ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
Vol.3 (12), DECEMBER (2013)
Online available at zenithresearch.org.in
1
DIGITAL NERVOUS SYSTEM-BASED CREDITWORTHINESS SYSTEM
FOR NIGERIAN BANKS
DR. AJAH IFEYINWA ANGELA
DEPARTMENT OF COMPUTER SCIENCE,
EBONYI STATE UNIVERSITY,ABAKALIKI.
NIGERIA
ABSTRACT A bank can lend successfully only when a borrower’s creditworthiness is accurately assessed. In
Nigeria, the challenge of lending in banking industry is that of “who to lend to”. This is as a
result of inability of banks to determine creditworthiness of a borrower. The challenge is due to
lack of comprehensive Information Technology (IT) based system with suitable technology that
will capture all key customer personal and loan data, poor system of identification, absence of
standard Credit Bureau for credit information sharing and obtaining credit history. The paper
therefore sets out to study the creditworthiness system used in Nigerian banks, its shortcomings
in determining the creditworthiness of an obligor and finally proposed a Digital Nervous System
(DNS) based Creditworthiness System that will help a great deal in mitigating the problem of
granting loan to fraudulent borrowers as well as those that lacks capacity to pay. DNS supports
business process by providing the infrastructure needed to consume data and information, filter,
sort and analyze data, and extract meaningful information which is delivered to authorized users
who needs it, at the right time, and in the right place. Object oriented approach of system
analysis and design is adopted in this work.
KEYWORDS: Biometrics, Credit history, Creditworthiness, Digital Nervous System, Global
Positioning System, Object oriented;
______________________________________________________________________________
1. INTRODUCTION
This paper is multi-disciplinary in nature being the novel application of Information Technology
(IT) in the field of banking. Therefore it is pertinent to introduce the issues in the problem
domain namely, banking before presenting the solution package proffered by IT.
1.1. Background of the Study
Creditworthiness can be defined as a presumed ability to meet agreed deadlines related to
repaying the credit and the interest accrued without affecting the vitality of the borrower, i.e. the
repayment process should be based on the income received in the process of the borrower's usual
activity, without affecting adversely his financial situation, his financial results as well as other
business entities. Establishing sound, well-defined credit-granting criteria is essential to
approving credit in a safe and sound manner. The criteria should set out who is eligible for credit
and for how much, what types of credit are available, and under what terms and conditions the
credits should be granted. Credit analysis is used in determining the current creditworthiness of
the loan applicant and forecasting the tendencies in its future development. This process is
ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
Vol.3 (12), DECEMBER (2013)
Online available at zenithresearch.org.in
2
connected with the financial and accounting analysis of the current and future activity and the
financial situation of the loan applicant in the specific economic environment and the expected
changes in the forthcoming periods. The information gathered during the credit analysis is of
great significance to the accurate structuring of the credit, which would contribute to lowering
the credit risk. This information can also be used if the need arises for restructuring the extended
credit in such a way that it brings higher profits to the borrower from utilizing the resources and
respectively the profitability of the bank-creditor.
The major risk that a bank faces is the probability of a customer’s default. This means
that looking at the overall financial status of the applicant is important. Assets such as property,
savings and stock accounts, current indebtedness, employment status and annual net salary or
wages, and overall credit rating are all components that factor into determining the bank credit of
the applicant. The values if recorded could serve as a point upon which the customer should be
judged. Today’s economic growth poses a big challenge to lenders to predict borrowers’
performance in recessionary conditions. Credit assessment techniques such as credit scoring
which is used to evaluate whether customers should or should not be granted credit, loan
screening aids such as advances in data technology, changes in regulatory environment, the
firm’s future profitability, the amount of the owners equity in the business to mention but a few
have often not been fully revealing and are imperfectly correlated across banks (Lewis, 1992).
Banks and micro finance institutions often rely on information to screen loan applicants and for
monitoring borrowers through repeated interaction with their customers. This normally applies to
the subsequent borrowers than the new entrants since it requires ample time to determine the true
creditworthiness of individual borrowers. McKenzie, (2002) states that when assessing
creditworthiness of loan applicants, banks usually refer to their past experience with similar
borrowers in similar markets. This may imply that, when a bank expands into a new market, the
negative effects of lack of expertise may overcome the benefits from risk diversification. He
further noted that, with statistical models, which go with credit cards, auto home mortgage, home
equity and small businesses, loan lenders simply plug the variables and later the computer does
the work. This helps the lenders make decisions more quickly and cheaply compared with old
style judgmental underwriting, and also more accurately and consistently. As a rule, a lender
must decide whether to grant credit to a new applicant. The methodology and techniques that
provide the answer to this question is called credit scoring. Credit scores assess the likelihood
that a borrower will repay a loan or other credit obligation. Credit scores indicate the risk of
customers based on their behaviour. They Provide Powerful credit intelligence as they represent
customer credit risk profile and can be used in a variety of automated applications. Bureaus
typically have a large amount of time series information which is useful to develop bureau
scores. E.g., US Fair Isaacs Corporation (FICO) scores.
1.2 Problem Statement
In Nigeria, the challenge of lending in banking industry is that of “who to lend to”. This is as a
result of inability of banks to determine creditworthiness/integrity of a borrower, multiple
lending to the same individual with the same collateral, use of fake collateral and use of same
landed/housing collateral for different loan facility, and use of fake identity in obtaining loan.
