chapter - 5 expert system prototype design and...

Desai Karanam Sreekantha. Ph.D Thesis in Faculty of Computer Studies 30 of 281

Chapter - 5 Expert system prototype

design and development

5. 5.1 Expert system prototype design and development

131

5.1.1 Introduction to Expert System Builder(ESB) software 132

5.2 5.2.1 CREES design 133

5.3 Database design 139

5.3.1 Manufacturing industry 140

5.3.2 Trading industry 144

5.3.3 Service industry 144

5.4 User interface design 146

5.4.1 Manufacturing industry data entry screens 148

5.4.2 Trading industry data entry screens 160

5.4.3 Healthcare industry – nursing home data entry 162

5.4.4 Hotel industry data entry screens 164

5.5 5.5.1 Credit risk assessment design 166

Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic Page 131 of 281

5.1 Expert system design for MSME credit risk rating

Expert systems represent an opportunity to assist users lacking expertise in a specific area to

carry out complex tasks, promote efficient governance and enable sustainable decision-

making in developing regions. Expert knowledge is a combination of a theoretical

understanding of the problem and a collection of heuristic problem-solving rules that

experience has shown to be effective in the domain.

Introduction to expert system builder (ESB) software

This paper implements the proposed expert systems prototype using Expert System Builder

(ESB) software system shown in Figure- 3. This software tool allows the creation of a

knowledgebased expert system, i.e. the software that processes the data like a human

expert. The development of the expert system has three stages, the first and second stage

builds the system, the third accesses it. The ESB Question Editor shown in Figure- 5 and

Knowledge Acquisition Program shown in Figure- 6 are concerned with the first stage. The

Expert System Builder User Interface program shown in Figure- 7 is the third and the last in

the series of ESB programs. This program amalgamates the question structure developed

with the Question Editor. The set of questions and choice of probable answers are built in the

system. The Knowledge base is developed with the Knowledge Acquisition Program.

The Knowledge Acquisition Program is used to build nearly 500 clients sample cases in the

system. User interface module shown in Figure- 7 helps us to choose a test case, where the

input options are selected to find out the most probable solution to the given problem. The

ESB User Interface helps the user in making decisions by asking several questions.

Figure-13 Expert system builder software

Desai Karanam Sreekantha. Ph.D Thesis in Faculty of Computer Studies Page 132 of 281

The final analysis of the sample client interpreted by this software is displayed in Figure- 8.

Figure- 14. Question editor

Figure- 15. User interface


Figure- 16. Knowledge acquisition

Figure-17. Client data records

The set of guiding rules in taking the credit risk decision by credit rating executives of the

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.

5.2 Credit Risk Evaluation Expert System design

The process involved in the development of an expert system

An expert system is created by „knowledge engineers‟ who analyze how human experts make

decisions and translate this into terms that a computer can understand, using facts and if-then

rules. This involves understanding what the experts know, through observation, workshops

and scoping of the processes and business rules used to form the knowledge base. This

process of codifying an expert's knowledge into components for representing logic flows is

time consuming and complex. Particularly since a good knowledge base can hold the

combined knowledge of many experts and forms a complex reasoning structure to interact

with the inference engine. Comprehensive analysis of the logic built into the system must be

undertaken by evaluating test data through a combination of manual and automated testing.


The Expert system prototype for credit risk evaluation has been designed as shown in

Figure-17.

Figure- 18 The Expert system prototype for credit risk evaluation

This Expert system is an intelligent computer program that uses expert‟s knowledge and

inference procedures to assess the credit risk of MSME client. The advantages of expert

system are superior problem solving, ability to save and apply knowledge and experience to

problems, reduced response time for complex problems, the ability to look at problems from a

variety of perspectives. The Expert system prototype consists of four major components such

as Language Interface, Knowledgebase, Inference Machine and Explanation facility.

Language interface: Is used for designing, creating, updating, and using expert systems.

The overall purpose of the user interface is to make the development and use of an expert

system easier for users and decision makers.

Current client database: It stores the facts collected from various sources about the client

under consideration

Client credit history database: It stores all the credit history of the clients and their credit

performance for extending the credit facility.

