Fraud Detection

AI-Powered Fraud Detection to Safeguard Every Transaction

Introduction

In today’s digital-first banking world, fraud is no longer a possibility — it’s a constant threat. Glib.ai’s advanced fraud detection platform uses real-time machine learning and behavioral analytics to proactively identify and prevent financial fraud before it impacts your business. Whether it’s identity fraud, document tampering, or transaction manipulation, Glib.ai acts as your intelligent first line of defense.

"GLIB.ai will help us assess merchant risk in a better and more efficient manner. With FinRay, we can quickly analyze key ratios from financial statements across multiple languages."

Francesco Garcia

Director - Merchant Credit Risk

Other Use Cases

Underwriting Automation Credit Scoring Fraud Detection Automated Document Processing Loan Eligibility Prediction

Key Benefits

Real-Time Anomaly Detection: Monitors millions of data points in real-time across user behavior, transaction flows, and document patterns to instantly detect suspicious activity.
Advanced Identity Verification & KYC Compliance: Cross-verifies user-provided KYC documents using 11+ AI-based checks — including face match, name matching, geo-IP validation, and document structure consistency — to identify potential fraud.
Automated Alerts & Escalations: Instantly flags irregularities and triggers rule-based alerts for human review, enabling quicker fraud containment and resolution.
Multi-Layered Security Architecture: Combines statistical models, NLP techniques, image forensics, and supervised ML algorithms to build a fraud firewall across your lending or transaction workflows.
Synthetic Identity & Document Tampering Detection: Detects morphing, cloning, AI-generated forgeries, fake Aadhaar/PAN uploads, and tampered bank statements or ITRs with high accuracy.
Compliance-Ready & Explainable: Provides audit trails, reasons for flagging, and transparent reporting to meet RBI/SEBI regulations around digital fraud management.

Human expertise + Machine intelligence

Assist, augment and automate.

Questions? Answers.

Here are some frequently asked questions. If you have any other questions, please feel free to contact us.

How does Glib.ai detect fraud using AI?

Glib.ai leverages AI to analyze transaction flows, behavioral anomalies, and inconsistencies in KYC documents and financial records. Our models are trained on large datasets of known fraud patterns and continuously learn from new signals — such as sudden changes in login behavior, mismatched document metadata, or unusual transaction timings. This allows us to catch fraud at an early stage — even before a transaction is completed.

What kinds of fraud can Glib.ai detect?

Our system is built to detect and prevent multiple categories of fraud, including: - Identity Fraud – Fake or stolen identity information - Synthetic Identity Fraud – Artificially created personas using mixed data - Document Fraud – Forged, tampered, or AI-generated KYC & financial documents - Transaction Fraud – Unusual financial behavior or suspicious fund flows - Credential Stuffing & Phishing Detection – Based on behavioral biometrics and login pattern monitoring Glib.ai provides BFSI firms with a 360° defense against both traditional and emerging fraud vectors.

How fast can Glib.ai identify and flag fraud attempts?

Speed is critical. Our system operates in real-time, with detection and alerts typically triggered within milliseconds to seconds of identifying a threat signal. This immediate response helps banks and NBFCs block or flag transactions proactively — before money moves or fraud is completed.

Can Glib.ai integrate into our current banking or lending platforms?

Yes — Glib.ai is designed to plug into your existing tech stack. We provide secure, well-documented APIs and SDKs for integration with: - Core Banking Systems (CBS) - Loan Origination Systems (LOS) - CRM or DSA dashboards - Digital KYC modules Integration timelines usually range from a few days to a couple of weeks, depending on system complexity.

How does AI fraud detection outperform traditional rule-based systems?

Traditional fraud systems rely on pre-set rules — for example, flagging a transaction above a certain amount or from a specific geography. However, fraud patterns today are dynamic, subtle, and often engineered to bypass fixed rules. Glib.ai uses adaptive machine learning to evolve its detection engine, identifying: - Subtle deviations in user patterns - Complex behavioral correlations - Image-based tampering (e.g., signature mismatches, document layout inconsistencies) This results in fewer false positives and higher fraud catch rates, giving your compliance team more precision and control.

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