These problems are powered by lack of comprehensive IT system with suitable technology that
will capture all key customer personal and loan data, poor system of identification, absence of
standard Credit Bureau for credit information sharing and obtaining credit history. Moreover, the
ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
Vol.3 (12), DECEMBER (2013)
Online available at zenithresearch.org.in
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weight of analyzing the information for loan processing is on account officer. Thus, the
credibility of a customer is based on the evaluation made by the account officer of the customer
who goes out to the field to evaluate the collateral and signs a document to confirm that the
security provided by the customer can carry the weight of the loan. The evaluation made by this
account officer in most cases is not recorded against the customers. Most times, loan officer
adopt some “sharp” practices in analyzing loan in a bid to meet his risk asset target giving to him
by his employer; they connive with a customer with good credit rating to secure loan from where
they can now extend part of loan to other customers that do not meet requirements for loan but
have great need for it to boost their business. This implies that loan officers extend loan to
customers whom they know from day one lack capacity to pay for the sake of meeting his target
and safeguarding his job. The practice is possible because the management of banks permit
approval of some kinds of loan at the branch level. Sometimes a branch may be stopped from
booking loan as result of poor performance. The manager of such branch can decide to book loan
through another branch. The manager of the booking branch due to his personal relationship with
the requesting manager books the loan based on the assessment done by the requesting manager.
The big questions here are, whether the said target can be met when these loans go bad? Who
will be held responsible, the defaulters, the loan officer, the man with good credit rating, or the
manager from another branch who based on the information provided by another manager
booked the loan?
These aforementioned problems formed the following research questions that guided this study;
How can we determine the creditworthiness of an obligor prior to loan processing? How can we
stop identity fraud in loan processing? How can we stop multiple lending to the same individual
with the same collateral? How can we stop usage of same landed/housing collateral for different
loan facility? and How can we handle moral hazard exhibited by loan officers. Providing
answers to these questions led to modeling a viable Creditworthiness System that will help banks
combat identity fraud as well as track effectively the multiple loan of one individual/corporate
business by means of Biometric-based (fingerprint) information. It will uniquely identify
collateral in the form of a landed/housing property using geographical positioning system
technology and track effectively where the collateral is currently used to secure credit. It will
also verify business registration details of the borrower across corporate affairs commission and
obtain his credit rating via credit bureau. It deals with the problem of moral hazards by granting
only head of credit of various banks access to approve loan while loan officers activity is limited
customers verification and keying in of values of required credit measurement parameters while
the system computes the score. This is achieved through effective integration of features that will
permit personal and credit information sourcing from relevant external agencies that hold vital
information needed for the aforementioned verification. The system will achieve this by
implementing DNS approach. DNS is not a program nor a hardware product, but a combination
of IT infrastructures, different software applications, Internet technology and the web concept,
which enables the efficient exchange of information on an organizational network (Shelkh et al,
2005). DNS will capture credit risk information and provides it where it is needed for decision
making and when it is needed. This will enhance the efficiency and effectiveness of lending
business processes. The interesting strength of the proposed system is its capability of
interconnecting loan processing of all banks, credit agencies including Central Bank of Nigeria
(CBN) credit bureau for effective information sharing. This promotes high level of transparency
in loan assessment and thus there is no “hiding place” for fraudulent borrowers. Major security
ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
Vol.3 (12), DECEMBER (2013)
Online available at zenithresearch.org.in
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considerations taken include ensuring Data Privacy and Security to encourage bank use the
system. This is achieved by designing the system to use the login details of the user (Loan
officer) to retrieve the bank of the user and then enforces him to do business concerning his bank
alone.
A server scripting language is totally implemented to protect the system from hackers. The main
contributions of this work are;
_To the best of our knowledge the DNS based credit worthiness system (CWS) is the first in
Nigeria that combines biometric and GPS technology to assist banks do instant creditworthiness
as well as deal effectively with the problem of identity fraud, use of fake collateral, and reduce
moral hazard associated with loan. It tracks and relate to loan officer if the borrower has multiple
loan with other banks and this information is very vital in taking loan decision.
_DNS approach promotes high level of transparency in loan assessment and enables credit
information sharing among banks and credit agencies. It enables banks to easily and swiftly
obtain credit information and provide it where it is needed and when it is needed.
_Biometrics fingerprint and GPS technology help in detecting identity fraud and use of fake
collateral respectively prior to loan processing.
We strongly believe that the system has mechanism that heightens borrowers’ incentive to repay,
every borrower knows that if he defaults his reputation with all other potential lenders is ruined,
cutting him off from credit or making it more expensive.
1.3 Methodology
Questionnaires and interviews are used to elicit information from credit officers and stakeholders
on how customer’s creditworthiness is determined. Articles from the national daily, internet, and
journals were accessed and this helped in understanding the concept of creditworthiness in bank
loan processing. Object Oriented Methodology (OOM) is used for modeling the proposed
system. Object-oriented analysis (OOA) applies object-modeling techniques to analyze the
functional requirements for a system. OOA focuses on what the system does and looks at the
problem domain, with the aim of producing a conceptual model of the information that exists in
the area being analyzed. This is typically presented as a set of use cases, activity diagram,
sequence diagram, and class diagram.
2. OVERVIEW OF CREDITWORTHINESS ASSESSMENT IN NIGERIAN BANK The approach used for determining the creditworthiness of a borrower in Nigeria’s financial
institutions, has not proved efficient in determining the borrower’s creditworthiness. This has
therefore resulted in many poor decisions in credit offers to both creditworthy and credit risky
borrowers, hence a need to design a mechanism to address the situation.
2.1 How the System Works
Description on how the existing system works is based on information gathered from credit
officers and stakeholders of some commercial banks. Figure 1 is the flow diagram of the existing
system. Creditworthiness assessment begins after a bank customer (borrower) fills and submit a
loan application form which include information such as personal and credit history, purpose for
the loan, amount of loan requested and collateral details.
ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
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Fig. 1. Loan Processing Flow Diagram of the Existing System
When the loan request is received by the account officer, the personal information
supplied by the user is manually analyzed based on the bank Risk Asset Acceptance
Criteria (RAAC). RAAC uses PARTS (Purpose, Amount, Repayment source, Tenure and
Security) as a metrics; The purpose for the loan must be clear, amount to be granted is
determined by the customers account turnover. The borrower’s repayment source is the
major determinant of lending. The tenure must fall within the banks acceptance tenure.
