Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 1 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
A Synopsis on
Credit Risk Evaluation of
Micro, Small and Medium Scale
Enterprises using
Evolutionary Neuro Fuzzy Logic
by
Desai Karanam Sreekantha
submitted in fulfillment for requirements of the degree
DOCTOR OF PHILOSOPHY
in
FACULTY OF COMPUTER STUDIES
to
SYMBIOSIS INTERNATIONAL UNIVERSITY, PUNE
under the guidance of
Prof. Dr. R.V.Kulkarni Professor & Head
Chh. Shahu Institute of Business Education & Research
(SIBER), Kolhapur - 416 004.
April 2013
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 2 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter Index
Chapter No. Title Page No.
1. 1.1
Introduction 1
Background of the problem 1
1.1.1 National status of MSME segment 1
1.1.2 International status of MSME segment 2
1.2 Statement of the problem 2
1.3 Purpose of the study 2
1.3.1 Advantages of credit risk assessment 2
1.3.2 Advantages of expert systems 2
1.4 Significance of the problem 3
1.5 Objectives of the study 3
1.6 Limitations of manual systems 3
2. Review of the literature 4
3. Research methodology and study design 7
3.1 Foundations for credit risk research 7
3.2 Scope of the study 7
3.3 Hybrid soft computing – evolutionary neuro fuzzy logic
7
3.4 Credit Lending Decision 8
4. Credit Rating Frameworks (CRF) design 9
4.1 4.1.1 Manufacturing industry CRF 10
4.1.2 Trading industry CRF 11
4.1.3 Service industry - healthcare - nursing home CRF 11
4.1.4 Service industry - hospitality – hotels CRF 12
5. Rulebase design 13
5.1 Manufacturing industry rulebase 13
5.2 Service industry rulebase 14
5.3 Trading industry rulebase 14
6. Expert system prototype development 15
6.1 Introduction to Expert System Builder(ESB)
software
15
6.2 Credit Risk Evaluation Expert System (CREES)
design
15
6.3 User interface design 16
6.4 Sample test reports 16-17
7. Result analysis 18
8. 8.1 Conclusions and Findings 19
8.2 Scope for further research 19
8.2 UGC MRP funding 19
8.3 Best paper of International Conference Award 19
9. Researcher publications and presentations 20-21
10. References 22-25
© SYMBIOSIS INTERNATIONAL UNIVERSITY, PUNE All Rights Reserved
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 3 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 1 Introduction
Introduction chapter describes the credit risk evaluation of Micro, Small and
Medium scale Enterprises (MSME) research problem, its background and
significance. The national and international status of the MSME industry,
objectives of research, hypothesis formulated, definition of the basic terms,
assumptions are discussed. This chapter also covers the limitations and
outline of the study.
Introduction
MSME segment is one of the fastest growing industrial sectors and constitute
over 90% of total enterprises in most of the economies of the world. The
researchers estimate that about 60% of the MSME credit is provided by
commercial banks alone. Over the past few years the credit risk evaluation of
MSME by banks and financial institutions has been an active area of research
under the joint pressure of regulators and shareholders. The Credit Risk
Evaluation (CRE) involves evaluation of risk parameters such as financial,
business, industry and management etc. A MSME client always has more
private information about the risk in his proposal, so that when a client comes
with a loan request, the banks have no way to judge its risk extent from the
facts in the loan documents. This is the direct reason leading to risk and crux
of the problem for banks. An incorrect decision endangers bank’s financial
capability ending up in steep decline in the margin of profits.
1.1 Background of the problem
1.1.1 National status of MSME segment
The quick results of 4th all India census of MSME (2006-07) reveals that the
sickness in MSME has increased from 13.98 percent in 2001-02 to 14.47
percent in 2006-07. One of the reasons for sickness is shortage of working
capital. Federation of Indian Export Organizations (FIEO) president Mr. A.
Sakthivel pointed out that "Interest rates in India are much above the
international benchmark”. This increased prime lending rates are a jolt to
MSME exporters.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
1.1.2 International status of MSME segment
Manufacturing sector accounts for 30–50% of GDP and drives the economies
of Asian countries such as Thailand, Indonesia, Malaysia, Singapore, Hong
Kong, Taiwan, Philippines, Korea, and China. China’s manufacturing segment
is 50% of GDP, but India is lagging behind with 25% share of GDP. The
present conditions do not promote manufacturing in India.
1.2 Statement of the problem
MSME need financial assistance to establish and conduct their business,
trading and service activities. The effective management of credit risk is a
critical component of risk management and essential to the long-term success
of any banking organization. The banks are in need of a consistent and
integrated credit risk evaluation system to optimize their lending operations.
Researcher developed of an Expert System called Credit Risk Evaluation
Expert System (CREES) prototype to support in taking credit risk decisions in
banks and financial institutions by credit rating executives.
1.3 Purpose of the study
The recent tarnishing bankruptcies in US such as WorldCom, Enron,
HealthSouth, Tyco and many other high profile banks failures in Asia forced
many banks and financial institutions to realize the need for a viable credit risk
management. The early decades of this century have witnessed many
sensational incidents of accounting irregularities in which the executives have
been caught cooking the books.
