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I have attached a "Table file" and a "sampledescription" file. 1st Job: There is a category for each article (Row) which I did, but not really happy. This tableneeds to be properly CATEGORISED if the...

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I have attached a "Table file" and a "sampledescription" file.
1st Job: There is a category for each article (Row) which I did, but not really happy. This tableneeds to be properly CATEGORISED if the writer thinks necessary.Job 2 (Main job). Then The writer needs to write a " each Category description" following the "sample description" file. (Note: There are 11 category articles now). writers can increase or decrease the number of categories. Need to write 1200 word. Must follow sample.
(Please note my actual research topics is " A systematic literature review on individual credit scoring, a social inclusion perspective". Informing this only for your knowledge)
Answered Same Day Jul 14, 2021


Abhinaba answered on Jul 16 2021
106 Votes
Sample Description
Table of contents
Analysis and synthesis of selected literature    3
Impact of Lending on credit scoring    3
Effect of Information sharing credit scoring    3
Credit scoring form a social inclusion perspective    4
Method model approach in credit scoring    4
Credit Risk and the importance of credit scoring    4
Small business Credit Scoring System    5
Credit Score and Social network impacts    5
Managerial Ability and its effect on credit scoring system    5
Reject Inference Approach to credit scoring    5
Credit rating in comparison with Credit Scoring    6
Credit default and its mitigation through Credit Scoring    6
Others    6
Reference and Appendix    7
Analysis and synthesis of selected literature
A total of 91 articles and papers were collected to conduct a representative analysis and synthesis. A table was formed to categorise the items along with the methodology and the findings of the article. The table consists of a summary of all the sections with its main points highlighted. For an empirical view, the category was separately analysed in the process mentioned below. The appearances were interpreted
oadly to make an empirical analysis of the research questions were answered with the findings and analysis.
Impact of Lending on credit scoring
The category includes 3 (3.2%) articles in total. The lending process is viewed from different points of view through these three articles. Ranging for the subprime consumers lending to small business la\lending and household mortgage. The forts article by Einav et al. (2012) analyses the subprime consumers and the role played by the down payment requirement for selection and repayment process. The main finding of the article defines the central part of a to restrict the origination and also contains bo
owing leverage. The article was written by Sääskilahti (2016) analysis the standard of HHI index and finding reveals that after the financial crisis the volume of business loans decreased and the average loan margins increased
Effect of Information sharing credit scoring
A total of 7 (7.5%) articles were refe
ed to under this category. A variety of topics were considered by the authors of the materials that ranged from credit score, credit information sharing, loan performance, access to credit etc. Most of the articles had an extensive study of the credit score system. Analysis of the effect of credit score and the performance of the loans were discussed (Behr and Sonnekalb (2012). Out of the seven articles, three did not have the aces to the methodology. Overall the findings related under this category sheds light on the facts that having a sound credit information system saves a lot of time for the financial institutions and also costs less for the information. (2018) also talks about the debt contact information and other relations among various details.
Credit scoring form a social inclusion perspective
The category is discussed and analysed under seven different articles ranging from the concept of or in banking, risk multi-objective, finance and baking system (Stewart, R. T. 2011). The articles use the Ekmel density, liner discrimination another LP program to analyse the data and find constructive recommendations. The findings from the articles provide evidence that credit scoring practice increases the accuracy across various algorithms.
Method model approach in credit scoring
Different authors have used various models; a percentage of articles merely 30 (33%) articles are introduced and analysed under the method model approach. The system of analysis in this category has been advocated by 30 different materials, 30 different elements have supported the policy of study in this category a wide variety of topics have been covered for the model approach. Machine learning, business analytics have been discussed (Luo,2019). Multiple criteria analysis, Decision Support, Credit ratings have been considered in the article (Doumpos and Figueira 2019) along with these various concepts of credit risk and deep learning, deep annul network etc.have been analysed through the different process. The ROC curve, the AUC value, the f score have introduced for the analysis of the hypothesis in the article by Hamori et al. 2018)
Credit Risk and the importance of credit scoring
The category of credit risk is one of the most important topics on the credit system and investments. The risk associated with lending out loans has been analysed through 9 articles based on various issues underlying the credits risk system. Risk analysis. Business intelligence software, decision support etc.and the liquidity point of view are carefully analysed through the articles (He and Xiong 2012). Various methods have been used in the process of calculation and analysis, as the return on the asset, the panel model analysis (Kolapo et al. 2012). The findings of the articles suggest that there are various categories of risk associated in the credit process and that the educated persons are aware of the system which the educated people tend to believe in traditional methods.
Small business Credit Scoring System
The small business credit scoring is a relatively new concept in the field of credit scoring and investments. The category focuses on the SMEs and their credit scoring system that allows financial institutions to access the score of the small business and take the decision on lending out. The articles suggest various solutions to the problems of giving out risky loans. Hasumi and Hirata 2014) indicate it extends loans only to the existing SBCS customers.
