Expert System for Banking Credit Decisions

Expert System for Banking

Credit Decisions

Intelligent Decision Support System – DV2408

5/31/2011

Blekinge Institute of Technology, Karlskrona, Sweden.

Group members:

Musharaf Hameed (760719-6859)

Bilal Ilyas (830720-7210)

Zia Mustafa (830116-5331)

Muhammad Salman (820203-4511)

Iqra Javed (860927-1120)

1 | P a g e

Table of Contents

Abstract ......................................................................................................................................................................... 2

1 Introduction ......................................................................................................................................................... 3

2 Division of Labor .................................................................................................................................................. 4

3 Project Analysis ..................................................................................................................................................... 5

3.1 Background .................................................................................................................................................. 5

3.2 Problem Definition .................................................................................................................................... 12

3.3 Possible Solution ........................................................................................................................................ 12

4 Project Design ..................................................................................................................................................... 18

4.1 System Design ............................................................................................................................................ 18

4.2 Use Case Diagrams ..................................................................................................................................... 21

5 Project Implementation and Simulation Results ................................................................................................ 25

5.1 Simulation .................................................................................................................................................. 25

5.2 Process Model ............................................................................................................................................ 27

5.3 Intelligence ................................................................................................................................................. 27

6 Future Directions ................................................................................................................................................ 28

7 Conclusions ......................................................................................................................................................... 29

References ................................................................................................................................................................... 30

2 | P a g e

Abstract

The problem of credit-risk evaluation in banks is a very challenging and important financial analysis problem.

Expert systems can perform very well for this complex and unstructured problem when compared to more

traditional statistical approaches. Expert systems with explanation for intelligent decision making can achieve a

high predictive accuracy rate. This paper presents an Expert System for evaluating and supporting credit

decisions on the banking sector, which uses the credit rating weights for each factor that affecting the decision

of the credit. This work has established an expert system tool that aids the decision maker to issue the right

decision with familiar and easy-to-use interface. There are two main methods have been applied to acquire the

knowledge of credit evaluations systems in banking with effectiveness, efficiency and correctness, they are

direct and indirect methods. The knowledge has been verified and evaluated from different sources, such as

periodicals, references and books, banks reports and publications, bank policies, and researches, working

papers, and banks studies, and then some modifications and enhancements have been done to reach the final

system.

3 | P a g e

1. Introduction

The banks face very challenging and important financial problems, because there is a weakness in credit-risk

assessment and completion of loan package. The banks crises studies in different countries, whether advanced

or developed, point out that most of the countries which are exposed to financial crises are due to the main

reason of the overdue credits (financial defaults). It is worth mentioning that 131 countries suffered from bank

crises, which differ in its magnitude during the period (1975-2010). The reasons for these problems are mainly

due to the lack in using good credit ratings and assessment, which might indicate a high risk of defaulting on a

loan, and thus leads to high interest rates. In other words, there is a financial failure, because of the weakness

in credit-risk assessment by the bank granting the credit. Those problems occurred to the decision maker

because the great number of factors that should be considered with different weight according to each case

and also the lake of existence knowledge. This financial failure mainly is due to the lack of experts in banking

domain.

To solve this problem an expert system has been suggested to keep the related knowledge to be used as an

intelligent decision support system. The primary goal of the expert systems is to make expertise available to

decision makers. Expert Systems are also referred to as knowledge based decision support system, are an

application of AI that helps decision makers make better decisions. 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.

This project is an assignment for the course “Intelligent Decision Support System (IDSS)” at Blekinge Institute

of Technology, Karlskrona, Sweden.

4 | P a g e

2. Division of Labor

This project really provides us a chance to enjoy a team work as every member of our group had shown keen

interest and full devotion in this project. Every group member exercised their responsibilities according to their

job designation that was decided before the start of the project.

One of our team member Mr. Bilal Ilyas has four years of working experience in banking credits section. So we

have tried to best utilize his experience and concepts in this project.

Musharaf Hameed 760719-6859 Project Manager

Bilal Ilyas 830720-7210 Project Analyst and Designer

Muhammad Salman 820203-4511 Developer

Zia Mustafa 830116-5331 Testing & Documentation Coordinator

Iqra Javed 860927-1120 Analyst & Documentation Coordinator

5 | P a g e

3. Project Analysis

There are two main methods have been applied to analyze the problem and acquire the knowledge of banking

credit decision system, they are direct and indirect methods. Indirect methods have been used in order to

obtain information that cannot be easily expressed directly. Direct method (i.e. document analysis) is used to

classify entities within the domain. These methods attempt to determine how the expert makes their

decisions. Document analysis involves gathering information from existing documentation. This process has

been achieved through deferent sources such as: periodicals, references and books, banks reports and

publications, banks publications and policies, and researches, working papers, and banks studies.

