AI-DRIVEN FRAUD DETECTION AND VERIFICATION SYSTEM FOR GOVERNMENT LOAN WAIVERS

Volume no :

6 |

Issue no :

1

Article Type :

Research Article

Author :

Mr.Murugan.K, S. Abhilash, K.Abijith, C.P.Abijth, C AdilAbdulNassar

Published Date :

May, 2025

Publisher :

Kiwi Publishers.

Page No: 1 - 13

Abstract : Government loan waiver schemes are critical tools for providing financial relief to distressed farmers and economically weaker sections. However, these schemes are often plagued by fraudulent claims, misidentification, and lack of transparency, leading to financial losses and ineffective policy implementation. This project proposes an AI-driven fraud detection and verification system to ensure the integrity and efficiency of government loan waiver disbursements. By leveraging machine learning algorithms, biometric authentication, and data analytics, the system identifies anomalies, cross-verifies beneficiary details across multiple government databases, and flags suspicious entries in real time. Natural Language Processing (NLP) techniques are also incorporated to analyze supporting documents and detect forged or inconsistent information. The system is designed to be scalable, secure, and adaptable to various state and central loan waiver programs, ultimately aiming to minimize fraud, improve governance, and ensure that benefits reach the genuinely eligible recipients.

Keyword: Aadhar, Smart Card, YOLOv8, Tesseract OCR, Capsule Networks, Identity Verification, Government Schemes, Fraud Detection, Deep Learning, UIDAI Integration, Optical Character Recognition, Document Security, API Cross-Verification, Image Preprocessing.

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INTRODUCTION
Government loan waiver schemes play a crucial role in promoting social welfare and alleviating
the financial burden on vulnerable populations, such as small-scale farmers and economically
disadvantaged citizens. However, the traditional document verification processes used in these
schemes are primarily manual and prone to inefficiencies. AI-DRIVEN FRAUD DETECTION is the focused keywords
These outdated systems are vulnerable to fraud, human error, and bottlenecks caused by
increasing application volumes. Current methods often require applicants to submit physical
copies or scanned versions of identification documents, which are then manually verified by
government clerks. Such processes are time-consuming, inconsistent, and offer limited
scalability.

Related Works

AI-DRIVEN FRAUD DETECTION

To address these pressing issues, this project proposes a cutting-edge AI-based identity verification system. By automating every stage of verification, from detecting and extracting identity information in uploaded documents to validating it through official government databases, the system ensures seamless processing. Furthermore, its advanced capabilities guarantee that only genuine and eligible individuals receive benefits under government loan waiver programs. With a focus on real-time processing, intelligent fraud detection, and secure infrastructure, the system sets a new benchmark for efficiency and accuracy in public sector service delivery.
Literature Survey:

Over the past decade, various initiatives have attempted to digitize identity verification
workflows, especially in the context of public services. Early implementations utilized basic OCR
software and static image upload portals for document handling. While these were significant
advancements over fully manual processes, they still lacked the intelligence needed to detect
forged documents or perform real-time validation with official records.
Previous systems often faltered due to poor scalability, limited automation, and an inability to
distinguish between authentic and tampered documents.
Recent studies have explored the application of deep learning models in document analysis and
fraud detection, achieving some success in specialized domains like banking and finance.
However, few solutions have been designed specifically for government schemes requiring
large-scale, multilingual, and region-specific document processing. Additionally, integration
with national identity databases such as UIDAI remains rare in these systems.
This project builds upon and advances the existing body of work by combining object detection,
OCR, and advanced forgery analysis into a cohesive and scalable verification platform.

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Fig1: Bolck of AI-DRIVEN FRAUD DETECTION