1.3.1 Advantages of credit risk assessment
1. Facilitates informed credit decision consistent with bank’s risk appetite
2. Provides ability to price products on the basis of risk
3. Facilitates dynamic provisioning and minimizes impact of losses
4. Lending decisions can be taken within the minimum time, thus lending
business volume can increase substantially
1.3.2 Advantages of expert systems
Application of expert systems in financial management has grown
considerably in the last two decades and they have advantages like
1. Superior problem solving capability
2. Applying experience to problems
3. Reduced response time for complex problems and
4. Ability to look at problems from a variety of perspectives
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
1.4 Significance of the problem
1.5 Objectives of the study Fig-1 Bankruptcies
The researchers aimed at developing an expert system prototype for credit
risk evaluation of MSME segment with following objectives
1. Designing the credit rating framework to measure and quantify all
subjective and objective parameters of credit decision environment
2. The credit evaluation solution is based hybrid soft computing technique
called on evolutionary neuro fuzzy approach
3. Collecting real credit data from various banks in their region and test the
solution
4. Designing an expert system incorporating local conditions and
requirements
5. Designing the solution to meet the different business lines and Industries
6. Implementation of expert system using fuzzy and neural network tools
1.6 Limitations of manual systems
Research has shown that human brain is capable of evaluating only a small
number of factors at a time, but the credit risk analyst needs to analyze and
deal with large number of differently valued factors in short time. The lack of
rigor in professional judgment leads to the risk of misinterpretation,
misunderstanding, miscommunication, miscalculation and misuse of soft
information. The manual credit risk evaluation systems are quite expensive
due to high cost of training, maintaining the qualified and experienced
personnel.
In 2007 world economic crisis, 13 US based
companies reported $50 billion of losses and
$300 billion market capture losses shown in
Fig-1. In March 2005 American Insurance
Group admitted to improper accounting that
cut its net worth by more than $1.7 billion.
These tarnishing bankruptcies forced many
banks and financial institutions to realize the
importance of credit risk management.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 2 Review of literature
This chapter covers an exhaustive survey of literature (of about 300 most
relevant articles) on MSME and credit risk evaluation from reputed journals
such as IEEE Transactions on credit risk, machine intelligence, business
journals and proceedings of various international conference proceedings
since 1964 to today. Some selected articles from the survey are discussed by
way of illustration.
The surveyed literature can be classified in two major three categories as
casual methods, statistical methods and rulebased methods as shown in
Fig-2. There are hybrid methods which are adopting more than one technique
for credit risk evaluation
Fig-2 Survey of credit risk models
Paul F. Smith (1964) authored Measuring Risk on Consumer Installment
Credit paper and developed a relatively simple statistical method for
measuring risk on individual accounts that can also be used for measuring
and controlling portfolio quality and for estimating loss rates.
Peter Duchessi and Salvatore Belardo (1987) have designed knowledge
based system prototype to support commercial loan officers in credit risk
analysis. The knowledge based system was called Loan Analysis Support
System (LASS). This system provides a user interface to deal with variety of
data, analytical operations and models in a flexible manner.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Rekha Jain (1989) paper entitled Expert Systems: A Management
Perspective highlights some of the expert system important features.
MARBLE (Managing and Recommending Business Loans Evaluations) expert
system is a loan evaluating expert system. MARBLE combines financial
projections with qualitative data. The loan granting decision is a combination
of factors related to an analysis of the firm's historical, financial information,
qualitative information about its product market, industry characteristics and
overall performance of the management. MARBLE system captures some of
these features by analyzing the economic characteristics (size, market share),
financial characteristics (profitability, liquidity and leverage), ability to repay
loans (cash flow analysis, security), value of collateral, competitive position in
industry, etc. Each of the relevant factors is given a certain weightage to
evaluate a credit risk score.
Sushmita Mitra and Yoichi Hayashi (2000) have conducted exhaustive survey
of neuro–fuzzy rule generation algorithms and presented Neuro–Fuzzy Rule
Generation: Survey in Soft Computing Framework paper. The neuro–fuzzy
approach, symbiotically combining the merits of connectionist and fuzzy
approaches constitutes a key component of soft computing at this stage.
Authors propose to bring these together under a unified soft computing
framework and included rule extraction and rule refinement in a broader
perspective of rule generation. Rules learned and generated for fuzzy
reasoning and fuzzy control are also considered from this wider view point.
This study also provided real life application to medical diagnosis.
Xuemei Zhu, Ping Wang (2008) in their paper, The credit risk assessment
method based on Dempster-Shafer combining evidence theory - Reasoning of
the methods evolved an expert methodology known as CAMPARI factor
analysis, which assesses the customer credit worthiness by applying seven
criterions. The word CAMPARI stands for C-Character, A-Ability, M-Margin, P-
Purpose, A-Amount, R-Repayment and I-Insurance. This method largely
depends on the ability of the expert in assessing the credit risk.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Such an expert method can prove quite efficient in assessing the debtor credit
as it relies on the historic information of the debtor and the subjective
judgment of the experts. This method is usually applicable for qualitative
rather than quantitative analysis and hence lacks an objective appraisal.