Credit experts knowledgebase: It assembles the knowledge of multiple human credit rating

experts and stores all relevant information, data, rules, cases, and relationships used by the

expert system.

Credit Portfolio Knowledgebase

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

KnoExplanation Knowledgebase

wledgebase

Inference Engine

Credit Experts Knowledgebase

Explanation


Credit portfolio knowledgebase: The various types of risks, which banks face in the credit

domain, do not necessarily reside in a single transaction; rather, it is in the portfolio. It is odd

that even today most credit risk analysis is based on a transaction rather than portfolio.

It stores all the information about the various portfolios of business. This will help in taking

credit decision of the client business portfolio.

Inference engine: It seeks information and relationships from the knowledge base and to

provide answers, predictions, and suggestions the way a human expert would. The inference

engine must find the right facts, interpretations, and rules and assemble them correctly.

Current client database: This is the database of clients currently having account with bank

Knowledge acquisition facility: The overall purpose of the knowledge acquisition facility is

to provide a convenient and efficient means for capturing and storing all components of the

knowledge base.

Credit processing block: It evaluates the credit worthiness of the client using credit rating

framework. The computation of credit risk uses the client credit history accumulated from

previous experiences and relevant client references.

Credit models knowledgebase: It is the warehouse of credit rating models applicable to

various MSME clients.

Fuzzy inference engine: It evaluates the client‟s numerical data in terms of fuzzy linguistic

variables and grades the client as Best, Good, Average and Poor.

Credit advising block: This block converts the credit rate range from AAA, AA to D in to

Highest Safety, Significant Safety and High Risk to Default.

Neural network block: It records the new facts, needed for future evaluation and also

updates the knowledgebase.

3.8 Soft Computing technique – evolutionary neuro fuzzy logic It is well known that the intelligent systems, which can provide human like expertise such as

domain knowledge, uncertain argument, and adaptation to a noisy and time varying

environment, are important in solving practical IC problems. A Fuzzy Logic controller (FLC)

can utilize the human expertise by storing its essential components in a rule base and

database, and perform fuzzy reasoning to infer the overall output value. However there is no

systematic way to transform experiences of knowledge of human experts to the knowledge

base of a FLC. There is also a need for adapting of some of the learning algorithms to set the

outputs within the required error rate. This paper adopts neuro fuzzy techniques to implement

the credit risk evaluation process. Fuzzy systems are suitable for extracting the knowledge

from data and have the advantage that the if-then rules that characterizes the relations

between the input and the output variables are natural.


The application of fuzzy logic involves three steps, the conversion crisp numerical values to a

set of fuzzy linguistic variables, the application of fuzzy inference system and subsequent de-

justification. The fuzzy systems do not, however, have learning capabilities. In certain living

organisms specialized cells called neurons form a complex network, which receives

processes and sends information between parts of the nervous system. The centre of this

network is in the brain, which stores and analyses the information. Neural networks are

uniquely able to learn.

The neuro-fuzzy system has the advantage of both fuzzy and neural systems. It has the

ability to learn from the data and transparent at the same time. The Neuro-Fuzzy system is

multi-layered architecture. The first layer consists of input variables; the middle (hidden) layer

contains fuzzy rules and the third one comprises output variables. Fuzzy sets are encoded as

(fuzzy) connection weights. Neuro fuzzy logic incorporates fuzziness into the neural network

framework. A Neuro-Fuzzy system is a fuzzy system, which is trained using a learning

algorithm derived from neural network theory to decide its parameters (fuzzy sets and fuzzy

rules) after processing data samples.

Fuzzy rules are a popular basis for classifiers due to their rather intuitive and understandable

application. The use of linguistic variables eases the readability and interpretability of the rule

base. Automatic induction of fuzzy rules from data is therefore an interesting topic. A

common approach to automatic rule generation is based on neuro-fuzzy systems. In many

practical domains the available training data is more or less unbalanced, i.e. the number of

cases of each class varies. This causes problems for many classification systems and their

associated learning algorithms. This is especially obvious if the classes are not well

separated. In such cases classifiers tend to predict the majority class. This may be

completely reasonable to minimize the error measure. However, the classifiers do not take

into account the semantics of the classes. Some classes may be more related to each other

than others, and thus misclassifications between them may be less severe. Classes may not

behave symmetrically, i.e. falsely classifying class A as B is more expensive than falsely

classifying class B as A. In many domains it is desirable or even necessary to let the user

model these asymmetries.


