Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM61826

A Next Gen Loan Prediction Model - Trustify

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM61826

Volume:

10 |

4 |

April 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM61826

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

AS

Ananthakrishnan S

computer science and engineering

Nehru college of engineering and research center

Download Author Certificate
AS

Anagha S

computer science and engineering

Nehru college of engineering and research center

Download Co-Author Certificate
AN

Akash NS

computer science and engineering

Nehru college of engineering and research center

Download Co-Author Certificate
AM

Arjun M

computer science and engineering

Nehru college of engineering and research center

Download Co-Author Certificate

Mentors

SJ

Shinu Jacob

computer science and engineering

Nehru college of engineering and research center

Download Mentor Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

The rapid growth of digital banking has created a need for secure, efficient, and automated loan management systems. Traditional loan processing methods are time-consuming, error-prone, and lack transparency. This paper proposes an AI and Blockchain-based Smart Loan Management and Verification System that integrates Artificial Intelligence (AI), Blockchain technology, and automated document processing. The system enables secure storage of loan records using blockchain, ensuring immutability and tamper-proof verification. AI techniques are used for document summarization, financial analysis, and loan eligibility prediction. Additionally, deep learning models are incorporated to detect forged or manipulated documents such as deepfake images. The proposed system improves efficiency, enhances security, reduces manual intervention, and ensures transparency in loan processing. This approach provides a reliable and scalable solution for modern banking systems.

KEYWORDS

Key Words: Blockchain Smart Loan Artificial Intelligence Document Verification Deepfake Detection Financial Analysis.

Cite This Article

Ananthakrishnan S, Anagha S, Akash NS, Arjun M, Shinu Jacob, "A Next Gen Loan Prediction Model - Trustify," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-7, April 2026. https://doi.org/10.55041/IJSREM61826

Full Article/Conclusion

The proposed Smart Loan Management and Verification System provides an intelligent and efficient solution for modern loan processing. The system automates the loan process by integrating Artificial Intelligence, Blockchain technology, and document processing within a web-based platform. By improving efficiency, transparency, and security, the system simplifies loan application, verification, and approval procedures. The AI-driven modules analyze financial data, generate reports, and predict loan eligibility, ensuring accurate and consistent decision-making. The system also enhances security through blockchain-based storage and SHA256 hashing, which ensures tamper-proof verification of documents. Fraud detection mechanisms using deep learning further strengthen the system by identifying manipulated or fake documents. Through its role-based access, the system improves coordination between users, banks, and administrators while maintaining organized records and transaction history. This enables faster loan processing and more reliable verification. Overall, the Smart Loan System demonstrates the potential of combining AI and Blockchain technologies to transform traditional loan management into a more automated, secure, and transparent process. The system provides financial institutions with a reliable platform for efficient and scalable loan management in modern digital environments.

References

1. S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. 2. V. Buterin, “A Next-Generation Smart Contract and Decentralized Application Platform,” Ethereum White Paper, 2014. 3. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. 4. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2010. 5. R. Smith, “An Overview of the Tesseract OCR Engine,” Proceedings of ICDAR, 2007. 6. A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems, 2017. 7. K. He et al., “Deep Residual Learning for Image Recognition,” IEEE CVPR, 2016. 8. T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” 2013. 9. D. Chaum, “Blind Signatures for Untraceable Payments,” Advances in Cryptology, 1983. 10. J. Benet, “IPFS – Content Addressed, Versioned, P2P File System,” 2014. 11. P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” NIST, 2011. 12. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” 2016. 13. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, 2015. 14. A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” 2012. 15. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” 2016. 16. J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” 2008. 17. M. Swan, Blockchain: Blueprint for a New Economy, O’Reilly, 2015. 18. N. Kshetri, “Blockchain’s Roles in Strengthening Cybersecurity,” Computer, IEEE, 2017. 19. A. Dorri, S. Kanhere, and R. Jurdak, “Blockchain in Internet of Things: Challenges and Solutions,” IEEE, 2017. 20. R. Jain and S. Sharma, “AI-Based Loan Prediction System Using Machine Learning Techniques,” International Journal of Computer Applications, 2020.

Editorial & Review Transparency

Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

3

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...