiscal year
bottlenecks.

Digital Payment Frauds: In FY2024-25, there were 13,516 fraud cases related
Operational Costs: Manual processing incurs costs ranging from ₹200–
to cards and internet transactions, constituting over 56% of all reported
₹500 per application, depending on staffing and complexity.
fraud cases

High Dropout Rates During Verification Metric Traditional Banks NBFCs Digital Lenders

Average Processing
2–5 days 1–3 days Under 5 minutes
Abandonment Rate: Over 30–40% of applicants discontinue the process at Time
the document upload or KYC stage. Operational Cost per
₹400–₹500 ₹300–₹400 ₹50–₹100
Loan
Primary Reasons: Poor user experience during document upload, delays or
Customer Drop-Off Rate 30–40% 25–35% 10–15%
lack of clarity in processing, and repetitive requests for the same
documents. Fraud Detection Rate 70–75% 75–80% 90–95%

Regional Segmentation: Credit Distribution Trends

There's a growing need for efficient, scalable, and fraud-resistant loan processing systems in semi-urban and rural regions 1
The share of bank credit from metropolitan branches declined from 63.5% in 2020 to 58.7% in 2025, indicating increased credit growth in non-metropolitan and
rural areas
Strategic Objective
From Document Upload to Decision in Minutes — Secure, Scalable, Seamless

Vision Statement
To redefine the loan origination journey through a hyper-automated, intelligence-driven document processing system, minimizing
human intervention, enhancing risk visibility, and delivering superior borrower experiences across retail and MSME segments.

Core Objectives
KPI Current Baseline Target with IDP
Reduce Decisioning Time by 98%
From an industry average of 48–72 hours to <5 minutes per application Avg. TAT (Time to
48–72 hours <5 minutes
Driven by real-time OCR, data structuring, and instant rule-based eligibility Decision)
scoring
Straight-Through
Achieve Straight-Through Processing (STP) of ≥70% ~35% ≥70%
Processing (STP)
End-to-end automation of application intake to decisioning for at least 7 out of 10
applications Manual Processing
~80–90% ≤20%
Industry average is ~30–40% STP among NBFCs and digital lenders (McKinsey, 2023) Ratio
Enhance Operational Efficiency by Reducing Manual Touchpoints by ≥80%
Document Fraud
Shift human effort from low-value document validation to exception handling and ~65% ≥90%
Detection Accuracy
risk anomaly reviews
Reduces cost per application from ₹400–₹500 to ₹75–₹100 (RBI, 2024; BCG reports) Drop-Off Rate
Improve Approval Accuracy and Risk Detection with AI-Enabled Insights During Document 30–40% <15%
Integrate ML-based rule engines to reduce false positives/negatives Upload
Target ≥90% document authenticity accuracy and ≥95% anomaly detection rate
Boost Customer Conversion by ≥25%
By reducing drop-offs during the documentation phase By integrating IDP into the loan lifecycle, we aim to redefine operational
Through seamless UX, guided uploads, DigiLocker integration, and instant eligibility efficiency, ensure regulatory-grade compliance, and deliver a customer
results experience that is instant, intelligent, and industry-leading
Industry Benchmarks & Existing Tools
Learning from lenders - mapping exisitng iDP implements

Platform Core Capabilities Implementation Impact Gaps / Opportunities

High dependency on structured data; lacks

Bank statement parsing, cash flow analysis (500+ Adopted by 200+ BFSI clients; Reduced income
contextual NLP for unstructured documents like
data points) verification time from 48 hours to 2–5 minutes
handwritten forms
KYC verification, AML checks, bureau integration, 99.7% OCR accuracy; Reduced fraud rates by API cost is high for early-stage lenders; limited
fraud signals ~40% for partner NBFCs custom scoring flexibility

Requires frequent human validation for noisy

OCR + ML on invoices, IDs, pay slips, semi- 95%+ field extraction accuracy; used by Open,
images; lacks Indian vernacular document
structured forms PayU, and Indifi
adaptability
Accuracy drops by 15–20% on scanned multi-
Drag-and-drop IDP model training; template-less 90–95% accuracy on clean documents; model
lingual documents; limited support for real-time
document processing trains in <30 minutes
loan decisioning
Focused on onboarding only; does not support
Video KYC, face match, ID validation (SEBI/RBI Used by ICICI, SBI, Axis; Cut onboarding time by
downstream risk scoring or rule-based
compliant) 70%
decisioning

Platform Impact Summary (Industry-wide): Gaps to Address / Our Differentiation:

Average TAT reduction: From 3–5 days → Under 5 minutes Context-Aware Parsing: Our NLP engine will interpret semi-structured + unstructured content (e.g.,
OCR Accuracy Range: 90–99.7% (varies by document type PDF notes, handwritten slips) with multilingual support, currently under-served by existing players
and platform) Decision-Grade Intelligence: Instead of stopping at KYC or field extraction, our rule-based and ML-
Fraud Risk Reduction: Up to 40% with multi-layered powered scoring engine will instantly categorize applications into Approve/Reject/Review classes
checks Affordability for NBFCs & Rural Lenders: Delivering value with <20% lower operational cost than
Manual Processing Cost Cut: 30–60% across early top vendors by using open-source OCR + custom-trained lightweight models, especially for Bharat-
adopters (McKinsey, 2022) focused applications.
Competitive Landscape
While most digital lenders demonstrate scale and speed, only a few like Navi Finserv exhibit true maturity in Intelligent Document Processing, revealing a clear whitespace for
precision-led, fully automated loan decisioning

KreditBee CASHe
High
A tech-driven digital lender focusing on salaried
Targets gig and blue-collar workforce; offers
millennials, KreditBee leverages OCR-based
short-term credit up to ₹4L with a heavy

Depth of IDP Automation

KYC and bank statement parsing, but manual
reliance on social profiling and mobile
fallback rates remain >35%, indicating limited
metadata. IDP involvement is limited to
IDP depth. With over ₹15,000 Cr disbursed to
surface-level OCR. Manual verifications
date and 50M+ app installs, it shows scale but
persist in ~40% of applications, limiting
lacks high-precision document intelligence and
scalability in regulated lending
real-time fraud detection layers

ZestMoney EarlySalary (now Fibe)

Primarily a BNPL player with ~17M registered
Processes ~80K applications/month with
users and presence across 80,000+ merchant
average approval time of <10 minutes.
partners, ZestMoney’s IDP utility is minimal—
Employs hybrid models combining OCR,
restricted to PAN/Aadhaar OCR. Its credit
employment pattern detection, and bank
decisioning hinges on behavioral and
statement classification. IDP sophistication is
transactional scoring rather than dynamic Low moderate; however, integration of deep
document parsing. High scalability, but low IDP
document fraud analytics is still emerging
sophistication Low Breadth of Product Coverage High
Navi Finserv LazyPay
With an in-house tech stack, Navi executes By integrating a scalable, real-time IDP framework with A sub-brand of PayU, LazyPay leverages
underwriting in <5 minutes and offers loans up parent infrastructure for agile loan disbursals
to ₹20L. Its full-stack IDP pipeline, incorporating modular, intuitive UX layers, our solution targets the (~90 seconds approval) but relies more on
real-time OCR, rule-based evaluations, and ML- underserved quadrant—high-tech, high-trust lending—to transaction history and e-commerce
assisted verification, has reduced manual footprint than document intelligence. IDP is
interventions by ~70% YoY. A clear leader in IDP
bridge the gap between compliance, speed, and user minimal and tailored toward consumption
maturity among neo-lenders delight. credit, not income-based lending
IDP-Driven Workflow
Our intelligent workflow fuses advanced IDP, multilingual NLP, and ML-driven risk intelligence to bridge the operational and inclusivity gaps

Multi-Format Advanced Document

Application Initiation:
User inputs personal,
Document Upload: Digitization:
Supports PAN, Aadhaar, OCR engines like Google
employment, and loan-specific
salary slips, Form 16, bank Vision/Tesseract extract
details via mobile/web app.
statements unstructured data

Real-Time Risk Profiling via Rules Semantic Parsing &

Decisioning Output: Engine + ML Ensemble: Validation:
Classification into Approved / Hybrid engine using rule-based Parses textual data and maps to
Rejected / Manual Review filters with supervised ML for semantic fields: Income, Name,
categories within <60 seconds default probability scoring IFSC, PAN, etc.

Feedback Loop and Refinement:

Every rejected or manually reviewed application is used to retrain models quarterly.

Gap in Current Platforms Our Enhanced Workflow

Reliance on static rules for eligibility Dynamic rule thresholds & ML-based confidence scoring

Inability to process vernacular/regional docs Multilingual OCR & NLP trained on Tier 2/3 datasets

Lack of cross-document semantic validation Deep contextual validation across submitted documents

Manual fallback for edge-case applicants Alternate data-backed fallback with behavioral scoring

Limited transparency to applicants Real-time explainability dashboard for user trust

Key Data Validations
Precise data validations at each document touchpoint ensure decision accuracy, reduce NPAs, and enhance trust in the automated underwriting process

20 10

Validation Pipeline 15 8

TAT in minutes

Error Rate %
6
10
ID Authentication 5
4

improves validation success rates by up to 2

92% (RBI Financial Inclusion Report, 2022) 0 0

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over 34% of loan rejections stem from
income document mismatch McKinsey Global Banking Report 2023 notes IDP-based platforms like Navi with custom
best-in-class digital lenders bring TAT below OCR models report lower rejections due to
5 mins, whereas traditional takes 2–5 days clear rules and retraining capabilities
DTI & Risk Ratios
<40% for salaried, <50% for self-employed
Kreditbee
Kreditbee Zest Money