A straightforward way to model them would be to directly specify the costs of each possible

misclassification. This is the basic idea of our approach, which we have incorporated into the

learning system. For the implementation of the presented approaches we modified the

NEFCLASS (NEuro Fuzzy CLASSification) model, a neuro-fuzzy model for data analysis. It

was designed as an interactive classification tool, that allows the user to influence the

automatic learning and classification process.

Authors applied the modified NEFCLASS to a machine vision problem where the severeness

of misclassifications is asymmetric. In classification task too many false negatives can

completely prevent the correct recognition of objects, whereas false positives lead `only' to

considerably longer execution times. The next sections give more detailed descriptions of the

vision task and the original NEFCLASS system.

The NEFCLASS Model

Fig -19 3-layer structure of NEFCLASS

This section describes the original NEFCLASS model. NEFCLASS can be viewed as a

special 3-layer feed-forward neural network (see Figure- 14). The units in this network use t-

norms or t-conorms instead of the activation functions common to neural networks. The first

layer represents input variables, the middle (hidden) layer represents fuzzy rules and the

third layer represents output variables. Fuzzy sets are encoded as (fuzzy) connection

weights. To enable the use of the same linguistic terms in the antecedents of different rules

the weights can be coupled and thus get changed together. NEFCLASS represents always

(i.e. before, during and after learning) set of fuzzy classification rules like

If (x1 is µ1) and (x2 is µ2) and (xn is µn) then Pattern (x1, x2, x3, x4……….. xn) belongs to class i1

where the µ1, µ2 … µn are fuzzy sets. The inputs are real-valued. By propagation through the

coupled fuzzy weights their membership degrees to the individual antecedents is determined.

The rule units accumulate these membership degrees with a t-norm (usually the minimum is

used).


Each rule unit is connected to exactly one output unit (i.e. the consequent of the represented

rule). The output units use a t-conorm to accumulate the rules' activations, where the t-

conorm is usually the Maximum. The predicted class is determined by a winner-takes-all

principle between the output units. It is possible to create a NEFCLASS rule base from

scratch using training data, or to initialize it by prior knowledge in form of fuzzy rules. If a

classifier is learned from data, two phases are needed:

1st learning phase: the rule creation

During rule creation an initial fuzzy partitioning for each variable is created. This is given by a

fixed number of equally spaced triangular membership functions. The combination of the

fuzzy sets forms a grid in the data space, i.e. equally distributed overlapping hyper boxes.

Then the training data is processed, and those clusters, which cover areas where data is

located are added as rules into the rule base of the classifier.

2nd learning phase: the fine-tuning of fuzzy sets

After the rule base has been created, the membership functions are fine-tuned by a simple

heuristic. For each rule a classification error is determined and used to modify that

membership function that is responsible for the rule activation (i.e. which yields the minimal

membership degree of all fuzzy sets in the rule's antecedent). The modification results in

shifting the fuzzy set, and enlarging or reducing its support, such that a larger or smaller

membership degree is obtained depending on the current error. The learning procedure

takes into account the semantic properties of the underlying fuzzy system. This results in

constraints on possible modifications applicable to the system parameters. NEFCLASS

allows to impose different restrictions on the learning algorithm, e.g., membership functions

must not pass its neighbors, must stay symmetrical, or membership degrees must add to 1


5.3 Datebase design – Manufacturing industry

Figure-20 Database for manufacturing industry

Table -15 Database tables for manufacturing industry

Sl. No.