The document analysis and knowledge acquisition processes facilitated us in building and maintaining

knowledge-base and mathematical models (i.e. the scoring system). We started with acquiring and classifying

the domain concepts. By domain concepts, we mean a full declaration of concepts, relations between

concepts, and relations between properties, which have been explained in the later sections.

3.1 Background

In order to better understand the problem and proposed solution, first there is need to understand some of

the terms used in financial and banking sector, such as: loan, types of loans, credit scoring and credit rating,

and credit principles.

3.1.1 Loan and Types of Loans

A loan is a type of debt. Like all debt instruments, a loan entails the redistribution of financial assets over time,

between the lender and the borrower.

In a loan, the borrower initially receives or borrows an amount of money, called the principal, from the lender,

and is obligated to pay back or repay an equal amount of money to the lender at a later time. Typically, the

money is paid back in regular installments, or partial repayments.

6 | P a g e

The loan is generally provided at a cost, referred to as interest on the debt, which provides an incentive for the

lender to engage in the loan. In a legal loan, each of these obligations and restrictions is enforced by contract,

which can also place the borrower under additional restrictions.

There are three general types of loans offered by banks or other financial institutions:

Secured loans: A secured loan is a loan in which the borrower pledges some asset (e.g. a car or

property) as collateral for the loan.

Unsecured loans: Unsecured loans are monetary loans that are not secured against the borrower's

assets.

Demand loans: Demand loans are short term loans (typically no more than 180 days) that are atypical

in that they do not have fixed dates for repayment and carry a floating interest rate which varies

according to the prime rate. They can be "called" for repayment by the lending institution at any time.

Demand loans may be unsecured or secured.

Loans can also be subcategorized according to whether the debtor is an individual person (consumer) or a

business. Common personal loans include mortgage loans, car loans, home equity lines of credit, credit cards,

installment loans and payday loans. The credit score of the borrower is a major component in and

underwriting and interest rates of these loans. The monthly payments of personal loans can be decreased by

selecting longer payment terms, but overall interest paid increases as well.

Loans to businesses are similar to the above, but also include commercial mortgages and corporate bonds.

Underwriting is not based upon credit score but rather credit rating.

3.1.2 Credit Score and Credit Rating

A credit score is a numerical expression based on a statistical analysis of a person's credit files, to represent the

creditworthiness of that person. A credit score is primarily based on credit report information typically sourced

from credit bureaus.

Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by

lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine

who qualifies for a loan, at what interest rate, and what credit limits. Lenders also use credit scores to

determine which customers are likely to bring in the most revenue. The use of credit or identity scoring prior

to authorizing access or granting credit is an implementation of a trusted system.

Credit rating estimates the credit worthiness of an individual, corporation, or even a country. It is an

evaluation made by credit bureaus of a borrower’s overall credit history. A credit rating is also known as an

evaluation of a potential borrower's ability to repay debt, prepared by a credit bureau at the request of the

7 | P a g e

lender. Credit ratings are calculated from financial history and current assets and liabilities. Typically, a credit

rating tells a lender or investor the probability of the subject being able to pay back a loan.

3.1.3 Cs of Credit Principles

Credit risk evaluation and investigation is very complicated and intense. The information that is being gathered

could be getting strewn and scattered all over the place. The Cs of Credit helps in making the evaluation of

credit risk systematic. They provide a framework within which the information could be gathered, segregated

and analyzed. It binds the information collected into 5 broad categories namely Character, Capacity, Capital,

Conditions and Collateral.

3.1.3.1 Character

In analyzing Consumer Credit, decision maker have to consider the following:

Has the person declared bankruptcy in the past?

Does the person have a good credit record?

Does he/she have a stable job?

What is the level of education/experience?

What is the person earning and what is the earning potential?

Stability at the place of residence, whether rented or owned.

In analyzing Commercial Credit, decision maker have to consider the following:

The size of the operations.

The number of years in business.