Adel Lahsasna (2009) in his article Evaluation of credit risk using
evolutionary-fuzzy logic scheme discusses the transparency and the accuracy
of credit scoring model. The author investigates credit risk using two different
types of fuzzy models namely Takagi-Sugeno (TS) and Mamdani using
generic software called Evolutionary-Fuzzy-Neuro-System (EvoFNS). This
software is used for fuzzy identification, (generation and optimization)
prediction, classification and knowledge extraction.
Shaomei Yang and Junyan Zhao (2009) in their paper Study on commercial
banks credit risk based on CAMEL rating system identifies five areas such as
Capital adequacy, Asset quality, Management ability, Earnings and Liquidity.
The initials of five words form the term CAMEL and CAMEL rating system is
used for the assessment of commercial banks credit rating system.
Linda Delamaire (February 2012) proposed the implementation of credit risk
management system based on innovative scoring techniques. Their thesis
presents a credit scoring system which aims at setting credit lines and thus,
controlling credit risk. It includes three types of models: application
scorecards, early detection scorecards and behavioral scorecards. They have
been built on real and recent data coming from a German credit card
company. The models have been built with a training sample and validated
accordingly, using logistic regression. Information value and validation charts
have been used for comparing the models. Finally, the author presents
possible extensions to the research and hopes that the technique described in
this thesis can play some part in preventing future financial crisis.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 3 Research methodology and study design
This chapter deals with the foundation work done by earlier researchers and
scope of the present research. The hybrid soft computing technique called
Evolutionary Neuro fuzzy logic has been applied in the present work.
3.1 Foundations for credit risk research
The basic work on credit risk was discussed in the book entitled
Knowledgebase applications in Banking Sector by Prof. R.V.Kulkarni and
Prof. B. L. Desai. The present research may be viewed as a unique
contribution in this field, which uses hybrid soft computing - evolutionary
neuro-fuzzy technique.
MSME clients need finance to setup and conduct their operations, so they
approach banks/financial institutions for credit. The credit rating executives
gather information about the clients in order to take an informed decision
about his creditworthiness and the prospects of repayment.
3.2 Scope of the study
The effective management of credit risk is a critical component for long term
success of any banking organization. What is really needed is a consistent
and integrated risk management system. The quality of credit decision
depends on the ability of credit risk executive’s subjective judgment and
experience. The researchers have studied credit risk systems at State Bank of
India, Zonal Office, Hubli, State Bank of India Commercial Branch, Belgaum,
Canara Bank, Bagalkot, Karnataka State Finance Corporation branch office,
Bagalkot, BVVS Software Technology Entrepreneurs Park, Bagalkot and
BVVS Rural Development Foundation, Bagalkot.
3.3 Hybrid soft computing - Evolutionary Neuro fuzzy Logic
Authors have applied hybrid soft computing technique called evolutionary
neuro fuzzy logic for designing an expert system solution for credit risk
evaluation of MSME. Neural networks imitate functions of the human brain's
ability to sort out patterns and learning from trial and errors, discerning and
extracting the relationships that underlie the data with which it is presented.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Neural networks are good at providing very fast and very close
approximations of the correct answer. Fuzzy computing can handle qualitative
values instead of quantitative values. It can define linguistic variables instead
of the classical numeric variable and perform computing with these variables,
using fuzzy rules, simulating in a certain way the human reasoning processes.
Evolutionary computation uses iterative progress, such as growth or
development in a population. This population is then selected in a guided
random search using parallel processing to achieve the desired end. The
principal constituents of soft computing are fuzzy logic, neuro computing, and
probabilistic reasoning, with the latter subsuming genetic algorithms, belief
networks, chaotic systems, and parts of learning theory.
3.4 The lending decision - participating institution model
Researchers have conducted interviews with credit rating executives and
evaluating documentation pertaining the underwriting process and identified
the key dimensions employed when assessing business loan applicants
Repayment capacity - accounts for 35 percent of the credit decision and
comprises the evaluation of financial parameters such as current, quick
and debt/service ratio; and an assessment of the borrower’s credit history
Financial position - accounts for 20 percent of the credit decision and
comprises the evaluation short and long term repayment capacity backed
by financial statements representative of an adequate financial condition
Character - accounts for 10 percent of the credit decision and comprises
personal factors of the borrower such as honesty, propensity to meet with
obligations, disposition, availability and cooperation with bank officials and
managerial experience
Business and industry conditions - accounts for 10 percent of the credit
decision and comprises the evaluation of physical conditions of the
business and industry conditions
Collateral - accounts for 25 percent of the credit decision and comprises the
evaluation of identifiable and accessible assets which net value covers the
loan principal, interests and escrow
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 4 Credit rating frameworks design
This chapter deals with designing the framework called Credit Rating
Framework (CRF) for credit risk evaluation of MSME. CRF is designed to
measure all the objective and subjective risk parameters of credit decision
environment. The CRF organizes all the facts of the client in five levels
depending on their nature and criticality. These risk parameters are
organized in to five hierarchical levels. These risk parameters are transformed
in to the linguistic variables of fuzzy logic. Every risk parameter is assigned a
score based on its significance in credit decision-making. Each client’s actual
credit weight is computed on the basis of client’s credit worthiness. This
framework forms the basis for knowledge base and Expert System design.