Table Name Table Purpose Description

1 account Behavior Client Account Transaction behavior history

2 admininfo CREES System Administrator information

3 assestliablities Clients Business assets and Liabilities data

4 associateconcerns Client Business Associate Concerns Information

5 authorization Authority Users to Process Client Credit Data

6 baddebts Client‟s Data about Bad Debts

7 balancesheet Client Business‟s Balance Sheet Details

8 bankInfo Client Bank‟s Address and other details

9 busdetails Clients Business Registration and other Information

10 businessframework Business Credit Rating Parameters Framework

11 businessratios Client Business financial ratios

12 capacity utilization Client Business Plant capacity Utilization

13 collateral Client loan collaterals data

14 competence Client Business Competency information

15 creditratings Credit ratings of the client for each credit parameter

16 clientWeight The client Weight for each of the parameters

17 directorlist Client business board of directors data

18 empdetails Client‟s business employee information

19 existingcredit Client existing credit facilities availed

20 guranterinfo Client loan guarantor data

21 informated Client knowledge about the business and credit facilities

22 Infrastructure Client‟s business infrastructure details

23 Insurance policies Client insurance policies data

24 involvement The involvement of client in

25 keydebtcreditors Client business Key creditors and debtors


26 keysuppliers Client Business Key suppliers information

27 landetails Client factory land details

28 liquidity Client business assets liquidity data

29 loanpupose Client loan purpose details

30 marketing Client business marketing information

31 machinespec Manufacturing client plant machinery specifications

32 nusringhomeclient Service industry nursing home client data

33 otherbank Client account data in other banks

34 pastloan Client past loan repayment data from various banks

35 plantcapacity Manufacturing client plant capacity details

36 productpricing Client product pricing strategy

37 profitabilitystrategy Client profitability strategy

38 profitloss Profit and loss a/c of the client

39 projectcost Client project cost assessment

40 pastperformance Information about the previous years client performance

41 realassetsowned Real assets owned by the client business

42 recurringcost Recurring costs of client business

43 reqdocuments The list of documents to attached with loan application

44 salesinfo Client product sales information

45 spousedetails Main client spouse details

46 suppliersbenificiaries Details of suppliers and beneficiaries

47 tradingframework Trading sector framework

48 utilities Manufacturing sector utilities requirements information

49 userinfo Expert System users data

50 willingness The information on willingness of the client

Figure-21 Table shema for business details

Table – 16 Table schema for business details

Sl. No.