The legal form of the business. By this one means 'retail', 'wholesale', 'service' or

'manufacturing'. Typically the incidence of business failures is high in the retail and service

segments. Is the business a parent, subsidiary or a division? Does the business have a holding

company? i.e. the structure of the business. Is the business a sole proprietor, partnership or

corporation? For sole proprietor or partnership type one would further seek personal

information on individual(s) running the business.

The number of employees. There are Industry specific Norms for 'Employees to Sales' ratio.

The management record of the company.

The location of the company.

Any previous evidence of fraud?

Any previous Insolvency record?

Any labor disputes or issues?

8 | P a g e

Are the products/service sold by the prospect complimenting products/service to the ones

that others may sell?

Is the business practice ethical?

Is the business seasonal/non-seasonal?

Is the business local/national or international? The economy of a business accordingly could

depend upon local/national or international economy.

Is there a growing or a going market for this business?

How willing is the prospect to share information?

How diligently does the prospect fill bank’s credit agreement/application?

What are the references saying?

Are there too many lay-offs especially of key personnel?

Are there any law suits pending against the company?

Is there any recent media coverage about the company? Is it positive or negative? Or are

there any rumors floating?

If the company's stock is publicly traded then see how its stock is performing?

One can also check the indices for a particular type of Industry to see how in general the

Industry is doing.

3.1.3.2 Capacity

What does credit decision maker analyze under this segment?

Capacity of the business to pay?

Capacity of the business in getting paid?

Capacity of the business to receive/absorb?

Capacity of the credit grantor to expose?

Sometime a business that decision maker is analyzing might not have the required capacity in kind but the

same could be latent and hidden in some other form. For example a start-up business should have a good

business blue-print of succeeding namely a good business plan. A contractor might have a good media

advertising plan, say an Ad in the local Yellow Pages. All this adds to the capacity of a business to carry on

trade and perhaps be successful.

Innovation, education, experience, knowledge would be some other considerations. The customer should be

able to foresee trends in the marketplace and blend accordingly. It should have plans both for good and bad

turns in the economy. Adoption of sound management techniques is important. Companies must remain

relevant with their processes, products and operate with speed in today's economic age.

9 | P a g e

Larger businesses should also have people that know how not just to manage the company but also its main

asset, its people.

The capacity of a business to pay its bills would stem from good cash-flow. A business could become cash

strapped if it does not collect its accounts receivable on time. Days Sales Outstanding is a measure of the

average number of days that a company takes to collect revenue after a sale has been made. A low DSO

number means that it takes a company fewer days to collect its accounts receivable. A high DSO number

shows that a company is selling its product to customers on credit and taking longer to collect money.

Days sales outstanding is calculated as:

Say if a business has a DSO of 55 days. This means that at an average this business gets paid by its customers in

55 days. So, in all probability, its capacity to pay its suppliers will be after 55 days. In this event a credit

decision maker should evaluate its borrowing capacity.

This would bring on the analysis of how the debt of the company is structured in terms of secured and

unsecured debt with the bank. Short term borrowing could be calculated as a percentage of the inventory and

accounts receivable on hand. Credit decision maker should look at the line of credit and see if there is capacity

for more borrowing. Also check for any negative occurrences as bad checks (cheques) or any default against

operating loans or covenants.

The capacity of customer’s product to influence payment is also important. If the product being sold is fiercely

competitive then it may not have the capacity to influence timely payment. It means end-use of loan or loan to

be utilized only for business activity. Competition definitely influences capacity.

3.1.3.3 Capital

Capital would refer to the financial resources obtained from financial records that a company may have in

order to deal with its debt. This portion of the credit analysis is the most important one. Weight is given on

Balance Sheet items and components like Working Capital (Current Assets - Current Liabilities), Net Worth

(Total Assets - Total Liabilities) and Cash Flow.

10 | P a g e

Credit analyst must know how to read financial statements and that too from the perspective of a creditor. He

should be able to see whether this company has the ability to absorb more debt and then where does this loan

fit in the overall debt-framework of this business. He should also evaluate to see if he can depend on the

numbers whether they are audited, unaudited or company prepared. If required speak with the firm or person

who has prepared the statements.

Leveraged borrowing depends on the equity/net worth that a company has and it is a good idea to see if the

company is committed to improve its borrowing-power by contributing to its Equity/Capital/Net Worth. One

way of doing this is by retaining all or portions of its earnings. Thus, keep an eye on the company's cash-flow

and cash-position.

But one must be cognizant of the fact that financial records are snapshots of the past and credit analysis is

trying to figure out the future. Thus all 5 Cs of credit are important in the overall analysis of a company or an

individual where you combine elements of the past to make a futuristic prediction.

