• Mandava Sandeep
• Mallu Yashwanth Reddy
• Mrs. V Subapriya
• N. Senthamilarasi

Rather than relying on third-party rating agencies for credit scores, this paper proposes KIRTi, a deep-learning credit-recommender system based on public blockchain. PBs and PLs will be able to make smart loans to each other under the scheme. By securing, approving, and automating the loan grant process, PL was able to speed up the disbursement process to PB. As PB's assets, liabilities, and transactions are recorded on a public blockchain, KiRTi keeps track of their past operations. Based on the suggested lending algorithms for both PB and PL, a long-short memory (LSTM) model retrieves and processes blockchain sequence data.We update CS's edge weights in real-time based on PB and PL's boolean indications of repayment success and loan default. Iterating the procedure gets better edge-weights and CS, ensuring that PB is credible enough for future loans. In order to automate loan repayments, it is suggested that PB and PL use smart contracts (SC). LSTM recommender systems are trained by using German credit datasets from the UCI repository. PB's credit histories are contained in this dataset. 700 of these have been repaid successfully, and 300 have defaulted. Using KiRTi, 97.5% accuracy can be achieved with Fmeasure 0.98304. Based on the security assessment, KiRTi's compute and communication costs are 20.96 ms and 121 bytes respectively.

Drug-related, Attack-related content, Positive online environment, Support Vector Machines (SVMs), Random Forests (RFs) I

"The Credit Recommender System Using Block Chain", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.b116-b122, April-2024, Available :http://www.jetir.org/papers/JETIR2404114.pdf

"The Credit Recommender System Using Block Chain", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppb116-b122, April-2024, Available at : http://www.jetir.org/papers/JETIR2404114.pdf