Attribute Name Attribute Description Data Type and Size

Sample Value

1. ClientId - Primary Key

Client Identification Variable character - 10

0101010101

2. ApplnId - Loan Application Variable 0101010101


Primary Key Identification character - 10

3. Objectives Business Objectives Variable character -200

4. Experiences Business Past Experience

Variable character- 200

5

5. BusinessSize Size of Business Integer 0-Micro, 1-Small and 2 Medium

6. TaxId Tax Identification code Integer -11 0101010101

7. BusinessBrief Business Description Variable character - 200

Description of Business activity

8. PAddline1 Permanent Address Line-1

Variable character - 50

Business Permanent Address

9. PAddline2 Permanent Address Line-2


Business Permanent Address

10. PPlace Permanent Place Name Variable character - 50

Business Place Name

11. PTaluk Permanent Taluk Name Variable character - 50

Business Taluk Name

12. PDist Permanent District Name Variable character - 50

Business District Name

13. PPhoneNo Permanent Business Phone No

integer Business Phone Number

14. PCellNo Client Mobile Numbers Integer - 50 Client Cell Nos

15. PFaxNo Client Fax Number Integer -11 Business Fax No

16. OccupancyYears Number of years in Business in present place

Integer 5

17. BusStartedDt Business Started Date Date

18. Registered Business Registered or not

Yes / No

19. IncorpotedDt Business Incorporated Date

Date

20. RegistrationNo Business Registration Number


21. RegistratedWith Business Registration Authority

Variable character -100

22. LegalStatus Legal Status of Business Registration

Character - 1 0-Propritary, 1-Perternership, 2-Private Ltd, 3- Public ltd

23. NoofEmployees Number of Employees in Business

Integer -2 100

24. SCSTOBCMinority Business belongs to SC/ST/OBC/ Minority

Character -1 0-SC,1-ST, 2-OBC, 3-Minority

25. OperatingPastYears Number of Year in Current Business

Character -1 4


26. PremisesStatus BusinessPlace is ownership

Character -1 0-Owned 1-Leased 2-Rented

27. TotAssetVal Total Assets Value Integer -15

28. TotCashReceivable Total Cash Receivable Integer -15

29. ClientExperience Client‟s past experience Variable character - 40

30. DailySalesVol Daily Sales Volume Integer – 11

31. NoDaystoSell Number days it takes to sell the product

Integer – 11

32. NoOfCustomers Number of Current customers

Integer -4

33. RawMaterial Raw Material are indigenous / Imported

Character 1

34. PaymentDelay No of days Payment is Delayed

Integer-3

35. SalesRevenue Amount earned through Sales revenue

Integer-11

36. NetIncome3Days Net income for past three years

Integer -11

37. SalesProceeDays No of Days Sales proceeds to realize

Integer-11

38. MajorByers List of major customer names

Variable Character -100

39. TotBorrowings Total Loan Borrowings Integer -11

40. CrSaleVol Total Credit Sales Volume

Integer -11

41. No.Competitors Number of Competitors in Business

Integer-11

42. ProductsTraded Number of Products Traded

Integer-2

43. ProfitPast3years Amount of Profits Earned for past years

Integer-11

44. AmtDrawnFmly Amount With drawn by Family members

Integer-11

45. WebAddress Business Website Address

Variable Character - 100

46. AnyOther Highlights if Any Variable Character - 100

47. Inventories Total Inventories Value Integer-11

48. PastWork Clients Past work Integer-11

Credit Risk Evaluation of MSME using Evolutionary Neuro Fuzzy Logic 3 of 281

Figure- 22 Table shema for client master


Database design - Trading industry

Figure-23 Trading industry credit rating framework table

Database design - Service industry - healthcare - nursinghome

Figure-24 Service industry – nursinghome list of tables


Database design - Service industry - healthcare - nursing home

Figure-25 Nursinghome credit rating framework table

Database Design- Service industry - healthcare - nursinghome

Figure-26 Proposed Nursinghome table structure


Figure-27 NurisngHome ProjectCost

Figure-28 Nursing home Client Weights


CREES Presentation Layer – Screen Layouts Credit Risk Evaluation Expert System Screen Layouts

Figure-29 Credit Risk Evaluation Expert System Desktop Screen

Figure- - 29.1 CREES Risk Evaluation Manu


Figure- 29.2 CREES Help Menu

Figure-29.3 System users Information


Figure-29.4 Manufacturing Client Data Capturing

Figure-29.5 Client Business Information


Figure-29.6 Client Personal Profile

Figure-29.7 Client Business Assets and Liabilities


Figure-29.8 - Lead Directors Personal Accounts

Figure-29.9 Directors List


Figure-29.10 Management Involvement in the project

Figure- 29.11 Client Business Address


Figure-29.12 Clients Business Information

Figure-29.13 Client availed Credit Facilities information


Figure – 29.14 The details of client‟s business Banks accounts in various banks

Figure-29.15 Client Business major creditors and debtors


Figure-29.16 Major Suppliers and Beneficiaries of client business

Figure-29.17 Client‟s employee details


Figure-29.18 Product Profile information

Figure-29.19 Client Business Profit and Loss account


Figure-29.20 Client business balance sheet

Figure-29.21 Client project cost information


Figure-30 Manufacturing client main menu

Figure-30.1 Power requirement risk assessment


Figure-30.2 Financial resources risk assessment

Figure-30.3 Client competence risk assessment


Figure- 30.4 Loan risk purpose assessment

Figure-30.5 Project cost risk assessment


Figure-31. Trading sector risk assessment

Figure-31.1 Management risk quality assessment


Figure-31.2 Client account behaviour and track record assessment

Figure-31.3 Industry risk profile assessment


Figure-32. Service sector – nursinghome desktop

Figure-32.1 Nursinghome background menu options


Figure-32.2 Nursinghome promoter‟s experience data capture

32.3 Promoter‟s Basic Information


Hospitality industry data entry screens

Figure-33. Hospitality industry desktop

Fig 33.1 Hotel utilities risk assessment

chapter - 5 expert system prototype design and...

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