Detection rate
% Automated
Navi Finserve

Employer Mapping CASHe

More than 12 months with current Zest Money
employer correlates with 23% lower Early Salary Navi Finserve CASHe Early Salary LazyPay
default risk (TransUnion CIBIL Study, 2023) LazyPay
0 20 40 60 80 100

Document Legitimacy
Experian India notes over 15% of lending
ML-powered tools can flag up to 86% of Deloitte Digital Lending Report states top-
tier lenders automate over 80–90% of rejections stem from forgery or identity
edited salary slips and IDs manipulation; high detection score =
disbursals via IDP + rule engine
Low document clarity = 1.7× more likely for higher risk coverage
application rejections
Intelligent Loan Journey
A seamless, intelligent loan approval journey powered by automated document validation, delivering real-time decisions with minimal user friction

Smart Document IDP & Document Decision Engine

Intent Capturing Loan Configuration
Upload Matching
Automated Parsing: PAN, Rule Execution: 20+
Touchpoint: User lands on User Input: Personal info,
Channels: Manual upload / Aadhaar, salary slips, Form parameters including DTI
the loan application employment type, loan
DigiLocker / Email scrape / 16, bank statements ratio, credit age, income
interface via web/app. amount, tenure, purpose.
WhatsApp BOT interpreted using ML volatility, and document
Tech Feature: Smart IDP Intelligence: Input
Backend Action: OCR + Name Consistency across authenticity score
autofill via mobile KYC sanitization + inline fraud
Preprocessor (deskewing, ID documents: ≥98% Decision Time: <30
plugins or Aadhaar-linked flag triggers (e.g.,
noise removal) with 96% match threshold. seconds for 80% of cases
data APIs. disposable email,
field-level extraction Income Proof Validation: (avg. STP efficiency =
Risk Mitigation: Drop-off mismatched mobile
accuracy Salary credit consistency 72.5%)
alerts and adaptive UI operator geography).
Risk Strategy: Checks for >90% across last 3 Contingency Checkpoint:
based on inactivity Contingency Avoidance:
duplicate submissions, months. Borderline cases routed to
analytics (avg. 20% Real-time feedback if
expired docs, image Employment Tenure: manual triage queue
improvement in income-to-loan mismatch
quality score <80% (re- Highlight if <6 months, within <10 minutes
completion rate – Deloitte crosses 40% DTI
prompt user immediately) triggering ‘manual (ensures no friction at
Digital Lending 2023) threshold.
override’ flag. decision boundary)

Outcome Communication & Guided Next Steps

Approved: Pre-filled sanction letter + next step CTA (auto-sign + e-mandate) Rejected: Transparent dashboard of failure points (improves re-application by 25%)
Needs Correction: Highlight specific document/field and allow immediate re-upload, significantly improving customer experience and conversion rates
Future-Ready Lending Powered by IDP
IDP is not a feature — it's an engine for long-term competitive advantage

Contingency Management
Risk Type Detection Rate Action Protocol

Reject + CIBIL blacklist (30%

Approval Workflow Triggers:
Identity Fraud 98.70%
Auto-approve: DTI <45% + CIBIL >750 + doc score ≥95% (68% of
recurrence reduction)
cases)
Flag for video KYC + metadata
Document Tampering 92.40% analysis (85% verification time
Hybrid review: DTI 45-55% + doc score 80-95% (22% of cases; 14%
reduction) approval rate)
Full audit: High-risk flags (10% of cases; 92% rejection rate)
Route to hybrid review (AI + human)
Income Fabrication 88.50%
with 72% rejection rate Financial Safeguards:
Earnest money protection: Auto-refund 1-3% deposit if loan
contingency fails (30-60 day clauses)
Contingency/Gray Resolution
Stage
Area
Prevalence
Mechanism
Success Rate Rate lock hedging: 85% success in securing ≤8.5% interest rates via
real-time market integration
Non-standard NLP parsing, hi-res
Document Upload 15-20% 89%
formats, poor scans rescan
Compliance & Risk Metrics
Handwritten/unclear Human-in-loop
Data Extraction 6-10% 85%
fields review

KYC/AML compliance: 99.4% adherence via auto-flagging of:

Identity Name/address Cross-check with
3-5% 98% Mismatched addresses (97.1% resolution rate)
Verification mismatch Aadhaar/PAN
Suspicious transaction patterns (82% detected pre-disbursement)
Salary slip vs. bank Hybrid review, ML
Income Validation 8-12% 88%
statement mismatch anomaly flag Regulatory penalties avoidance: 4.8% annual loss reduction
via audit trail automation
Synthetic or stolen AI/ML + biometric
Fraud Detection 2-4% 98%
identity KYC