Credit Score and Social network impacts
Two (2.1%) articles are chosen to discuss the topic under this category. The articles focus on the director’s web, social capital, credit ratings and other activities (Benson et al. 2018). The findings from the tow article suggest that there is a connection between the credit score and the social network system where well-connected board members can easily remove the concerns over CRA. (Wei et al. 2016). The articles used the models of exogenous network and directors network effects on the credit scoring and social network system
Managerial Ability and its effect on credit scoring system
The managerial ability category had 3 (3.2%)articles related to the concept that were reviewed and analysed. The main topic covered under this category is Credit rating; management; Real activities management; accruals (Kim et al. 2013). The findings from the articles suggest that there is a positive relation between RM and credit upgrades (Bonsall et al. 2017).
Reject Inference Approach to credit scoring
The category of reject reference approach consists of two (2.1%) articles based on the concepts of credit scoring and rejects text (Bücker et al. 2013). The report by (Li et al. 2017) focuses on Reject inference, scoring, Semi-supervised, Support Vector Machines, lending, accuracy. The main findings from these two articles are not to ignore the rejected customers profile when developing scorecards for the retail business and also prove that the rejected customer profile is helpful for adding value in practice.
Credit rating in comparison with Credit Scoring
The category consists of 11 (12%) articles. A credit rating system is a measured approach in the whole order of baking and credit policy. The credit score is often tackled about under various other categories. The 11 articles mainly focus on the method of credit score and the impact of it in the quality of information and on the credit policy system (Becker and Milbourn 2011). Credit ratings, Diversification Discount, Information asymmetry, discussed in the article by (Chou and Cheng 2012). The main findings from their articles are that the credit rating system positively factors the credit policy and also helios in understanding the bo
owers’ record and intentions Barth (2012).
Credit default and its mitigation through Credit Scoring
The category focuses on 7(7.5%) articles about various credit defaults, default risk and credit spreads. The risk of giving out credit is the default system. The articles suggest multiple guidelines and recommendations minimise the risk of default on the loans given out. Among various recommendations, cash policy is recommended by (Acharya et al.2012). CDS trading is a risky venture as recommended by (Su
ahmanyam et al. 2014). Further CDS system and the credit risk assessment system is analysed by (Lin et al. 2017). Other articles focus on credit repeating and bo
owing constraints (Garmaise and Natividad 2017)
In the last category 7 (7.5%) articles were chosen. Several topics are discussed through these articles ranging from consumer finance, CSR student loans, business ethics, credit ratings etc. the findings from different articles focus on the efficiency of the bank and the information system are beneficial for the financial institutions.
Reference and Appendix
Table 5: Findings from different researchers based on category
    Focus Area
    Impact of Lending on credit scoring
    Einav et al. (2012)
    Subprime consumer lending, contract pricing, credit market
    Analyse subprime consumer lending and the role played by down payment requirements in screening high-risk bo
owers and limiting defaults
    An empirical model of the demand for financed purchases that incorporates both adverse selection and repayment incentives.
    The central role that down payment requirements play in limiting loan originations and constraining bo
ower leverage.
    Sääskilahti (2016)
    Financial crisis, Small business lending, Local bank competition
    Examines whether the effects of the financial crisis on the volumes and prices of small business loans depended on the pre-crisis local competitive environment.
    Auxiliary analyses, the standard Herfindahl– Hirschman Index (HHI), the Lerner index
    The monthly volumes of new business loans decreased and the average loan margins increased after the onset of the crisis.
    Bezemer et al. (2020)
    Credit allocation
Business lending
Household mortgage
    To analyse the effect of an increase in mortgage lending flows on business credit flows.
    A VAR model, Linear local projections and single equation estimation
    The impact of mortgage credit expansion on business credit growth is found to be positive in advanced economies and negative in emerging and developing economies.
    Effect of Information sharing credit scoring
    Tsai et al. (2011)
    Banks, Credit Information Sharing
    Explores how information costs, proxied by characteristics of credit reporting systems, affect the foreign expansion 
    Logit estimation models and OLS estimation model used
    Banks are attracted to countries where the credit reporting system helps reduce banks' information costs.
    Büyükkarabacak and  Valev (2012).
    Banking crises,
Credit information sharing,
Credit growth
    Study the effect of information sharing on the likelihood of banking crises.
    No Access
    Credit information sharing reduces the likelihood of banking crises and it does more so in low income countries.
    Behr and Sonnekalb (2012)
    Information sharing, cost of credit, loan performance, credit registry
    Analyze the effect of information sharing between lenders on access to credit, cost of credit, and loan performance.
    No access
    Information sharing by means of a credit registry does not affect access to or cost of credit, but improves loan performance. Bo
owers are disciplined to repay in their concern about future access to credit.
    Dierkes et al. (2013)
    Credit risk
Asymmetric information
Private firms
    Investigate on how business credit information sharing helps to better assess the default risk of private firms.
