KiRTi: A Blockchain-based Credit Recommender System for Financial Institutions
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Product Description
Aim
The
main aim of this project to remove the third party agent between the
perspective lenders and perspective borrowers.
Abstract
In this paper, we propose KiRTi, a deep-learning based
credit-recommender scheme for public blockchain to facilitate smart lending
operations between prospective borrowers (PB) and prospective lenders (PL) to
eliminate the need of third party credit-rating agencies (CRAs) for
credit-score (CS) generation. Thus loan grants to PB from PL is secured,
authorized, and automated so as to expedite the disbursement process. KiRTi
stores PB historical transactions, current assets, and liabilities as
time-series sequenced data in a public blockchain. The sequenced data is
fetched from blockchain by a long-short term memory (LSTM) model that generates
CS for loan recommendations based on proposed lending algorithms for PB and PL.
To ensure real-time updation of CS, edge-weights are updated based on boolean
indicators from PB and PL, which indicates the successful repayments and
loan-defaults. The process is iterated to improve the accuracy of edge-weights
and generated CS to ensures the correct credibility of PB for future lending.
Smart contracts (SC) are proposed for automatic setup of loan repayments
between PB and PL. To model the LSTM recommender scheme, a German credit
dataset from UCI repository is considered with 1000 credit-histories of PB,
with 700 successful repayments and 300 defaults. KiRTi achieves an accuracy of
97.5% in comparison to conventional approaches with an Fmeasure of 0.98304. The
security evaluation of KiRTi shows that it has computation cost of 20.96 ms and
communication cost of 121 bytes compared to other state-of-the-art approaches.
In
proposed System, loan disbursement cycle is high. Perspective lenders and
perspective borrowers have direct communication. So, that they can know their
details clearly. In this approach we implement a sentiment analysis to read a
borrowers reviews. For using sentiment analysis we can find out the total
percentage of positive reviews negative reviews. Based on the reviews and
positive negative reviews percentage PL will decide to lend the loan to that
particular user or not. So that maximum users can get a loan without any
difficulties. We will store the perspective lender lending details and
perspective borrower’s payment history in block-chain.
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