The customer’s detail is sent to the CBN Credit Risk Management System (CRMS) to
verify across an existing database if the applicant is indebted to any bank. Approval is
granted or rejected based on these information. on a company upon which a bank decide
if it can carry on with the loan processing.
After certain formalities are fulfilled, a report, in the form of an offer letter, is generated
and sent to the user for signing, confirming the approval and acceptance of the loan
respectively. This report is usually not tabulated and calculated in a manner a borrower
will understand at a glance the total amount including interest and other charges he is
expected to repay. Rather, it is defined in percentage which most customers are unable to
calculate and as a result they not clearly aware at hand his total indebtedness to the bank
If the loan is rejected for some reason, then a report showing the reason for the rejection
is generated and a hardcopy is given to the customer.
A sample of the CBN CRMS report shown in Figure 2 below is a credit history of a company
requesting for loan.
Fig. 2. A sample of CBN CRMS / Credit Bureau report on corporate borrower
ZENITH International Journal of Multidisciplinary Research _______________ISSN 2231-5780
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Banks takes loan decision based on this report. It refers to credit information on a corporate loan
that went bad. It shows the borrowers code, the type of credit, number of banks indebted to,
credit limit, outstanding amount and the performance status. Here, only indebtedness to banks is
being reported. The customers credit relationship with other public sectors like Tax office, Power
Holdings Company of Nigeria Plc (PHCN), Water corporation, Phone company, Business
company, and so on are not recorded.
2.2 Limitations of the Existing System
The Limitations of the existing system are summarized below;
1. The information shown on CBN CRMS report is not sufficient to access and determine the
repayment ability of a borrower. The system shares customer past indebtedness with other
banks and has no other credit information from other financial institution such as PHCN, Tax
Office, Water corporation, phone companies and so on that a consumer has had a financial
relationship with. Banks sometime do not report bad credit to CBN and this encourages
fraudulent customers to obtain multiple loans from various banks.
2. Apart from CBN CRMS there is no other credit bureau that provides banks with borrower’s
credit history. Though there are few private credit bureaus in operation in the country, banks
do not use them. From information we gathered, these private bureaus have a great challenge
of getting true personal and credit history of individual as a result of lack of standard system
of identification in the country.
3. Non availability of Credit registers encourages fraudulent customers in using fake collaterals
to obtain loan or same collaterals to obtain multiple loans from various banks.
4. The system has not got customer risk scoring feature. The criteria for creditworthiness
judgment is based on PART as earlier described.
5. Lack of a standardized national system of identification; driving license or national identity
number, is a weak tool for identifying a customer. This is because there is great laxity in a
way these numbers are obtained in the country. The institution issuing the identity has no
robust database to enable them check for duplication. Moreover, Know Your Customer
(KYC) and Know Your Customer Business (KYCB) adopted by banks today do not
completely guaranty customers identity.
6. There is no IT- based creditworthiness system present in banks that can check on- the- spot
customers’ creditworthiness. Banks depend mainly on the information provided by the
customer in the application form and have no way of checking for the genuineness of data
filled in the form. The current system introduces much delay in loan processing. As a result
successful loan applicant gets the loan when the need for it must have past or irrelevant for
the purpose for which it is done.
3. REQUIREMENT DEFINITION
The study conducted was on the existing creditworthiness system in Nigerian commercial bank.
Analysis was made out of the prevailing information structure and the critical success factors of
the organization were vital in finding out the information requirements for the system. In
determining requirements for the new system, Business Process Reengineering (BPR)
techniques that involves a substantial amount of change was used BPR was preferred because it
seek to radically improve the nature of the business and also a high-level of business
requirements is needed for developing high quality system that satisfy users need. The BPR
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activity that was adopted is technology analysis. Lists of important and interesting technologies
were developed and how each of these technologies could be applied to the business process and
how the business will benefit is discussed below. The functionalities that will be provided by the
new system will apply the combination of the following technologies are discussed below;
1. Biometrics based Personal Identification Number called National Reference Number
(NRN) is introduced to uniquely identify each customer. The NRN will be assigned by
Federal Inland Revenue for every permanent worker and directors of corporate business
as a proof of identity. This idea must be backed with strong policies that will (i) stop
registration of corporate business whose directors have not got NRN. ( ii) mandate
directors of already registered business to obtain NRN and update their record with
Corporate Affairs commission. ( iii) stipulate one NRN per person no matter the number
of company he /she has got. This will check or stop using fake collaterals to obtain loan
or same collaterals to obtain multiple loans from various banks. (iv) Deny access to loan
to individual/directors with no NRN. Biometrics fingerprint identification system
integrated in the creditworthiness will spot out fake NRN.
2. Global Positioning System (GPS) will be used to read and record the coordinates of
landed/ housing collateral obtained by a loan officer or bank approved property valuer.
This will also guard against use of fake collateral in obtaining loan. The values read in
this exercise will be keyed in the creditworthiness system (CWS) and the values will be
matched with the existing values in the land Registration database.
A DNS approach leverages the power of the Internet to interconnect various credit agencies,
CBN CRMS, and banks to the CWS. The unified connection will enable the system captures
credit information and provides it where it is needed for decision making and when it is needed.
4. REQUIREMENTS ANALYSIS
Out of the analysis carried out, the following were identified as the user requirements for the
system.
4.1 Functional Requirements
This relates directly to the process the system must perform or information it needs to contain.
Process- oriented
1. The system must validate bank employee access; only loan officer and loan committee
member is allowed access to the system.
2. The system must verify applicant’s identity with the National Identity register.
3. The new system should be able to verify information provided by the customer in the
loan application and stop further analysis on any that is found incomplete or fraudulent.