3.1.3.4 Conditions

This refers to the external conditions surrounding the business that credit analyst is analyzing.

For example the construction industry might get influenced with the changes in the government's wide range

of policies on immigration, interest rates and taxation.

There might be likelihood that a company being under credit-risk evaluation deals in international trade and a

shift in the currency rates might have a detrimental or beneficial effect on it.

Business with local economies is prone to the social climate and their influence on the local society. A lot of

businesses became insolvent in the Ice Storm a couple of years ago in eastern parts of the US and Canada that

were totally dependent on the local economy.

3.1.3.5 Collateral

Assets pledged by a borrower to secure a loan or other credit, and subject to seizure in the event of default,

also called security. Asset is any item of economic value owned by an individual or corporation, especially that

which could be converted to cash. It would thus appear that collateral reduces the bank's risk when it grants a

loan even as it increases the costs to the bank (lender) because of increased documentation as well as the

costs of monitoring the collateral. The best collateral for any loan is the borrower's ability and willingness to

repay the same according to one school of thought.

11 | P a g e

Characteristics of good collateral are:

1) Standardization: Aside from eliminating any ambiguity between the parties the lender appreciates

the worth and re-sale ability of such asset in the event of default. It also leaves neither party in doubt

as to the value of such collateral and hence the amount of loan it can collateralize.

2) Durability: This is to ensure that the collateral will still be in useful condition and hence saleable

from after the maturity of loan. Hence it can still be sold in the event that the borrower could not pay

at maturity of the loan.

3) Marketability: Assets that have wide secondary markets represent better collaterals than those

with little or no secondary market at all.

4) Identification: Assets that cannot be easily moved such as real estate and machinery and

equipment represent better collateral. Collateral that can be identified by unique features or

characteristics such as house number, block, Society or nearest land marks that cannot be erased are

preferable to assets that have no distinguishing features.

5) Stability of Value: Banks prefer assets with ready secondary (resale) market as collateral. Assets

that are highly susceptible to rapid obsolescence render older models less valuable.

Thus in evaluating the degree of risk of a customer, information revolving around the 5Cs of credit is very

necessary.

12 | P a g e

3.2 Problem Definition

For our project we have chosen Personal Bank Loan from unsecured loans category as our targeted problem.

Formal problem definition is:

“An Expert System for evaluating and supporting banking credit decisions for personal bank loans ,

which are considered as one of the highest risk and unsecured loans that is granted to any individual

by the bank for various purposes that does not involve any collateral.”

3.3 Possible Solution

A Personal Bank Loan is by definition an unsecured loan granted to an individual by the bank for various

purposes. Such a loan includes the personal installment loan, tax loan, and overdraft accounts. Since it is

common practice by the banks that for such loans, no collateral of any kind is required from customers, the

risk of bad debts is greater than usual. For this reason, an Expert System for evaluating and supporting credit-

risk is proposed in reducing the risk of incurring bad debts.

Our proposed solution to be presented here is based on the four Cs of credit principles: capital, character,

capacity, and condition. These four credits’ (4C’s) criteria can be used by the bank to evaluate the forces and

type of risks to which the bank may be exposed. For example, character is an important intangible factor to

evaluate the reputation and integrity of any applicant for a personal bank loan.

We used the 4C’s criteria into a quantitative format so that a uniform decision-making scheme for personal

bank loans can be used. For instance, if a personal bank loan can be evaluated on the basis of a score point

system in which a scale of 0 to 100 is used, a decision can be made such that application scores greater than 55

points is to be regarded as favorable.

Definition of 4C’s criteria for a Personal Bank Loan

Capital

Character

Capacity

Condition

The debt/worth ratio

Credit history and the financial commitment to the debts involved

Manage money effectively to generate the funds to repay a loan

Economic and job conditions and relationship with banks

13 | P a g e

In quantifying the 4C’s criteria, the first step involved a basic “quantification” assessment on loan application.