This process resembles the credit risk executives thought process. The credit
risk evaluation process is depicted in Fig-3.
Fig-3 Credit Rating Framework Design Process
The credit decisions parameters for manufacturing, trading and service
industries are identified as shown in Fig-4. The technical feasibility is the
dominant risk parameter in manufacturing industry with score of 200 out of
total score 500 (40%) weightage. Finance risk is 40% of total risk in trading
industry. Management risk is the dominant risk parameter in service
industries like hotels and healthcare.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Fig-4 Credit risk parameters in Manufacturing,Trading and Service Industries
Credit Rating Frameworks (CRF)
The Credit risk parameters for all there I dustries are shown in the following
Tables 1 to 3. Table-3 Service Industry risk parameters
Table -1 Manufacturing industry risk
20%
20%
15%
25%
20%
Management Risk
Property Risk
Operational Risk
H e a l t h C a r e C r e d i t R i s k
1 4 %
8 %
1 3 %
1 3 %1 3 %
8 %
1 0 %
1 3 %
8 %
1 P r o f i l e o f P r o m o t e r s 2 B a c k g r o u n d o f N u r s i n g H o m e
3 P r o p o s e d N u r s i n g H o m e 4 M a r k e t P o t e n t i a l
5 P r o j e c t C o s t 6 Im p l e m e n t a t i o n S c h e d u l e
7 S t a f f P r o f i l e 8 S o u r c e s o f F i n a n c e
9 R e p a y m e n t
Sl. No
Title Linguistic Values
Max Weight
1 Technical Feasibility
OK / Not OK 200
2 Management Commitment
Excellent/ Good/ OK/ Poor
100
3 Commercial Viability
OK /Not OK 75
4 Financial Analysis
Excellent/ Good/OK/Poor
75
5 Economic Analysis
Excellent/ Good/OK/ Poor
50
Total Weight 500
Sl. No
Risk Parameter Max. Score
Min. Score
1 Profile of Promoters 70 28
2 Background of Existing Nursing Home
70 28
3 Proposed Nursing Home Features
75 30
4 Market Potential 75 30
5 Project Cost 75 30
6 Schedule of Implementation
50 20
7 Staff Profile 75 30
8 Sources of Share Capital
60 24
9 Repayment Proposal
50 20
10 Total 600 240
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
The trading industry CRF is shown in table. The major risk parameters like
Management risk, Business risk and Finance risk, its sub parameters, fuzzy
linguistic variables are shown in the Table - 2 below and Table-4.
Table-4 Trading CRF
Sl. No
Parameter Score Wei ght. in%
1 Management risk
40 27
2 Business risk 50 33
3 Finance risk 60 40
Total 150 100
Code Parameter Score Sub Score
Relative Weight
in %
Best Good Avg. *
Poor
1.0 Management Risk 60 40 60 52 34 19
1.1 Management Quality 10 10 8 4 2
1.2 Financials and MIS 10 10 8 4 1
1.3 Account Behavior 20 20 18 13 8
1.4 Technical Qualification 20 20 18 13 8
2.0 Business Risk 50 33 50 36 26 13
2.1 Competition 10 10 8 6 3
2.2 Locational Advantage 5 5 3 2 1
2.3 Commodities Traded 5 5 3 2 1
2.4 Market Perception 10 10 8 6 3
2.5 Industry Profile 5 5 3 2 1
2.6 Product Characteristics 10 10 8 6 3
2.7 Marketing 5 5 3 2 1
3.0 Financial Risk 40 27 40 32 20 8
3.1 Earnings/Growth Trends 6 6 5 3 1
3.2 Tol/TNW 6 6 5 3 1
3.3 Inventories to Debtors/ Sales
4 4 3 2 1
3.4 Sundry Creditors to Purchases
4 4 3 2 1
3.5 Net Profit /Sales 6 6 5 3 1
3.6 Bank borrowing to sales 4 4 3 2 1
3.7 Current Ratio 6 6 5 3 1
3.8 Stock holding to Sales 4 4 3 2 1
Total 150 100 150 120 80 40
Credit Rate
Risk Level Weight Weight in%
AAA Highest Safety
WAAA >=95<100
AA High Safety WAA >=85< 95
A Adequate Safety
WA >=80< 85
BBB Moderate Safety
WBBB >=70< 80
BB Inadequate Safety
WBB >=60< 70
B Significant Risk
WB >=55< 60
C High Risk WC >=50< 55
D Default WD < 50
Table-5 Credit Ratings, Risk levels and Weights percentage
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
The fourth level sub parameters of risk parameter Management
Experience are shown in Table-6 below
1.1.1 Management Experience - Sub Parameters
The third level sub parameters of risk parameter Technical
Qualification are shown in Table-7 below
1.4 Technical Qualification
The international standard credit ratings designed by Standards and Poors credit rating company are shown in Table – 8 below.