This include identity, collateral and business registration verification
4. The system must determine applicant’s creditworthiness as this will give the bank pre
information on the probability of the customer defaulting.
5. The new system should be able to limit errors during data entry by users.
6. The system should provide a high level security for networked transactions.
Information oriented
1. The system must retain information about good and fraudulent customers.
2. The system should include information on currently running loan.
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3. The system must include credit information that is updated at least weekly.
4.2 Non functional requirements
This refers to the behavioral properties that the system must have, such as performance and
usability. They include;
Navigability Requirements
1. The system should allow easy record entry and deletion of records should only be done by
authorized personnel.
2. The system should be able to save and retrieve and print information
Operational: this describes the physical and technical environment in which the system will
operate.
1. The system should be able to integrate with the existing network of various bank and
relevant database like National Identity Register, Credit Bureau database, and Corporate
Affairs Commission database.
2. It should be able to work on any web browser.
Reliability
The system has no availability requirements. The system is to be used during standard working
hours (8:30am to 7:00pm)
Performance: This defines the speed, capacity, and reliability of the system.
1. The system should support 100 simultaneous users at all time
2. The system should be efficient, reliable, and should allow timely acquisition of
information whenever needed.
Security: This addresses who has authorized access to the system under what circumstances.
1. Only authorized trained bank staff usually the credit officers, has authority to operate the
system.
2. The system includes all available safeguard from viruses, worm, Trojan Horses and
hackers.
3. Any user with insufficient fund is automatically disabled from accessing the system.
Cultural and Political: This describes cultural, political factors and legal requirement that affects
the system.
1. Customers personal and credit information is protected in compliance with the Data
Protect Act.
2. Data privacy and security of banks must be enforced by the system to encourage banks
use the system.
3. The system should be secured from hackers.
The three security requirements will be achieved by ensuring that
1. The system uses the login details of the user(Loan officer) to retrieve the bank of the user
and then enforces him to do business concerning his bank alone.
2. Server scripting language such as PHP, is totally implemented
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5. DOMAIN ANALYSIS
Behavior and structure diagrams are used in doing domain analysis. Behavior diagrams used are
Use Case, Activity, Sequence and Behavioral State diagram. They are used to depict the dynamic
relationships among the instances or objects that represent the business information system.
These diagrams provide support in modeling the functional requirements of the system. Class
diagram is the Structure diagrams used to represent data and static relationship that are in the
system.
5.1 Main Use Case for the CRMS Use cases that capture business requirements for the system and illustrate the interaction
between the system and its environment.
5.1.1 Identifying Actors in the System
The following actors are identified. Actors provide services to the system under discussion.
Loan Officer – Bank employee who analyze loan applications. He can also view usage
information to know if he has login available and change password his password.
Admin – A CRMS staff that manages users access to the system, change password his password
create and updates users account.
Admin
Change Password
Users
Usage Information
Loan officer
Assess Loan
View usage information
Change Password
CRMS
Fig. 3. Main Use Case Diagram for CRMS
5.1.2 Assess Loan Use Case
Primary Actor: Loan officer
Supporting Actor: Billing system.
Brief Description: This use case begins when the loan officer logs in to the CRMS and supply
his credentials (username, and password) for proper logging into the system. The system verifies
the credentials are valid (E-1). The system then loads the main menu for the loan officer to
assess loan.
Pre-conditions: The user must have registered and made sufficient payment to use the system.
Post-conditions: The loan officer is successfully logged in to use the system
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Main flow of events:
Loan officer logs into the CWS and enters his username and password. The system verifies that
the login details are valid (E-1), and that the loan officer has sufficient fund to assess loan (E-2).
If the loan officer is logging in for the first time, the system prompts him to change his password
(E-3). After password has been changed, the system login window displays again for the loan
officer to re- login in with his user-defined password. A web page that has links for performing
loan assessment operations is displayed.
Alternative Flows and Exceptions:
E-1: An invalid login details is entered. The system prompts the customer to enter a valid login
details. The user can re-enter the login details or terminate the use case.
E-2 : Payment made has been used up or no payment made. The user is disabled from using the
system and informed about insufficient fund. The use case terminates.
E-3: An invalid password is entered or the new password and confirm password does not match.
The user can try again .
5.1.3. Verify Customer Use Case
Primary Actor: Loan officer
Brief Description: This use case begins when the loan officer has successfully logged in. It
provides interfaces for the verification of customer information provided in the application form.
The system validates information on the loan application.
Pre-conditions: The customer must have valid NRN.
Post-conditions: Customer identity, Collateral details, Business incorporation details is
successfully verified, credit history is obtained from credit bureau.
Loan officer
Verify Customer Details
Calculate Creditworthiness
Financial Risk
Management Risk
Business Risk
Relationship RiskExt. Bureau Rating
Security Risk
Identity
Collateral
Business Registration
Determine Customer's Creditworthiness
Ext. Bureau Report
<<ext
end>>
<<extend>>
<<extend>>
<<extend>>
<<extend>>
<<extend>>
<<ex
tend
>>
<<extend>>
<<extend>>
<<
exte
nd
>>
<<extend>>
<<extend>>
Customer's fingerprint
capturing
<< includes >>
Fig. 4. Verify Customer and Compute Creditworthiness Use Case
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Main flow of events
The loan officer clicks on verify customer and the system displays various activity that can be
done (IDENTITY, COLLATERAL,BUSINESS INCORPORATION AND EXT BUREAU
REPORT).
1.If the activity selected is IDENTITY, the S-1: Identity verification subflow is performed.
2.If the activity selected is COLLATERAL, the S-2: Collateral verification subflow is
performed.
3.If the activity selected is BUSINESS INCORPORATION, the S-3: Business incorporation
verification subflow is performed.
4.If the activity selected is EXT. BUREAU REPORT, the S-4: Bureau report subflow is
performed.