From these assessments, it has been invariably found that the major concerns of banks on unsecured loans are

repayment ability and income stability of clients. A client is considered to fetch a stable income if he/she is

allocated a fix sum of money each month. Similarly, a client is regarded to have a high repayment ability if the

client repays the debts consistently, without (or seldom) making belated repayments. Additionally, the job

nature of a client is also taken into account whereby those with highly "mobile" occupations are assigned less

points; this practice is being attributed to fact that these clients would be less readily contacted or tracked

down! By examining these factors closely, it can be concluded that the 4C's criteria can be represented

qualitatively by the following 10 determinants: income, age, repayment pattern, employment record, debt

ratio, accommodation status, job nature, number of dependents, marital status, and bank relationships. These

10 factors are selected by virtue of their relatedness and relevance to the 4C's criteria and because such

information is readily accessible to the bank through records on pay-checks (pay-cheques), credit card and

income tax statements. The relatedness of these 10 determinants to the 4C's is given in Figure 3.1.

Figure 3.1: Relatedness of the 10 determinants to the 4C’s

14 | P a g e

3.3.1 The Credit Scoring System

The scoring system for our proposed system is based on the aforementioned 10 determinants. These

determinants will be assessed according to their scores. Higher points will be awarded to an application if

applicant is favorable to the bank. For example, from a scale of 0 to 10, a 0, 5 and 10 point score will be

assigned to determinants that corresponds to least favorable, average and most favorable, respectively.

These 10 determinants are described in detail as under:

1) Age: It is common practice in banking that a personal loan is granted only to those who fall within

the legal age group (which is 21 year old in most of the countries), and should be less than 60 years

old (it is the retirement age in most of the countries). Since the proposed system is a personal loan, it

is emphasized to those who are in the productive age - i.e. under employment. The most favorable

age range lies between the age of 41 - 55 years and thus 10 points is assigned to them.

Range (Age in years) Scores

< 21

21 – 30

31 – 40

41 – 55

56 – 60

> 60

Rejected

0

5

10

5

Rejected

2) Income: Income is also divided into several categories for assessment. Applicants who earn

more than $ 15,000 a month will be considered as experience labor and therefore belong to the high

earning power group; they are afforded the highest score. The total income specified here includes

the total value of the basic salary plus revenue derived from other sources, all of which would have to

be proven with tax statements or payroll slips.

Range (Income per month in $) Scores

< 3,000

3,000 – 4,000

4,001 – 6,500

6,501 – 10,000

10,000 – 15,000

> 15,000

Rejected

0

2

5

7

10

15 | P a g e

3) Job nature: The scoring system for job nature is divided into two groups. The first group

comprises those who hold a "favorable" job, i.e. whose working environment is in an office; 10 points

will be awarded to them. The second group would include those whose job mobility rate is high, such

as a salesman, who is not physically stationed in an office. For the latter group of people who earns

more than $ 15,000 per month, 5 points is awarded to them; otherwise 0 points is assigned.

4) Employment record: It is believed that an applicant with a good employment record (one who

stays with the same company for more than 10 years) would likely to be more mature and stable, and

unlikely not to repay the loan, thus highest points will be given to them.

Range Scores

With present employer < 1 year

< 1 year & > 2 years with formal job

2 – 5 years

6 – 10 years

> 10 years

0

2

6

8

10

5) Marital status: Pertaining to family status, married applicants are considered to be reliable, thus 10

points are assigned.

Range Scores

Single

Married

0

10

6) Number of dependents: For this category, the scores for income and number of

dependents should be taken into consideration. For instance, with four or more dependents, the

score for the income determinant should be reviewed such that a maximum of 8 points is to be

assigned.

Range (No. of dependents) Scores (Maximum income score)

>= 4

2 – 3

1

8

9

9.5

7) Relationship with bank: Customers presently doing business with the bank, such as holders

of a bank account and/or a payroll account, and other financial transactions, are preferable applicants

because it would reduce the risk of bad debts; thus higher scores are given.

16 | P a g e

Range Scores

No accounts

1 account (> $ 500)

Payroll account

0

5

10

8) Accommodation status: This evaluation is based on the type of housing and period of stay.

The most favorable applicants are those who are house owners as their repayment ability would be

higher. However, if it is rented accommodation, then points should be deducted from the previous

score because for the latter, the applicant could easily evade rental payments and thus their

propensity to avoid financial responsibilities is higher.

Range Scores

Rent

Company quarter

Government housing

Live with parents/relatives

Own with mortgage

Own without mortgage

0

3

3

3

6

10

Range (Duration of stay) Scores

> 1 year

< 1 year of present but > 3 years of previous

< 3 years of previous

-1

-1

-2

9) Debt ratio: The debt ratio is a measurement rate of the current earning power over regular

expenses such as mortgage loan for housing, overdraft account, or any liability loans. If the debt ratio

is computed to be less than 10%, then 10 points should be awarded.