Code Parameter Score Linguisti
c Value
1.1.1.1 Client is in the business for more than a
decade and well experienced
5 Best
1.1.1.2 Client is in the business for more than a five years but less than decade
4 Good
1.1.1.3 Client is in the business for less than a five years
2 Average
1.1.1.4 The client is totally new in this venture 1 Poor
Code Parameter Score Linguistic
Value
1.4.1 One or more promoters are technically qualified
20 Best
1.4.2 Promoters have availed the services of Technocrat
15 Good
1.4.3 Depends upon experienced technician without professional people
5 Average
1.4.4 No support for technical knowledge 2 Poor
AAA Best credit quality - Extremely reliable with regard to financial obligations
AA Very good credit quality - Very reliable
A More susceptible to economic conditions - still good credit quality
BBB Lowest rating in investment grade
BB Caution is necessary - Best sub-investment credit quality
B Vulnerable to changes in economic conditions - Currently showing the ability to meet its financial obligations
CCC Currently vulnerable to nonpayment - Dependent on favorable economic conditions
CC Highly vulnerable to a payment default
C Close to or already bankrupt - payment on the obligation currently continued
D Payment default on some financial obligation has actually occurred
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 5 Rulebase design The set of guiding rules in taking the credit risk decision by credit rating
executives in banks are captured and encoded in to a rulebase. A separate
fuzzy rule base is developed for every industry and every major risk such as
management risk, business risk and finance risk in the credit rating framework,
to facilitate change of rules easily. These rulebases are used by expert system
in credit risk evaluation from the client’s credit data. The Mamdani inference
approach i.e. first infer-then-aggregate (FITA) is used to implement the fuzzy
logic rule base. These rulebases consists of about 300 rules to evaluate the
credit worthiness of the client. Some of the sample rules are listed below.
Manufacturing industry risk assessment rulebase
Rule No
Antecedent IF
Consequent Then
1. Technical Feasibility is Excellent AND Management Commitment is Excellent AND Commercial Viability is Excellent AND Financial Analysis is Excellent AND Economic Analysis is Excellent
Client Rating is Excellent
2. Integrity is excellent AND Involvement is excellent AND Financial Resources are excellent AND Competence is excellent AND Leadership is excellent AND Organization Structure is excellent
Management Commitment is excellent
Healthcare service industry risk assessment rulebase
3. Profile of Promoters is Excellent AND Background of Existing Nursing Home is Excellent AND Proposed Nursing Home Features are Excellent AND Market Potential is Excellent AND Project Cost is Excellent AND Staff profile is Excellent AND Schedule of Implementation is Excellent AND Sources of Capital are Excellent AND Repayment Proposal is Excellent
Promoters Rating is the Excellent, Credit Risk is Negligible
4. Infrastructure is Excellent AND Consultancy profile is Excellent AND Diagnosis facilities are Excellent AND Ambulance Facilities are Excellent AND Surgical Facilities is Excellent
Proposed Nursing Home Features are Excellent
5. Repayment of Interest is Excellent AND No Transgressions AND Compliance of Sanction terms is Excellent AND Turnover in the Account is Excellent AND Cheque /Bill Return is Excellent AND Operation in the Account is Excellent
Account behavior and Track Record is Excellent
6. Management Quality is the Best AND MIS & Financials is the Best AND Account Behavior & Track Record is the Best AND Technical Qualification is the Best
Management is the Best Management Risk is Negligible
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 16 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
7.
Property Risk Rating is Highest Safety AND Management Risk Rating is Highest Safety AND Operational Risk Rating is Highest Safety AND Financial Risk Rating is Highest Safety AND Market Risk Rating is Highest Safety
Hotel credit proposal is Excellent, Credit Risk Rating is Highest Safety, Rating is AAA
Hotel industry risk rulebase
8. Location & Visibility Rating AND Property Characteristics Rating AND Construction cost per room AND Access to Road Rail AND Parking Availability Rating AND Catchment Area Rating
Property Risk is Highest Safety Property Risk Rating is AAA
9. Management Quality is the Best AND MIS & Financials is the Best AND Account Behavior & Track Record is the Best AND Technical Qualification is the Best
Management is the Best, Management Risk is Negligible
Trading industry risk rulebase
Rule No
Antecedent IF
Consequent Then
10. Management is the Best AND Business is the Best AND Financial is the Best
Client Rating is the Best ,Client Risk is Negligible
11. Competition is the Best AND Locational Advantage is the Best AND Commodities Traded is the Best AND Industry Profile is the Best AND Market Perception is the Best
Business is the Best Business Risk is Negligible
12. Earnings/Growth Trends is The Best AND Tol/TNW is The Best AND Inventories +Debtors/Sales is The Best AND Sundry Creditors to Purchases is The Best AND Net Profit /Sales is The Best AND Bank borrowing to sales is The Best AND Current Ratio is The Best AND Stock holding to Sales
Financials is the Best Finance Risk is Negligible
13 Repayment of Interest is Excellent AND No Transgressions AND Compliance of Sanction terms is Excellent AND Turnover in the Account is Excellent AND Cheque/Bill Return is Excellent AND Operation in the Account is Excellent
Account behavior and Track Record is Excellent
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 17 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 6 Expert system prototype design
6. Expert system prototype design This chapter discusses the methodology of developing the prototype for Credit
Risk Evaluation Expert System (CREES). The software development process
of CREES and tools used is explained. Menu structures and screen layouts
for capturing the client data and risk evaluation process are discussed.