Sub-flows
S-1: Identity:
The system displays a biometric application. With the fingerprint machine attached to the
computer. The customer swipe the exact finger he/she used to register with the National Identity
Commission (E-11). The loan officer compares displayed information with that on the
application form (E-5).
Alternative Flows and Exceptions:
E-4: An invalid NRN is entered. The system prompts the user to enter a valid NRN. If NRN does
not exist, the customer is marked fraudulent and customer’s record is updated. The use case is
terminated.
E-5 : Information mismatch. Customer record is updated .The use case is terminated.
E-11: An invalid finger is swiped or the customer is not registered. The customer swipes the
correct finger. But if the customer is not registered the use case is terminated.
S-2: Collateral:
The system displays a search window containing a field for Collateral Number. The loan officer
enters valid Collateral Number (E-6). The system displays details for the entered Collateral
Number. The loan officer compares coordinates information with that on the application form
(E-7).
Alternative Flows and Exceptions
E-6: An invalid Collateral Number is entered. The system prompts the user to enter a valid
Collateral Number. If Collateral Number does not exist , the customer is marked fraudulent and
customers record is updated . The use case is terminated.
E-7: Information mismatch. Customer record is updated .The use case is terminated
S-3: Business Incorporation
The system displays a search window containing a field for Incorporation Number. The loan
officer enters valid Incorporation Number (E-8). The system displays details for the entered
Incorporation Number. The loan officer compares Incorporation information with that on the
application for (E-9).
Alternative Flows and Exceptions
E-8: An invalid Incorporation Number is entered. The system prompts the user to enter a valid
Incorporation Number. The user can re-enter the Incorporation Number. If Incorporation
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Number does not exist , the customer is marked fraudulent and customers record is updated . The
use case is terminated.
E-9: Information mismatch. Customer record is updated .The use case is terminated
S-3: Ext. Bureau Report Use case
The system displays a search window containing a field for Bureau Ref. Number. The loan
officer enters valid Bureau Ref. Number (E-10). The system displays details for the entered
Bureau Ref. Number. The loan officer records bureau report.
Alternative Flows and Exceptions:
E-10: An invalid Bureau Ref. Number is entered. The system prompts the user to enter a valid
Bureau Ref. Number. If Bureau Ref. Number does not exist , the customer is marked fraudulent
and customers record is updated . The use case is terminated.
5.1.4 Creditworthiness Use Case
Primary Actor: Loan officer
Brief Description: This use case begins after loan officer has completed verification of customer
information.. It provides the interface to input creditworthiness parameter for determining
creditworthiness of a borrower.
Pre-conditions: The Verify Customer Use Case must execute before this use case begins
Post-conditions: The result of the creditworthiness computation is stored in the database
Main flow of events:
Loan officer clicks the creditworthiness link and the system displays the creditworthiness form in
which the loan officer enters values for the specified parameter (E11). The Next and Back button
are used to navigate to and fro the form .When the form is submitted, the system uses the entered
values to calculate the creditworthiness. The result for all the parameters assessed is displayed
before the loan officer finally save the final result in the database.
Alternative Flows and Exceptions
E-11: No value is entered. The user is informed of the particular field that is required. The user
can enter value.
5.2 Activity Diagram for CWS
An activity diagram represents the dynamics of the system. They are flow charts that are used to
show the work flow of the system; that is, it shows the flow of control from activity to activity in
the system, what activities can be done in parallel, any alternate path through the flow, and what
are the various verifications that should be made. The activity diagram for calculating
creditworthiness is described in Figure 5 below.
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Verify customer
Display Creditworthiness
form
Reject Loan
Update customer Information
Notify Customer
Initiate Loan Analysis
[valid details]
produce report
[invalid details]
Input values for customer
Send result to database
Calculate Creditworthiness
Display result
[All values entered]
Yes
No
Fig. 5. Activity Diagram for Creditworthiness Assessment
1. A user initiates loan analysis activity.
2. The user verifies customer information by comparing retrieved customer record with the
one on the application form. A loan can be rejected if invalid record is discovered and the
customer will be notified and his record updated as well.
3. The system displays the creditworthiness form and the user inputs values for the specified
parameters and submits.
4. The system calculates the creditworthiness and displays result.
5. The user submits the result to the database.
5.3 Sequence Diagram for CWS
The sequence diagram is a dynamic model that supports a dynamic view of the evolving system.
It shows the explicit sequence of messages that are passed between objects in the defined
interaction. It emphasizes the time-based ordering of the activity that takes place among a set of
objects, they are helpful for understanding real-time specifications and complex use cases. The
sequence diagram helped the researcher to model the dynamic part of the system.
In figure 6, actor and objects that participate in the sequence are placed across the top of the
diagram using actor symbols from the use case diagram and rectangles. They participate in a
sequence by sending and/or receiving messages.
A line runs vertically below each actor and object to denote the life line of the actors/objects over
time. A thin rectangular box, called the execution occurrence, is overlaid onto the lifeline to
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show when the classes are sending and receiving messages. A message conveys information
from one object to another one. The UML diagrams (use case, activity, sequence and class
diagrams) have helped the researcher to get a great deal of information about the customer, loan
officer and the loan.
Loan Officer
Login Request
Fetch Customer History
Retrieve Customer History
Display Customer History
Fetch Collateral Registration Info.
Retrieve Collateral Details
Display Collateral Info.
Fetch Business Registeration Info
Retrieve Business Registration Details
Display Business Registration Info
Fetch Customer Ext. Bureau Rating
Retrieve Customer Credit Rating
Display Customer Credit Rating
Click Creditworthiness
Enter values,Compute, and Store Creditworthiness
Validate Login Details
Valid
Bank Network
Database
Fig. 6. Sequence Diagram for the Credit Risk Management System
5.4 Data Modeling
A data model presents the logical organization of data in the system without indicating how the
data are stored, created, or manipulated. This helps the researcher to focus on the business
without being distracted by technical details. Class diagram that shows the data components of a
business system is used to model the data in the new system and this is presented in Figure 7
below. The data to support the CWS can be organized into 16 main classes:- customers, banks,
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loan officers, loan, verification, identity, bureau, creditworthiness computation and so on.