Range (Debt ratio) Scores

> 30 %

20 – 30 %

10 – 19 %

< 10 %

Rejected

0

5

10

10) Repayment pattern: The record of the repayment pattern of an applicant reflects the client's

repayment ability. A person who manages his money poorly is typically slow in loan repayments or

has a tendency to frequent overdraft. Therefore, those who are clear of such bad debts should be

17 | P a g e

given the maximum score. Again, the point score system is used in conjunction with the existing

overdraft account and mortgage loan with the bank. The lower score of the two items will be assigned

as the final score.

Range (Mortgage/Personal loan) Scores

No experience

> 3 delays in 6 months

2 – 3 delays in 6 months

1 delay in 6 months

0 delay in 6 months

10

Rejected

5

6

9

Range (Overdraft in current account) Scores

0 count in 6 months

1 – 3 counts in 6 months

> 3 counts in 6 months

9

8

7

18 | P a g e

4 Project Design

4.1 System Design

The system design describes the structure of the system that needs to be constructed in terms of the

computational mechanisms, representational constructs, and software modules. A general structure of our

proposed system is shown in Figure 4.1.

Figure 4.1: General architecture of Expert System for Banking Credit Decisions

Models refer to a set of models/algorithms that allow the system to produce a specific set of desired solutions.

A mathematical model (i.e. the credit scoring system) has been integrated with the system to select the

appropriate decision rules during the run time according to each situation.

The knowledge base stores the facts of management problems. It also includes expert knowledge on how and

when to use these facts to generate management solutions. The knowledge base presentational paradigms

used in this system are decision rules - which are in the form of IF-THEN-ELSE format as described earlier.

An inference engine is a set of strategies which matches the data provided by the database with the

information contained in the knowledge base. We have used the forward-chaining strategy in which the

system works from an initial set of conditions and proceeds forward to the next rule if a specific condition is

met. The database contains the relevant facts or data describing a problem. The database will be used to

interact with models, rules and algorithms specified in the knowledge base.

End user (bank loan officer or credit manager) can ask “How” the system reaches the generated solution. The

system provides this type of explanation as tracing the line of reasoning. It will display the suitable reason that

links the findings with the conclusion. If more than one solution path is generated from the given finding the

system will display all those paths with their explanation text.

19 | P a g e

4.1.1 Rule-based System Architecture

From the credit scoring system discussion, we have demonstrated on how the 10 determinants could be used

to evaluate and transform the 4C's criteria into a quantitative means for evaluating a personal loan application.

Using our proposed solution, each of the quantifiable events can be rewritten as a set of decision rules. For

example, the decision rule for an employment status can be set up as:

Decision Rule for Employment Status:

IF the period of employment with current employer is longer than 10 years

THEN award 10 points to the applicant for employment status

ELSE IF the period of current employment is between 6 - 10 years


ELSE IF the period of current employment is between 2 - 5 years


ELSE IF 1) the period of current employment is less shall 1 year AND

2) the period of formal employment is > 2 years


ELSE award 0 point to the applicant for employment status

Likewise, the remaining 9 rules have been written in a similar format. When all these rules have been

developed, a final score can be computed by scanning through all the decision rules. Eventually, based on their

experience, a general practice for bankers would approve applications that score more than 68 points;

whereas applications with scores that is less than 54 points will be rejected. Applications with scores of

between 54 to 68 points would be considered as marginal cases and should be passed onto managers to

review for final decisions.

20 | P a g e

Again, this final decision can be based on a similar decision rule such as this:

Decision Rule for Application Status

IF 1) there is a "reject" by any condition OR

2) the total score is less than 54

THEN reject that application and print the reason why

ELSE IF the total score is between 54 and 68

THEN print the "reconsideration" message and its score

ELSE approve the application and outline its score point

An additional rule which would need to be imposed for the above decision rules is the maximum limit of credit

that is to be approved. This limit is monitored by two means:

Final Loan Amount Calculation

1) 60% of the earned income, and

2) 60% of the debt ratio

21 | P a g e

4.2 Use Case Diagrams

To represent the graphical overview of the functionality provided by the system in terms of actors, their goals

(represented as use cases), and any dependencies between those use cases, use case diagrams have been

implemented.