Researchers have designed an expert system model and called Credit Risk
Evaluation Expert System (CREES) and tested the system with test data
select representative sample size of 500.
6.1 CREES design The CREES user/language interface frontend shown in Fig-3 is developed
using Dream viewer tool.
MySql server is used for implementing the backend database design.
Apache Tomcat 5.5 web server has been used in web interface development.
Fuzzy Jess tools are plugged in to eclipse environment.
6.2 User interface design
Credit Portfolio Knowledgebase
e
Credit Processing
Credit Models
Credit Reporting
Language I n t e r f a c e
Client Credit History
Database
Current Client Database
Fuzzyfication
Defuzzyfication Fuzzy Inference
Knowledge Acquisition
Neural Networks
Credit Experts Knowledgebase
Evaluate Alternatives
Explanation Knowledgebase
Knowledgebase Inference Engine Explanation
Fig-5 Prototype for Credit Risk Evaluation Expert System (CREES)
Fig-6 Desktop of Credit Risk Evaluation Expert System (CREES)
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Fig-7 System users information
6.3 Sample test reports The sample test reports are shown in the following test cases
Test Case : 1
Credit Risk Evaluation using Expert System- Manufacturing Sector Client Id : C000000001 Application Id : A000000001 Client Name : ABC Cement Industries Ltd 1. Technical Feasibility Rating is - OK 2. Management Commitment Rating is - OK 3. Commercial Viability Rating is - OK 4. Financial Analysis Rating is - OK 5. Economic Analysis Rating is - OK Credit Risk Rating of the client is Significant Safety
Test Case : 2
Client Id : C000000001 Application Id : A000000001 Client Name : ABC Cement Industries Ltd 1. Loan Details Rating is - Very Good 2. Net Present Value Rating is - Very Good 3. Business Ratios Rating is - Very Good 4. Project Cost Rating is - Very Good 5. Liquidity Rating is - Very Good 6. Profitability Rating is - Very Good 7. Current Business Assets Rating is - Very Good 8. Balance Sheet Status Rating is - Very Good Economic Analysis Risk Rating is Excellent Risk Rating High Safety-AA
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 19 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Test Case : 3 Management Client Integrity Risk Rating Client Id : C000000001 Application Id : A000000001 Client Name : ABC Cement Industries Bagalkot Credit Risk Code : 010301 1. Willingness Rating is - Good 2. Informated Rating is - Good 3. Enthusiasm Rating is - Good Risk Score vs Max Score 8.0/15.0 Risk Rating Moderate Safety-A
Test Case : 4
Credit Risk Evaluation using Expert System Trading Sector Client Credit Risk Assessment Client Id : C000000002 Application Id : A000000002 Client Name : ABC Pharma Industries Hubli Trading Sector Standard Credit Risk Rating Scores Soft Computed Trading Sector Credit Score is: Excellent :148.40 Out of Standard Score : 150.0 Credit Risk Rating Highest Safety - AAA Management Risk Rating is - Excellent Business Risk Rating is - Excellent Finance Risk Rating is - Excellent
Test Case : 5
Service Sector Credit Rating Assessment Client Id : C000000007 Application Id : A000000007 Client Name : Sanjeevini Nursing Home, Bagalkot 1. Promoters Risk Rating is - Default 2. Existing NursingHome Risk Rating is - Default 3. Proposed NursingHome Risk Rating is - Default 4. Market Potential Risk Rating is - Default 5. Project Costing Risk Rating is - Default 6. Staff Risk Rating is - Default 7. Implementation Risk Rating is - Default 8. Capital Risk Rating is - Default 9. Repayment Risk Rating is - Default Credit Risk Advice The Client Risk Rating is Default, Client Risk Rating is Highest Risk and Client Risk Grade is D
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 20 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter – 7 Result analysis 7.1 Result analysis Researchers have collected the real data (Hiding the Client’s Identity) and
also simulated the about 500 client’s data. These samples are representing
the Manufacturing, Trading and Service sectors. They have tested CREES
system. The results are compared with that of manual system as shown in
Table-9. The Percentage of clients misjudged by the fuzzy tool is
(21/500)*100 = 4.2%. The credit risk decisions taken manually and CREES
system are agreeing to great extent about 95.8% accuracy. Fig-9 shows
graph of comparison of results.
Table – 9 Results of CREES are compared with that of manual system
Fig-9 Graph of comparison of results.
Sl. No Client Risk Level
Manual Decisions
CREES Decisions
Relative Error %
No. of Clients Mis Classified
1 Highest Safety 78 83 6.0 6
2 High Safety 70 73 4.1 3
3 Significant Safety 90 87 -3.4 3
4 Low Risk 91 93 2.1 2
5 High Risk 85 82 -3.6 3
Highest Risk 86 82 -4.8 4 Total 500 500 0.4 21
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 21 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 8. Conclusions and Findings
8.1 Conclusions and Findings • Significance of the Credit Risk is realized and given high priority by
regulators and banking , finance and insurance sectors
• There is immense need for Expert System (CREES), as on now all credit
risk decisions of MSME are carried out manually and causes lot of delay
• The methodology Evolutionary Neurofuzzy logic captures the knowledge
of human experts in their domain languages
• CREES Expert system is friendly to Credit Rating Executives and MSME
Users
8.2 Scope for further research 1. Development of Data warehouse of the industry-wise, business-wise
knowledgebase of credit rating frameworks
2. Data mining techniques are needed to explore the hidden knowledge
patterns in the data sets.