Attributes having asterisks next to them is used to uniquely identify an entity. For example, the
customer id is used to identify a particular customer. Class diagram also communicates high-
level business rules. Business rules are constraints or guidelines that are followed during the
operation of the system. The new system should support the business rules described below and
it should ensure that users do not violate the rules when performing the processes in the system.
Creditworthiness Computation
Financialrisk Score
Businesssrisk Score
Managementrisk Score
Security Score
Relationshiprisk Score
Bureaurating Score
Bureau score
Compute()
Financial Risk
Leverage
Liquidity
Profitability
Coverage
Calculatefinscore()
Business Risk
Businesssize
Businessoutlook
Industrygrowth
Markecompetition
Entryexitbarrier
Caculatebusscore()
Management Risk
Experience
Succession
Teamwork
Caculatemanscore()
Relationship Risk
Accountconduct
Personaldeposit
Compliance
Limitutilization
Caculaterelscore()
Security
Secoverage
Location
Guarantee
Caculatesecscore()
Bureau Rating
Bureauratingcode
Bureauscore
Caculatebureauscore()
Customers
*customer id
*branch
name
address
industry
phone
emailGetcustomerinfo()
Banks
*branch
name
address
phone
Getbankinfo()
Loan Officer
*loanofficer id
*branch
name
Getloanofficerinfo()
Verification
type
Showresult()
Loan
*loan id
*customer id
loan type
loan amount
purpose
tenure
Getloaninfo()
Consumer Corporate/Commercial
Bureau
customer id
Incorporation
Incorporation num
Collateral
Collateral num
Identity
fingerprint
requests
be
long
s
wo
rks
performs
results in
lea
ds to
Bill Payment
*receipt number
amount
date
Showpayment()
Bill
*Bill number
date
num of loan accessed
unitcost
totalcost
Showpayment()
generates
covers
makes
Fig. 7. Class Diagram for Bank Creditworthiness System
Business Rules
1. A customer can belong to one or more bank (communicated by “crows’s foot” placed on
the line closest to Bank class).
2. There are several loan application in the system and each customer may request one to
many loan contracts. This is communicated by a line on the “crows’s foot” nearest the
Loan class . However, it should be noted here that the system does not support the use of
same collateral for multiple loan; This is checked by the Verification class; During this
stage the system uses the customer’s biometric id to check across the database if the
collateral is encumbered or free.
3. Consumer and Corporate/Commercial are kinds of loan and therefore inherits the same
properties and operation of Loan class (communicated by a solid line from the subclass
(Consumer, Corporate/Commercial) to the superclass (Loan) and a hollow arrow pointing
to the Loan.
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4. A loan officer can only determine creditworthiness for one bank (communicated by a line
close to the Bank class but can verify one to many loan application (communicated by a
line on the “crows’s foot” nearest the Verification class).
5. Since there are several Banks using the CWS, there are likely to be several loan contracts
requested by different customers and processed by different loan officer concurrently . To
uniquely identify an individual loan contract,
I. The Loan class has added the customer id as an additional identifier attribute.
II. The Customer class has added the branch as an additional identifier attribute to
know the branch and bank of the customer.
III. The class Loan officer has added the branch as an additional identifier attribute to
track the branch office of the bank of the loan officer in charge.
6. Application verification is made up of Identity, Collateral, Incorporation and Bureau
(communicated by a diamond placed nearest Application verification class).
7. Each verified application leads to zero to one creditworthiness computation
(communicated by a zero on a line nearest Creditworthiness Computation class). This
means that there exist conditions under which the loan officer cannot continue with
creditworthiness computation. Such conditions include situation where fake identity or
fake collateral, unregistered business was detected.
8. Credit computation consist of Financialrisk, Businessrisk, relationshiprisk, Bureau
Rating, Managementrisk, and Security (communicated by a diamond placed nearest
Creditworthiness Computation class).
9. One or more loan request generates one or more bill (communicated by the “crows’s
foot” nearest the Loan and Bill class ) .
10. Each bank makes one to several payments depending on the number of loan processed
(communicated by the two bars on the line closest to Banks and the “crows’s foot”
nearest the Bill Payment).
5.5 Behavioral State Machine Diagram for a Loan Officer
Behavioral State Machine Diagram is a dynamic model that shows the different states that a
single class passes through during its life in response to events, along with its responses and
actions. Figure 8 presents a behavioral state machine diagram representing the loan officer class
in the context of a customer’s creditworthiness determination.
Biometrics Identity
Verification
Credit History Check
Collateral Verification
Creditworthiness Process
Terminated
Entering Creditworthiness
Parameters
Computed
Business Reg. Check
[ Identity = Invalid ]
[ Collateral = False ]
[ Bussi. Reg = Incorrect ]
[ Credit History = Unavaliable ]
Creditworthiness Computation Completed
Customer Fingerprints
Read
[ Identity = Valid ]
[ Credit History = Avaliable ]
[ Collateral = True ]
[ Bussi. Reg = Correct ]
Creditworthiness is computed
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Fig. 8. Behavioral State Machine Diagram for a Loan Officer
The diagram tells that a customer’s fingerprints are read to verify his/her identity and if it is valid
his credit history is checked. If the customer has credit history, the business registration
verification is done. If the business registration information is correct, the loan officer then enters
creditworthiness parameters that are used to calculate the creditworthiness. The creditworthiness
computation is terminated if at any point during verification the Identity, credit history, collateral
and business registration records is found to be invalid.
