scholarly journals Determining Personal Credit Rating through Voice Analysis: Case of P2P loan borrowers

2021 ◽  
Vol 15 (10) ◽  
Keyword(s):  
Credit Rating ◽  
Voice Analysis ◽  
Personal Credit

scholarly journals Give us a little social credit: to design or to discover personal ratings in the era of Big Data

2020 ◽  
Vol 16 (3) ◽  
pp. 369-387 ◽  
Author(s):  
Abigail Devereaux ◽  
Linan Peng
Keyword(s):  
Big Data ◽  
Credit Rating ◽  
Credit Ratings ◽  
Personal Characteristics ◽  
The State ◽  
State Council ◽  
Credit System ◽  
Personal Credit ◽  
Social Credit ◽  
Financial Credit

AbstractIn 2014, the State Council of the Chinese Communist Party announced the institution of a social credit system by 2020, a follow-up to a similar statement on the creation of a social credit system issued by the State Council in 2007. Social credit ratings of the type being developed by the State Council in partnership with Chinese companies go beyond existing financial credit ratings in an attempt to project less-tangible personal characteristics like trustworthiness, criminal tendencies, and group loyalty onto a single scale. The emergence of personal credit ratings is enabled by Big Data, automated decision-making processes, machine learning, and facial recognition technology. It is quite likely that various kinds of personal and social credit ratings shall become reality in the near future. We explore China's version of its social credit system so far, compare the welfare and epistemological qualities of an ecology of personal ratings emanating from polycentric sources versus a social credit rating, and discuss whether a social credit system in an ideologically driven state is less a tool to maximize social welfare through trustworthiness provision and more a method of preventing and punishing deviance from a set of party-held ideological values.

scholarly journals PERSONAL CREDIT RATING FORECASTING USING ML.NET

2020 ◽  
Vol 481 (3) ◽  
pp. 63-71
Author(s):  
Dmytro S. Antoniuk ◽  
Tetiana A. Vakaliuk ◽  
Galyna V. Marchuk ◽  
Vladyskav V. Didkivskyi
Keyword(s):  
Credit Rating ◽  
Personal Credit

Building a Conceptual Model of Factors affecting Personal Credit and Insolvency in China based on the Methodologies used in Western Economies

2012 ◽  
pp. 204-215
Author(s):  
Grzegorz Majewski ◽  
Abel Usoro ◽  
Pattarin Chumnumpan
Keyword(s):  
Conceptual Model ◽  
Credit Rating ◽  
Chinese Economy ◽  
Lessons Learned ◽  
Chinese Society ◽  
Factors Affecting ◽  
Personal Credit ◽  
Goods And Services ◽  
Evaluation Procedures ◽  
Substantial Risk

Chinese economy is developing at an unprecedented pace. This expansion is prominent not only in the external aspect (increased export), but also internally in the increase in the demand for goods and services by common Chinese families. This demand cannot always be met by the monthly salary and therefore the need for personal credit. Because of the substantial risk involved in lending, there is need for robust and reliable credit evaluation procedures, strategies, policies, and systems. Lessons learned from the subprime mortgage crisis in U.S. are that lending can be a very risky activity that can lead to recession for a whole economy. Banks and other financial institutions in China are in need of appropriate procedures and systems should a barrier to further economic development be avoided. Besides, existing models and systems that are prevalent in the West may not fully match Chinese banking environment or the society itself. An appropriate personal credit rating methodology should take into account the differences between the Western and Chinese society and culture. There apparently does not exist such a methodology in literature that takes into consideration the unique Chinese situation. The aim of this chapter is to begin to fill this gap in knowledge by building a conceptual model of factors influencing demand for consumer credit and insolvency (bad debts) in China, based on the available methodologies used in the Western societies.

THE ANALYSIS OF THE SECTOR OF RETAIL LENDING OF RUSSIAN BANKS DURING THE PANDEMIC PERIOD OF 2020

2021 ◽  
pp. 41-46
Author(s):  
E. P. Ramzaeva ◽  
◽  
O. V. Kravchenko ◽  
◽  
Keyword(s):  
Interest Rates ◽  
Commercial Banks ◽  
Global Economy ◽  
Credit Rating ◽  
Significant Risk ◽  
Personal Credit ◽  
Debt Burden ◽  
Consumer Lending ◽  
Study Results ◽  
Key Indicators

Lending to the population is an essential part of the country’s economic system, its development in the global financial market. Due to the steady downward trend in economic growth and real incomes of the population, the spread of the coronavirus pandemic in the Russian Federation in 2020, the issue of fulfillment by the borrowers their obligations to banks became relevant. Many consumers of credit services were unable to fulfill their obligations in full and in due time, which led commercial banks to the most significant risk – credit one. The study considers the main aspects of the functioning of the Russian consumer lending market in the context of the lockdown of the global economy, analyzes the factors, which determined both the growth and contraction of the market under the pandemic influence. The study assessed the dynamic changes in key indicators determining the state of this economic sector in 2020. The authors analyzed the dynamics of granting retail credits and the dynamics of overdue debt. The paper considered the level of the debt burden of the population and the indicators of personal credit rating of borrowers – the main factors influencing the favorable decision on loan granting. Based on the study results, the authors conclude that the pandemic period did not cause severe damage to the retail lending sector. As the main trends of the current year, the study highlights toughening of the requirements on the part of commercial banks to borrowers, an increase in interest rates on credit products, and the improvement of payment discipline in the regions.

Application of the algorithm based on the PSO and improved SVDD for the personal credit rating

2014 ◽  
Vol 01 (04) ◽  
pp. 1450037 ◽  
Author(s):  
Sulin Pang ◽  
Shuqing Li ◽  
Jinwang Xiao
Keyword(s):  
Hybrid Method ◽  
Credit Rating ◽  
Credit Scoring ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Personal Credit ◽  
Credit Data

Considering the question of personal credit rating, this paper proposes a hybrid method for credit assessment based on an improved Support Vector Data Description (SVDD) algorithm combined with the particle swarm optimization (PSO) algorithm. First, the paper carries out data preprocess, and then it solves the two problems: parameters optimization and feature selection at the same time using the PSO algorithm combined with the improved SVDD algorithm and assesses the credit data using the optimized parameters and features. Finally, the method constructed is tested through two data sets in practice, and the results show that the hybrid method constructed in this paper can obtain higher classification accuracy compared with some other existing credit scoring methods.

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