3. Testing CREES with international Credit Data sets
8.3 UGC MRP Approval, Funding and Recognition of work Researcher’s had applied for Minor Research Project proposal based on
present research work to University Grant Commission (UGC), New Delhi.
This MRP was approved and funded Rs 1,20,000/- by UGC in Nov.2012
and the work is in progress
8.4 Best Paper of the International Conference Award
The paper presented Expert System Design for MSME Credit Risk Rating
International Conference on Management of MSME - MSMECON-2010 at
Institute of Management Technology (IMT) in Nagpur has been awarded as
Best Paper of Conference with Cash prize of Rs. 10,000/-
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 22 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 9 Research Publications
The researchers have published their work and results in various international
journals and presented in conferences which are listed in the following tables.
9.1 Research Publications
Sl. No
Date
Paper Title Journal Profile Publisher IF
1 Nov. 2012
HealthCare Industry Credit Risk Evaluation Expert System
Submitted to International Journal of Applied Soft Computing
Elsevier Publications
2.8
2 Oct. 2012
Credit Risk Evaluation Using Knowledge Mining
International Journal of Knowledge Engineering ISSN 0976-5816 Volume 3, Issue 2, 2012, pp.178-183
BIOINFO publications, USA
4.5
3 Jan. 2012
Expert System Design for credit risk evaluation using Neuro Fuzzy logic
Expert Systems the Journal of Knowledge Engineering ISSN- 1468-0394 Volume 29, Issue 1, pp 56-69
Wiley Blackwell publications
1.23
4 Jun. 2010
The Survey of Credit Risk Assessment Techniques
International Journal of Technology and Applied Sciences ISSN 0975-1416 Volume 2, Issue 1, pp. 61-70
Graphic Era Deemed University, Dehrdun
5 Jun. 2010
Knowledgebase design for Credit Risk Evaluation is using Evolutionary Neuro Fuzzy Logic
International Journal of Pharma and Bio Sciences, ISSN No 0975 – 6299
Elsevier Publications
0.47
6 July- Dec. 2008
Industrial Loan Processing Using Neuro Fuzzy Logic
Journal of Intelligent Information Processing (IJIIP), ISSN0973-3892, Volume 2,Isuue -2, pp. 305-318
Serial Publications New Delhi
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 23 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
9.2 Paper presentations in International and National conferences
Sl. No
Period Title of the Paper Presented
Conference Hosted by
1 Jan. 2013
Accepted the paper for presentation Hospitality Industry Credit Risk Evaluation
National Conference on Risk Management in Banking and Insurance and Financial Services
Institute of Public Enterprise, Hyderabad
2 17-18th Feb. 2012
Innovative Credit Risk Management
National Conference on Innovative Management Practices
Banarasdas Chandiwala Institute of Professional Studies, New Delhi
3 10 to 12th March 2011
Knowledgebase design for Credit Risk Evaluation is using Evolutionary Neuro Fuzzy Logic
National Conference in Computational Neuro Science
SIBACA, Lonavala., Maharastra.
4 17th - 18th Sep. 2010.
Expert System Design for MSME Credit Risk Rating
International Conference on Management of MSME-MSMECON-2010
Institute of Management Technology (IMT), in Nagpur
5 2nd – 3rd March 2008
Knowledgebase System Design for Credit Risk Evaluation using Neuro Fuzzy Logic
National Conference on Knowledge Mining
Annamalai University, Chidambaram, Tamilnadu
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 24 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
Chapter - 10 References
10.1 Bibliography
This research work comprises an exhaustive survey of credit risk literature of
text books, websites, credit policy documents, interactions with experts and
study of about 300 most relevant articles on credit risk evaluation from
reputed journals such as IEEE transactions, machine intelligence, business
journals and proceedings of various symposiums some selected articles from
the survey are discussed by way of illustration.
10.2 Text books and documents studied
R.V.Kulkarni and B.L. Desai, Knowledgebased Applications in Banking
Sector, New Delhi, New Century Publications, 2004.
Bart Kosko, Neural Networks and Fuzzy Systems, Prentice Hall of India
Pvt. Ltd, New Delhi Eastern Economy Edition 2000
Stamatios V.Kartalopoulos, Understanding Neural Networks and Fuzzy
Logic, IEEE Press Eastern Economy Edition, 2003
Lending Policy Karnataka State Financial Corporation 2005-2006
C.R.Kothari Research Methodology Methods and Techniques, New Age
International Publishers, 2008.
Programme on Risk Management Canara Bank Staff Training College.
Bangalore.
10.3 Research journal articles
1. Peter, Duchessi and Salvatore Belardo. (July/August 1987). Lending
Analysis Support System (LASS) an application of knowledge based
system to support commercial loan analysis margin credit evaluation
system, IEEE Transactions systems, man on Cybernetics, 17 (4), pp.