6. SYSTEM DESIGN AND IMPLEMENTATION
6.1 Interface Design Prototype
Interface design prototype was done using Storyboard approach. The storyboard shows hand-
drawn pictures of what the screens will look like and how they flow from one screen to another.
Verify Customer Menu
Identity
Collateral
Business Incorporation
Ext Bureau Report
Collateral Information
Id
Type
Location
Value
coordinatesBusiness Incorporation Information
Name
Date of Incorporation
Reg. Number
State Registered
Shares Issue
Paid Capital
Directors
Major Shareholders
Secretary
lear
Retrieve Identity Information
Customer NRN:
SearchClear
Clear
Retrieve Collateral Information
Collateral Number:
SearchClear
Clear
Retrieve Business Incorporation Information
Incorporation Number:
SearchClear
Identity Information
NRN
Firstname
Lastname
Date of Birth
Term Address
City
State
Hometown
State of origin
Marital Status
Occupation
Work Address
Phone Number
Term Address Duration
LGA
Bureau Rating Code
PHCN payment
Water Rate payment
Phone Bill Payment
Score
Clear
Retrieve Bureau Rating
Bureau Ref. Number:
SearchClear
Fig. 9. Storyboard for Verify Customer
6.2 Interface Template Design
For the interface template, the researcher uses two different templates, one for the verification
process and another for the rest of other interfaces for the system.
For the verification page, the use of frameset was adopted to divide a browser window into
sections called frames. Each frame can display a separate web page. A Fixed left frameset was
used. The left frame contains navigational elements, and the main frame that displays the
verification site content. This is shown in figure 10.
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Fig. 10. Customers Verification Interface Template
The researcher’s choice of using frameset is because of the numerous advantages it offers and
because verification process has numerous activities which include, collateral and business
registration verification as well as credit bureau record check. All these processes are achieved in
one page. This helps in improving site performance, providing separate scrollbars for each frame,
and simplifies site performance.
For other interfaces, the researcher decided on a simple, clear design that had a modern
background pattern, with the CWS banner on the top, the copyright statement on the bottom
page, and the left edge for the CRMS animation. Main menu follows immediately after banner
for navigation within the CRMS. The menu contained the links to the four top-level screens
(About us, Contact us Register and Help). The center area of the screen is used to present the
main page (Home page) for a particular level of user. This page contains links (navigational
system) to all activities for the user. It is also used for displaying forms and reports when the
appropriate link is clicked.
6.3 User Interface Forms
The user interface forms were designed using Hypertext Preprocessor (PHP). Different screens
were developed.
6.3.1 Creditworthiness Input Forms.
Figure 11 and 12 show parts of the form in which the user enters customer’s value for the
creditworthiness computation. The user (loan officer) first enters the customer’s details and click
on Next button. This takes him to the next page where he enters the financial risk details. Then
followed by business risk, management risk, security risk, relationship risk and finally clicks on
Left frame Main Frame
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finish button to send the result of the computation to the database. Figure 12 shows an input form
and the corresponding input data for business risk analysis.
Fig. 11. A Fragment of Creditworthiness Input Form
Fig. 12. Another Fragment of Creditworthiness Form
6.4 Input Validation
This was achieved using Javascript & PhP script. All data entered into the system were validated
to ensure accuracy. The system does not accept data that fail any important validation check to
prevent invalid information form entering the system. The system identifies invalid data and
notify the user to resolve the information problem.
`6.5 Creditworthiness Computation Design
The visual representations of conceptual classes or real situation objects in this domain are
figured out in Figure 13. The class diagram shows the implementation-specification artifacts, like
windows, forms, and other objects used to builds the creditworthiness subsystem. Each class
shows the class’s name at the top, attributed in the middle, and methods (operations) at the
bottom. Customers, Banks, Financialrisk, Businessrisk, relationshiprisk, Bureau Rating,
Managementrisk, Security and Creditworthiness Computation are classes. The attributes are
properties of the class about which we want to capture information. For example the Security
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class above contains the attributes Secoverage, Location, and Guarantee. The Financialrisk class
attributes contains a derived attributes indicated by placing a slash (/) in front of the attributes’s
name example “/Leverage”. Derived attributes are calculated from other attributes. For example
Leverage is calculated by dividing total current assests by total current liabilities.
Creditworthiness Computation
Financialrisk Score: Num(2)
Businesssrisk Score:Num(2)
Managementrisk Score: Num(2)
Security Score: Num(2)
Relationshiprisk Score: Num(2)
Bureaurating Score: Num(2)
Bureau score: Num (2)
Compute()
Financialrisk
/Leverage: Num(1)
/Liquidity: Num(1)
/Profitability: Num(1)
/Coverage: Num(1)
Calculatefinscore()
Business risk
Businesssize: Num(1)
Businessoutlook: Num(1)
Industrygrowth: Num(1)
Markecompetition: Num(1)
Entryexitbarrier: Num(1)
Caculatebusscore()
Managementrisk
Experience: Num(1)
Succession: Num(1)
Teamwork: Num(1)
Caculatemanscore()
Relationshiprisk
Accountconduct: Num(1)
Personaldeposit: Num(1)
Compliance: Num(1)
Limitutilization: Num(1)
Caculaterelscore()
Security
Secoverage: Num(1)
Location: Num(1)
Guarantee: Num(1)
Caculatesecscore()
Bureau Rating
Bureauratingcode:Varchar(9)
Bureauscore: Num(1)
Caculatebureauscore()
Creditworthiness
Customeratingcode:Vchar (9)
AggregateScore: Num(3)
RiskGrading: Char(20)
CustomerRatingDescription:Blob
Storeresult()
Customers
CUS_name: Varchar(20)
CUS_groupname: Varchar(25)
CUS_industry: Varchar(50)
Getcustomerinfo()
Banks
BAN_branch:Num(9)
BAN_bankname: Vchar(25)
Getcusbankinfo()
Fig. 13. Class Diagram for Creditworthiness Subsystem
Method/operation that is unique to each class is shown with parenthesis. For example
Caculatesecscore() and the parenthesis represents a parameter that the operation needs for it to
act. Figure 13 also communicates that Creditworthiness Computation represent aggregation.