    Two probit regression models with DEF as dependent variable
    business credit information sharing substantially improves the quality of default predictions. 
    Sutherland  (2018)
    Debt contracts, Information sharing, Information asymmetries, Relationship lending, Transactional lending, Entrepreneurial finance, Credit reports, Credit scores
    Examine how credit reporting affects where firms access credit and how lenders contract with them
    Use within firm-time and lender-time tests that exploit lenders joining a credit bureau and sharing information in a staggered pattern.
    Found information sharing reduces relationship switching costs, particularly for firms that are young, small, or have had no defaults
    Taylor and Francis
    Liberati and Camillo (2018)
    Credit scoring, psychological traits, credit information, financial prediction, Kernel discriminant
    Considers financial histories and psychological traits of customers of an Italian bank and compare the performance of kernel-based classifiers with those of standard ones.
    No access
    They found very promising results in terms of misclassification e
or reduction when personality attitudes are included in models, with both linear and non-linear discriminants.
    Loaba and Zahonogo (2019)
    Banking credit, economic growth, information sharing, credit register cover, simultaneous equations, three‐stage least square
    Aims to analyse the information sharing effect on bank credit and economic growth in West African Economic and Monetary Union.
    A simultaneous model was tested on panel data and a structural model based on two simultaneous equations is used to analyse the information sharing effects on banking credit and economic growth.
    High information sharing does not result in obviously high bank credit and economic growth. However, results also show the necessity for promoting information sharing structures.
    Credit scoring form a social inclusion perspective
    Finlay (2010) 
    OR in banking, Credit scoring, Genetic algorithms, Profitability
    A comparison of predictive models of continuous financial behaviour with binary models of customer default.
    Linear regression, genetic algorithms, neural networks and logistic regression.
    Models of continuous financial behaviour to outperform classification approaches. 
    The operation research society-JSTOR
    Stewart, R. T. (2011)
    Finance, banking, consumer credit, credit scoring, risk, multi-objective
    This paper outlines a profit-based scoring system for credit cards to be used for acquisition decisions.
    Optimal binning for scoring modelling
    The results suggest a profit-based scoring system segmented by risk and predicting spend improves upon a risk-only strategy.
    Avery et al. (2012).
    Credit score, disparate impact
    Examine the individual predictive factors included in credit scoring models and assess whether including each of these factors in a credit scoring model results in a disparate impact by race or ethnicity, age or gende
    The model-building methodology developed as part of the Federal Reserve Board’s Report to the Congress on Credit Scoring and Its Effect on the Availability and Affordability of Credit (Board of Governors 2007)
    No evidence of disparate impact by race or gender. However, they found evidence of some limited disparate impact by age, in which the use of variables related to an individual's length of credit history appear to lower the credit scores of older individuals and increase them for the young.
    Van Gool et al. (2012)
    Microfinance; credit scoring; logistic regression; credit risk; Bosnia–Herzegovina
    Analyzes whether microfinance institutions can benefit from credit scoring, which has been successfully adopted in retail banking
    Credit scoring versus Human intensive microfinance (Bosnian microlender). 
Two logistic regression-based scoring models. The models are assessed in terms of stability, readability, and discriminatory power.
    Credit scoring is not yet able to fully replace the human-intensive microfinance credit process. However, it is recommendable to introduce credit scoring as a refinement tool in the lending process, in order to combine both statistical and human best practices. Moreover, a microlender staff can learn from credit scoring models to validate or contrast practical intuition.
    Crone and Finlay (2012)
    Credit scoring, Data pre-processing, Sample size, Under-sampling, Over-sampling, Balancing
    Describes an empirical study of instance sampling in predicting consumer repayment behaviour, evaluating the relative accuracies of logistic regression, discriminant analysis, decision trees and neural networks 
    Classification and Regression Trees (CART), Kernel Density (KD), K-Nearest Neighbour (KNN), Linear Discriminant Analysis(LDA), Linear Programming (LP), Logistic Regression (LR), NN = Neural Networks (NN), QDA = Quadratic Discriminant Analysis
    Model building on credit scoring datasets, and provides evidence that using samples larger than those recommended in credit scoring practice provides a significant increase in accuracy across algorithms.
    The operation research society-JSTOR
    Kennedy et al. (2013)
    Banking, credit scoring, low-default portfolio (LDP), supervised classification, one-class classification (OCC), benchmarking
    An extensive evaluation of approaches to solving the LDP problem when building credit scoring model. Also investigates the suitability of over sampling, which is a common approach to dealing with LDPs
    Normal process, oversample process, one-class classification process
    Only in the near or complete absence of defaulters should semi-supervised OCC algorithms be used instead of supervised two-class classification algorithms. 
    So et al. (2014)
    Pricing, Risk decision analysis, credit scoring
    To show how to build a scorecard to estimate the chance of each applicant being a transactor and how that makes a difference to profitability...

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