1 Zijmr Vol3 Issuse12 December2013

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




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




3

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




4

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.




5

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




6

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




7

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.




8

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




9

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




10

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


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




11

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).


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).


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




12

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.


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


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.


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.




13

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




14

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,




15

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.




16

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




17

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

Email

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.




18

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




19

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




20

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.




21

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.




22

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.

1 Zijmr Vol3 Issuse12 December2013

Documents

Transcript of 1 Zijmr Vol3 Issuse12 December2013