This shows that it comprises of other classes. This relationship is denoted by a diamond placed
nearest the class representing the aggregation(Creditworthiness Computaion),and lines are drawn
from the arrow to connect that classes that serves as it parts (Customers, Banks, Financialrisk,
Businessrisk, relationshiprisk, Bureau Rating, Managementrisk, Security). The creditworthiness
class represents a class that stores the result of the creditworthiness computation.
The creditworthiness for all the advances will be calculated by this class. The computation is
done by the business logic (handled by the PHP code) and the result of the computation is stored
in the database.
6.6 Modules of the Creditworthiness System
1 Verify Customer Module: The user (loan officer) after successful log in clicks Analyze
link. The system displays a web page that contains all the links for credit risk analysis. The user
then clicks on the verify link which displays an interface for the verification of the customer’s
identity, collateral, and business incorporation details. The details provided in the loan
application on the aforementioned are matched with the corresponding records in the database.
Any false information detected during the verification is recorded against the customer and the
application is automatically rejected. The customer’s external credit bureau rating is also
retrieved by this module. The user proceeds to the creditworthiness module if the customer’s
verification is successful.
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2. Creditworthiness Computation Module: The module is initiated by a click event on the
creditworthiness. The system displays a creditworthiness form where the user enters value for the
specified fields. This is six pages form that collect data to analyze customer’s financial, business,
relationship, management, and security risk. The system computes the creditworthiness based on
the data entered by the user. The score retrieved form the external credit bureau is also used in
this
computation. The result of this computation is stored in the database.
6.7 System Packaging
Package diagrams use packages that represent the different layers of a CWS to illustrate the
layered architecture of the system.
6.7.1 Package Diagram for Customer Verification Module
This package deals about the verification of information provided by the customer .This
verification is the first task performed by the loan officer when he successfully logs in to analyze
loan. It will use the customer’s data and collateral data to match that with the external agencies
database like, National identity register, credit bureau database, and business incorporation
database .
Customer Verification Package
Collateral
Value: VARCHAR(45)
Showvalue()
Customer
Customer ID: NUM(10)
Name: VARCHAR(45)
SOL: VARCHAR(6)
Industry: VARCHAR(45)
Getcustomerinfo()
External Agency
Type: object
Showdetails()
Fig. 14. Package Diagram for Customer Verification Module
6.7.2 Package Diagram for Creditworthiness Module
This package deals with the creditworthiness computation of a customer which can be done once
the loan officer finish customer’s verification and clicked on creditworthiness. It will use the
customer data and bank data, business risk, management risk, financial risk security risk and
relationship risk data to determine customer’s creditworthiness. The result obtained here is stored
in the database.
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Creditworthiness Package
Business risk
Businesssize: Num(1)
Businessoutlook: Num(1)
Industrygrowth: Num(1)
Markecompetition: Num(1)
Entryexitbarrier: Num(1)
Caculatebusscore()
Managementrisk
Experience: Num(1)
Succession: Num(1)
Teamwork: Num(1)
Caculatemanscore()
Financialrisk
/Leverage: Num(1)
/Liquidity: Num(1)
/Profitability: Num(1)
/Coverage: Num(1)
Calculatefinscore()
Security
Secoverage: Num(1)
Location: Num(1)
Guarantee: Num(1)
Caculatesecscore()
Relationshiprisk
Accountconduct: Num(1)
Personaldeposit: Num(1)
Compliance: Num(1)
Limitutilization: Num(1)
Caculaterelscore()
Banks
BAN_branch:Num(9)
BAN_bankname: Vchar(25)
Getcusbankinfo()
Customer
Customer ID: NUM(10)
Name: VARCHAR(45)
SOL: VARCHAR(6)
Industry: VARCHAR(45)
Getcustomerinfo()
Fig. 15. Package Diagram for Creditworthiness Module
7. CONCLUSION
We have presented a Digital Nervous System based creditworthiness system that combines
biometric and GPS technologies to help Nigerian Banks effectively detect fraudulent loan
application before determining the probability of default of a borrower. The use of UML as
adopted by object oriented approach in analyzing and designing the system makes future
enhancement of this system possible. Moreover the system will expedite loan processing time
thereby making loans available to diligent applicants on time. Determining borrower’s
creditworthiness is an important step to reducing credit risk. The value (PD) obtained at this
point is an essential parameter for calculating expected loss of a loan if given. The DNS
approach promotes high level of transparency in banks loan processing as well as help in
strengthening the Credit Appraisal Procedures of banks. Integration of biometrics fingerprint and
use of GPS system helps in detecting loan fraud. The researcher strongly believes that the system
has mechanism that heightens borrowers’ incentive to repay, and stop identify fraud, stop use of
fake collateral, reduce moral hazard; every borrower knows that if he defaults his reputation with
all other potential lenders is ruined, cutting him off from credit or making it more expensive.
Future work: The use of OOM in analyzing and designing DNS based CWS makes future
enhancement simple and possible. Additional modules like credit risk, operational and market
risk computation can be added thereby upgrading the system to handle the core risk elements of
banks.
REFERENCES
Lewis,E.M.1992. An Introduction to Credit Scoring. Athena Press, San Rafael
Mckenzie,D. 2002. Payment Systems and Infrastructure; Banks and Banking Reform. The World
Bank Group, Washington D.C.
Shelkh H., Mohammed B, and Rashid A. 2005. Digital Nervous System-DNS. Retrieved May
11, 2011 from http://www.itep.ae/english/EducationalCenter/Articles/dns_01.
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