Analyzing Decentralized Credit Scoring Models

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INTRODUCTION

The traditional credit scoring models are centralized systems, that use a narrow set of data to assess a borrower’s creditworthiness. They focus on the credit history, income , and outstanding debts to determine the creditworthiness of a borrower. Although this method has been used for years , it has some limitations, such as biases, limited data, accessibility and lack of transparency. Also this centralized models always exclude people in developing countries or those without access to formal banking institutions.

However, decentralized credit scoring models offer a solution to the limitations of the centralized model, by using blockchain technology and decentralized finance protocols. These models get their own data about the borrower’s creditworthiness from decentralized sources such as social behavior, utility payments etc. They also promote greater inclusion and transparency and also allow individuals to maintain control over their financial data.

The use of these decentralized models is a significant innovation in the financial system. It includes more and more people, especially those who don’t have access to banking institutions. Also, since the system is decentralized, data privacy, transparency, and accuracy is ensured.

KEY FEATURES OF DECENTRALIZED CREDIT SCORING MODELS

• DATA PRIVACY AND OWNERSHIP:

One of the important features of the decentralized credit scoring models is that they ensure data privacy and ownership. For the traditional centralized system, they rely on third parties such as banks, credit bureaus etc, for the management of individual’s financial data, and this leads to issues with privacy and data breaches. Also the people will have no control over how their data is used.

However, with the decentralized credit scoring models, the people retain the ownership to their data, and they get to decide who gets access to and how the data is being used.

As a result of this, there is reduction in the risk of data breaches , cyber attack, privacy violations etc. So it is safe to say that the decentralized credit scoring systems provide greater privacy and security for their users.

• INCLUSION OF ALTERNATIVE DATA SOURCES:

The decentralized credit scoring system has the ability to incorporate alternative data sources This is another of its advantages. For the traditional centralized system, they rely on the data from financial institutions, such as loans repayments and credit card usage. This data often times does include people without a formal credit history, and this affect individuals especially those in developing countries that don’t have access to a bank.

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The decentralized models in the other hand, gets data from different sources, such as utility payments, rental history, social behavior etc. By collecting data from this broader set, a more comprehensive and accurate assessment of an individual’s creditworthiness is provided.

Furthermore, it makes it possible for individuals without a formal credit history to still have access to credit. For example when a person pays his utility bill on time, this will show the lenders that he is reliable.

• TRANSPARENCY AND FAIRNESS:

Transparency and fairness are features of the decentralized credit scoring models. The centralized models usually lack transparency, as the people are not aware of how their credit score is being calculated. Also this centralized system can also be biased as some people tend to the tribalistic.

However, the decentralized scoring models, by creating a transparent and auditable system, using blockchain technology. There by reducing the possibility of bias and discrimination in the decision making process.

Also the decentralized credit scoring system allows individuals to track and verify their own data. The level of transparency provided helps build trust between the borrowers and the lenders, as they both can see how the credit score is being arrived at.

• IMPROVED ACCESS TO CREDIT MARKETS:

This decentralized credit scoring system ensures an improved access to credit markets for people who have always been excluded by the centralized system. Those people that lack a formal credit history are not allowed to obtain loans or mortgages in the centralized system.

However with the decentralized credit scoring system, this is not the case, as data is drawn from different sources, there by creating a more comprehensive picture of an individual’s financial behavior.

Also the decentralized credit scoring models, can connect borrowers to lenders around the world, there by removing the problem of geographical regions. This means that people can get access to loan globally. This is particularly beneficial for those in regions without banks.

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CONCLUSION

The decentralized credit scoring model is a better way of determining an individual’s creditworthiness, as it removes several limitations abd is also available to people all over the world. It also ensures privacy and security of individual’s data, as it put the individuals in control of their own data.