A Blockchain-Based distributed network for Secure Credit Scoring

Jain, Nimit; Agrawal, Tarushi; Goyal, Pranav; Hassija, Vikas

doi:10.1109/ispcc48220.2019.8988510

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…115 Scholars have argued that a blockchain-based credit analysis framework can be effective in collecting data from various sources regarding the users and in computing a credit score based on the consensus of multiple stakeholders in the network, including banks, insurance companies, and other third-party financial institutions. 116 Some have also proposed using blockchain technology not to import information from existing financial system, but as an alternative standalone credit reporting system to record off-chain transactions and information and calculate credit scores. 117 The interest in using blockchain technology to record and calculate the credit reporting process is not purely academic.…”

Section: Off-chain Integration: Tradfi Credit Going On the Blockchainmentioning

confidence: 99%

Decentralized credit scoring: Black box 3.0

Packin,

Lev‐Aretz

2024

American Business Law J

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Much like traditional credit scoring, decentralized credit scoring calculates a borrower's creditworthiness, but the fully automated process is executed on the blockchain by Decentralized Finance (DeFi) platforms. Originally, DeFi emerged as an alternative to the centralized traditional finance (TradFi) system; however, decentralized credit scoring combines DeFi data and traditional data that include a wide range of information sources, from traditional credit reports to social media information. Despite their fairness‐oriented narrative, an examination of the business models of the protocols and entities operating in this space reveals that these hybrid scores are subject to the same algorithmic distortions that have been observed in traditional and alternative credit scoring models. Moreover, decentralized credit scores present their own distinctive set of fairness issues. Particularly, both upgrade to smart contracts and their reliance on external algorithms, known as oracles, which feed outside data, introduce heightened potential for error and bias in the credit scoring process. These “black box 3.0” issues can result in opaque automation of biased processes and perpetuate social injustices, requiring regulatory intervention to strengthen the linkage points between DeFi and TradFi and better protect consumers from the black box 3.0 consequences of decentralized credit scores.

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Section: Off-chain Integration: Tradfi Credit Going On the Blockchainmentioning

confidence: 99%

Decentralized credit scoring: Black box 3.0

Packin,

Lev‐Aretz

2024

American Business Law J

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“…Besides scoring, they also involve digital verification of customers' credentials, which is tedious. Researchers introduce a framework and create a digital identity and use block chain to calculate credit score [7]. In a distributed user network, details are transparent, which improves the trust level and plays a fair role in scoring.…”

Section: Introductionmentioning

confidence: 99%

Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model

Pan

2021

Complexity

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Credit score is the basis for financial institutions to make credit decisions. With the development of science and technology, big data technology has penetrated into the financial field, and personal credit investigation has entered a new era. Personal credit evaluation based on big data is one of the hot research topics. This paper mainly completes three works. Firstly, according to the application scenario of credit evaluation of personal credit data, the experimental dataset is cleaned, the discrete data is one-HOT coded, and the data are standardized. Due to the high dimension of personal credit data, the pdC-RF algorithm is adopted in this paper to optimize the correlation of data features and reduce the 145-dimensional data to 22-dimensional data. On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. It is found that logistic regression is more suitable for the personal credit evaluation model based on Lending Club dataset. Finally, based on the logistic regression model with the best parameters, the user samples are graded and the final score card is output.

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