608-616.
2. Lyn C.Thomas. (2000). A survey of credit and behavioral scoring:
forecasting financial risk of lending to consumers, International Journal of
Forecasting 16, pp. 149-172, www.elsevier.com/locate/ijforecast.
3. Pang Sulin, Liu Yongqing and Li Rongzhou. (June 28-July 2,2000). The
Optimal Design on The Decision Mechanism of Credit Risk for Banks
With Imperfect Information, Proceedings of The 3 World Congress on
Intelligent Control and Automation, Hefei, P.R. China.
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 25 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
4. Sushmita Mitra and Yoichi Hayashi (2000) Neuro–Fuzzy Rule
Generation: Survey in Soft Computing Framework.
5. Markus Kern / Bernd Rudolph. (January 2001). Comparative Analysis of
Alternative Credit Risk Models – an Application on German Middle
Market- Loan Portfolios, Center for Financial Studies, an der Johann
Wolfgang Goethe-University aunusanlage, 6 D-60329.
6. Frankfurt am Main. Rong Zhouli, Su-Lin Pang, Jian Min Xu. (Nov. 2002).
Neural networks credit risk evaluation model based on back propagation,
Conference on Machine Learning and Cybernatics Beijing.
7. Uwe Schmock Ernst Young. (2003). Performance of Modern Techniques
for Rating Model Design, Master Thesis Master of Advanced Studies in
Finance.
8. Adel Lahsasna. (June 2004). Evaluation of Credit Risk Using
Evolutionary-Fuzzy Logic Scheme No 16, ECB Publications Occasional
paper series.
9. Chorng-Shyong Ong, Jih-JengHuang, Gwo-Hshiung Tzeng. (2005).
Building credit scoring models using genetic programming, Expert
Systems with Applications, pp. 1-7.
10. B.K.Kim and Ram R. Bishu. (2006). Uncertainty of Human error and
Fuzzy approach to Human Reliability Analysis, International journal of
Uncertainty Fuzziness and Knowledge System, 14(1) World Scientific
Publishing Company, pp. 111-129.
11. Yuan Hua, Chen Xiaohong. (2007). Credit Risk Assessment Revisited
Methodological Issues and Practical Implications, working group on risk
assessment, 1-4244-1312-5/07/$25.00.
12. Jozef Zurada. (2007). Rule Induction Methods for Credit Scoring Review
of Business Information Systems, Second Quarter, 11(2) E-mail:
[email protected]), University of Louisville, pp. 11- 22.
13. Viresh Moonasar. (31 May, 2007). Credit Risk Analysis using Artificial
Intelligence: Evidence from a Leading South African Banking Institution
Research Report: Mbl3 presented to the Graduate School of Business
Leadership University of South Africa.
Desai Karanam Sreekantha Ph.D Synopsis – April. 2013 Page 26 of 27
Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
14. Yuansheng Huang, Chengfang Tian. (2008). Research On Credit Risk
Assessment Model of Commercial Banks Based on Fuzzy Probabilistic
Neural Network, The International Conference On Risk Management &
Engineering Management 978-0-7695-3402-2/08 $25.00 © 2008 Ieee,
Doi 0.1109/Icrmem.2008.33.
15. A.Lahsasna, R.N.Ainon, Teh Ying Wah. (May, 2008). Intelligent Credit
Scoring Model using Soft Computing Approach, Proceedings of the
International Conference on Computer and Communication Engineering,
Kuala Lumpur, Malaysia.
16. Wai Chuen Wong, Yao Xiao, Le Lei and Xinjiang Guo. (September,
2008). Research on Credit Risk Evaluation Model Based on LVQ Neural
Network. Proceedings of the IEEE International Conference on
Automation and Logistics Qingdao, China, 978-1-4244-2503-7/08/2008
IEEE.
17. R.V.Kulkarni and Prof.B.L.Desai. Knowledgebase Applications in
Banking.
18. Shorouq Fathi Eletter, Saad Ghaleb Yaseen and Ghaleb Awad Elrefae.
(2010). Neuro-Based Artificial Intelligence Model for Loan Decisions,
American Journal of Economics and Business Administration 2(1), ISSN
1945-5488 © 2010 Science Publications , pp. 27-34.
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Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic
19. Adel Lahsasna, Raja Noor Ainon and Teh Ying Wah. (April, 2010).
Credit Scoring Models using Soft Computing Methods, The International
Arab Journal of Information technology, 7(2), pp. 115-123.
20. Shaomei Yang. Study on Commercial Banks Credit Risk Based on AGA
and Camel Rating System, Second International Workshop on
Knowledge Discovery and Data Mining.
21. Linda Delamaire. (February 2012). Implementing a credit risk
management system based on innovative scoring techniques, Linda
Delamaire University of commerce and social science department of
economics the University of Birmingham.
22. CreditWeek Special Report. (January 25, 2012). The Global Authority On
Credit Quality Leveraged Debt in 2012, The Markets Are Open, But
Credit Risk Remains Special Report.
(Desai Karanam Sreekantha) (Prof. (Dr.) R.V. Kulkarni)
Date : Professor & Head
SIBER, Kolhapur