4.2.1 System Use Case

System use case describes the automation process of the system. We have described it at the system

functionality level. Figure 4.2 shows the use case diagram of system approach. First of all, credit loan officer

receives the loan application and complete initial pre-screening. If applicant’s details are invalid, then the

application will be cancelled. If loan application is complete and details are valid then applicant’s documents

verification and credit history report will be called. If status from either report is not clear then application will

be rejected. If status is clear then credit scoring will be evaluated. On the basis of scoring criteria defined

earlier, application will be approved, rejected or forwarded to credit manager for review.

22 | P a g e

Figure 4.2: System use case diagram

23 | P a g e

4.2.2 Activity Diagram

With the help of activity diagram graphical representations of workflows of stepwise activities and actions with

support for decisions, choices have been shown in Figure 4.3.

Figure 4.3: Activity Diagram

24 | P a g e

4.2.3 Entity Relationship Diagram

Figure 4.4: Entity Relationship Diagram

25 | P a g e

5. Project Implementation and Simulation Results

5.1 Simulation

In this section, simulation and simulation results are described. We have used Microsoft Visual C# for program

implementation and Microsoft Access for system database.

Performance measure of our system is dependent on credit rules as described earlier. If we enter the right

figures, result will be fine. We have tested our system with some application’s details, which has been taken

from NIB Bank Limited.

Some screen shots of the system are given below. Figure 5.1 is a snapshot of the system capturing date from

user (bank loan officer).

Figure 5.1: Loan application data entry

26 | P a g e

After capturing required data in system, result is generated on the basis of credit scores. Figure 5.2 describes

that entered application has got 82 scores. As it has got more than 68 points, so it is said to be approved.

Figure 5.2: Loan application status “Approved”

27 | P a g e

If any application gets less than 54 credit scores then it will be rejected without any review, and rejection

reasons will be stated, as shown in figure given below.

Figure 5.3: Loan application status “Rejection”

5.2 Process Model

Process models are processes of the same nature that are classified together into a model. More generally we

can say that process model is roughly an anticipation of what the process will look like in a system. Our

proposed system consists of process like loan application prescreening, data entry, credit scores calculation.

5.3 Intelligence

The intelligent aspect of our system is the functionality of taking correct decision on banking loan applications

based on the applicants’ data. This is an activity usually performed by a human expert. We have proposed the

solution for banking credit decisions as an Expert System. Expert systems which are also referred to as

knowledge-based decision support systems, is a domain of artificial intelligence. We have implemented

decision rules in our system in the form of decision trees on the basis of certain key factors (credit rules) to

intelligently select the appropriate rule for specific conditions.

28 | P a g e

6. Future Directions

Since this work is focuses on only one segment of banking credit lines, the study could be continued to extend

this system to evaluate and support banking credit decisions for commercial loans and mortgages. For this

purpose our proposed credit scoring system could be extended and implemented to include financial scoring,

management scoring and economic scoring. And credit criteria scoring should take into the consideration of

the stability, profitability, liquidation, and growth and activity factors.

A lot more factors need to be considering for processing commercial loans. Data that will be required from

company’s financial statement would be like:

Required Data from Company’s Financial Statements

Data Year 1 Year 2 Year 3

Earnings

Revenue

Gross-margin

Interest

Depreciation

Expenses

Cash

Accounts receivable

Inventory

Current assets

Fixed assets

Long-term liability

Current liability

Stockholder equity

Cost of goods sold

3152

151051

37282

3340

0

27551

2773

22936

44671

70552

5597

8902

45068

21601

113769

2740

121805

30039

1880

0

22544

1429

11956

29671

51545

4639

8508

28384

18583

91766

121

120038

27789

1635

0

26033

2776

18247

35366

57625

7245

8567

33684

21634

92249

29 | P a g e

7. Conclusions

In this project, we have presented an expert support system for a personal bank loan problem. It has been

shown that such a system can be developed by first transforming the four credits' criteria - capital, capacity,

character and conditions - into qualitative forms before converting them into a series of decision rules upon

which a credit scoring system is derived.

The system takes the decision on loan application and helps the human experts in explaining why a particular

decision is made. It evaluates the credit-worthiness of applicant by entering the relevant information or

parameters into system, a report will be generated to indicate if the application should be approved, rejected

or reconsidered. In the report, reasons for applications being rejected will be given. The system is easy to use

and does not require prior knowledge of evaluation schemes for personal bank loans.

30 | P a g e

8. References

1) Basel Committee on Banking Supervision, "Credit Ratings and Complementary Sources of Credit Quality Information." No 3, August, 2000.

