Evolution of Grounded Theory for Credit Rating

1. WELCOME
EVOLUTION OF GROUNDED THEORY FOR
CREDIT SCORING, INDIA

2. BY
MUTHARASU A SELVARASU
Professor, Department of Business Administration,
Annamalai University
&
RAIS AHMAD ITOO
Doctoral Research Scholar, Department of Business
Administration, Annamalai University

3. INTRODUCTION
•Lending and Borrowing
•Credit Evaluation
•Credit Decisions
•Banking in service sector

4. REVIEW OF LITERATURE
•Grounded Theory: Grounded theory, a research
methodology primarily associated with qualitative research,
was first proposed by Barney Glaser and Anselm Strauss in
1967. According to its founders, grounded theory constitutes an
innovative methodology, facilitating ‘the discovery of theory
from data’ (Glaser & Strauss, 1967).
•Credit Scoring And Credit Scoring Models: Credit
scoring is a group of decision models and their under-lying
techniques which give support to lenders when providing credit
to customers (Heiat, 2012). In addition, credit scoring model is
a decision support system that helps the managers in financial
decision-making process.

5. RESEARCH METHODOLOGY
•Research Problem
•Research Objectives
•Population Sampling and Data Collection
•Pre-test
•Methods of Data Collection
•Validity and Reliability
•Limitations of the Study

6. RESEARCH PROBLEM

7. RESEARCH OBJECTIVES

8. POPULATION, SAMPLING AND DATA
COLLECTION
47Public Banks 21,
PrivateBanks 18and Foreign banks 8.
Theoretical sampling.

9. PRE-TEST
Interviewing 6 Banking Officials
2 State Bank of India
 1 City Union Bank
1 HDFC
 1 ICICI
1 Punjab National Bank

10. METHODS OF DATA COLLECTION
25 respondents from public sector banks,
17 respondents from private sector banks and
5 respondents from foreign banks
dealing with scoring of personal finance application and preparation of credit report.
Each interview last for 30-45 minutes. Before starting an interview, participants were asked to clarify
doubts about study, if any.
Secondary data sources used for this study are;
529 commercial bank customer responses from big data;
79 images related to credit scoring models and CIBIL;
65 CIBIL newspaper articles;
102 news articles (19 bank loan, 22 credit scores, 10 loans, 10 home loan, 16 RBI and loans, 25
loan defaulters) and
59 interview videos given by credit bureaus employees.

11. VALIDITY AND RELIABILITY
To ensure validity, following five interrelated procedures were
followed
(a) Respondent Validation,
(b) Refutability,
(c) Constant Comparison,
(d) Comprehensive Data Treatment, and
(e) Deviant Case Analysis.
Proportional reduction in loss method was used to assess the
reliability of coding scheme. The proportional reduction in loss for
this study is 0.81, which is well above the 0.70 cut-off level
recommended for exploratory research

12. USE OF NVIVO AS DATA ANALYSIS
TOOL

13. DATA ANALYSIS AND CODING
Open Coding(Free Nodes): Initially
researcher found 238 free nodes from
the data sources.
Axial Coding (Tree Nodes): 22
categories
Application-information,
motivation for repayment,
extra benefits,
standard bank charge,
Imposed bank charges,
screening eligibility,
 clearing norms,
loan processing,
demographic details,
employment details,
financial details,
loan details,
behavioural details,
collateral details,
disbursement,
positive experience and
negative experience,
Credit agencies,
credit scoring,
credit scoring methods,
credit reporting and
credit report errors.

14. Evolved Theories
1. Loan Information Searching Theory
2. Loan Repayment Theory
3. Theory of Loan Pricing
4. Theory of Selecting Loan Applicant
5. Loan Service Processing

15. Evolved Theories
1. Theory of Rating Credit Score
2. Loan Lending Theory
3. Theory of Customer Loan Experience
4. Credit Agency Theory
5. Generating Report and Reliability Theory

16. THEORY FOUNDATION-
CASE1: PUBLIC SECTOR BANKS
PUBLIC SECTOR BANK
BORROWER'S
EXPERIENCING
PERSONAL FINANCE
Application-
Information
Motivation for
Repayment
Standard Bank
Charges
Screening
Eligibility
Clearing Norms
Loan Processing
Demographical
Details
Employment
Details
Financial Details
Loan Details
Behavioural
Details
Collateral Details
Disbursement
Positive
Experience
Negative
Experience

17. THEORY FOUNDATION-
CASE2: PRIVATE SECTOR BANKS
PRIVATE SECTOR BANK
BORROWER'S
EXPERIENCING
PERSONAL FINANCE
Application-
Information
Motivation for
Repayment
Extra
Benefits
Standard Bank
Charges
Imposed Bank
Charges
Screening
Eligibility
Clearing
Norms
Loan
Processing
Demographical
Details
Employment
Details
Financial
Details
Loan Details
Behavioural
Details
Collateral
Details
Disbursement
Positive
Experience
Negative
Experience

18. THEORY FOUNDATION-
CASE3: FOREIGN BANKS
FOREIGN BANK
BORROWER'S
EXPERIENCING
PERSONAL FINANCE
Application-
Information
Motivation for
Repayment
Standard Bank
Charges
Screening
Eligibility
Loan
Processing
Demographical
Details
Employment
Details
Financial
Details
Loan Details
Behavioural
Details
Collateral
Details
Disbursement
Positive
Experience
Negative
Experience

19. THEORY FOUNDATION-
CASE4: CREDIT BUREAUS
CREDIT SCORING BY
CREDIT BUREAU
Credit Rating
Agencies
Credit Scoring
Credit
Reporting
Credit Report
Error
Credit Scoring
Methods

23. SUGGESTIONS
Customers should be made aware about Third Part
Agencies
Commercial Banks should take care about customer
support services, hidden charges, transparent and hassle
free process and detailed information to customers.

24. CONCLUSION
Internal credit scoring is either done using loan originating software or using rating
sheets.
External credit scores tell about the behaviour of the applicant, e.g. payment history,
dues, current loans, loan amount etc. From CIBIL, Equifax, Experian and High Mark
Affect credit score;
credit mix, defaults reported, utilization of credit,
over dues, payment history, guarantor, late payment,
loan behaviour, credit card repayment, credit limit etc.
Impact on customer satisfaction are;
customer support, service, follow ups, response, processing,
transparency, hassle free process, reliability, detailed information.

25. LIMITATIONS OF THE STUDY
Many of the bank employees couldn’t cooperate, because of
their busy schedule and data confidentiality policies of the
banks.
In order to theorize similar results, also samples could have
been taken from other metro cities.

26. THANK YOU