2) Nikola A Tarashev, "An empirical evaluation of structural credit risk models" Bank for International

Settlements- Basel, Switzerland -July 2005. BIS Working Papers -No 179.

3) Oh, K. J., & Kim, T. Y., "Financial market monitoring by case-based reasoning". Expert Systems with Applications, 2007, 32(3), 789–800.

4) Kumra, R., Stein, R. M., & Assersohn, I., "Assessing knowledge based approach to commercial loan

underwriting". Expert Systems with Applications, 2006, 30(3), 507–518.

5) Linda A, Gayle D, Anthony S., "Issues in the credit risk modelling of retail markets" in Journal of Banking & Finance, Volume 28, Issue 4, April 2004, Pages 727-752.

6) Rada, R., "Expert systems and evolutionary computing for financial investing: A review”. Expert

Systems with Applications, 34 (2008).

7) Luke Hodgkin son, Ellen Walker, "An Expert System For Credit Evaluation And Explanation" Hiram College, POB 67, Hiram, OH 44 – 2003.

8) Wagner W.P. and Otto J., Chung Q.B., "Knowledge acquisition for expert systems in accounting and

financial problem domains" Department of Decision and Information Technologies, Villanova University, Villanova, PA 19085 USA February 2002.

9) Hashad, Nabil "Your Gide to Banking Credit Resk" BASEL part 2, 1997.

10) Chow, W.S. & Hawaleshka, O. "A novel machine grouping and knowledge-based approach for cellular

manufacturing system", European Journal of Operational Research. 69(3), (1993): 357-372.

11) Doukidis, G.I. & Paul, R.I. "A survey of the application of artificial intelligent techniques within OR society", Journal of Operational Research Society. 41(5), (1990): 363-375.

12) Duchessi, P. & Belardo S., "Lending analysis support system (LASS) – An application of a knowledge-

based system to support commercial loan analysis", IEEE Transactions on Systems. Man and Cybernetics. SMC-17(4), (July/August, 1987): 608-616.

13) Duchessi, P., Shawky, H. & Seagle, J.P. "A knowledge-engineered system for commercial loan

decision" Financial Management. (Autumn. 1988). 5. Giarratano, J. & Riley, G. Expert Systems: Principles and Programming. Boston: PWS-KentPublishing.1989.

14) Humpert, B. & Holley P. "Expert systems in finance planning", Expert systems. 5(2), (May, 1988).

15) Matz, L. "Automating loan operations procedures", Bank Administration. December, 1987.

16) Neale, I.M. "Modelling expertise for KBS development”, Journal of Operational Research Society.

31(5), (1990): 447- 458.

17) Krugman, P. (1990). What happened to Asian Banks: European Journal of Decision Support System Barketey CA, University of California. 3(2)20-53.

18) Lewis, E.M. (1992). An Introduction to Credit Scoring. Athena Press, San Rafael.

19) Mayo, K.S. (1999).Finance for the Poor: Micro Finance Development Strategy. IADB, Washington D.C.

31 | P a g e

20) McKenzie, D. (2002). Payment Systems and Infrastructure; Banks and Banking Reform: The World

Bank group, Washington D.C.

21) McCarthy, N. (1994). Credit Risk Measurement, New Approaches to value at Risk and Other Paradigms: New York: John Wiley and Sons, Inc.

22) Merton, R. (1974). On the Pricing of Corporate Debt: the Risk Structure of Interest Rates,” Journal of

Finance, 29:449-470.

23) Nakamura, A. (2003). A report on bad loans and entry in local credit market. In: Publication Distribution Bank of Canada. Accessed on 10 April 2005.

24) Srinivas, H. (1993). Forum on barriers facing banks in lending to many small and disadvantaged

business entrepreneurial institute, Productivity Press, Portland.

25) Sprague, P. J. and Carolson, N. (1982). ”Factor Models for Portfolio Credit Risk,” Working Paper, Department of Statistics, Bonn University.

26) Tjiptone, J. and Darmad, A. (2002). Lending still defensive in nature. Proceedings of 2002 summer

Management Conference, Lake Buena Vista, Florida, 57-78.

27) Efraim Turban, Jay E. Aronson and Ting Peng Liang, Decision Support Systems and Intelligence Systems, Seventh Edition, Prentice Hall, Upper Saddle River, NJ.

Expert System for Banking Credit Decisions

Documents

Transcript of Expert System for Banking Credit Decisions