CHAPTER FOUR: DATA ANALYSIS AND INTERPRETATIONS
This chapter applies the framework of problem loans management
developed, from the last
chapter to some problem loans files. The idea is to compare the
Bank's handling of problem loans with the criteria of the framework.
In this chapter, we will:
- Present how we sampled the population, defined the variables
and collected data.
- Explain the methodology we followed to analyze and interpret
data
- Present the outcome of data collection
- Analyze and interpret data
For matters of confidentiality, names of companies studied are
not given. Only numbers are attributed to files to identify them.
I. Data collection
1. Sample selection
The population of the study was all problem loans files
classified at January 31, 2005 and the
unit of analysis a problem loan file. The size of the population
was 24 and the sample size
50%. The stratified method was used for the following reasons:
- The population was incongruous because problem loans files
fall into four categories.
- The sample was designed to reflect the structure of the
population so that files from each class would be studied.
The structure of the sample was as follows:
|
Classification
|
Population size
|
Theoretical
sample size
|
Practical
sample size
|
|
Class IA
|
11
|
5,5
|
6
|
|
Class II
|
3
|
1,5
|
2
|
|
Class III
|
3
|
1,5
|
2
|
|
Class IV
|
7
|
3,5
|
4
|
|
Total
|
24
|
12
|
14
|
Finally, a practical sample of 14 problem loans files was
used.
36 MBA in Banking and Finance
2. Variables
Before listing the variables studied, it is important to recall
the being answered in this part.
The question is: «What actions are taken when loans
become problem loans?» The four variables focused on are:
- the financial situation and/or age of the overdue amount
- the classification
- the provision
- the remedial strategy
a. The financial situation and/or overdue
period
The financial situation of a borrower is the main factor that
justifies its classification in the Bank's classification system. It is
more likely to help understanding the classification and then the
provision to be made and remedial strategy to be adopted.
In the classification we built and considered the best, the main
factor of classification is the overdue period. Nonetheless, the financial
situation of the borrower is not meaningless.
Then, we collected information on both financial situation and
overdue period to explain and justify the classification.
Information on financial situation of companies was in the
form of text and figures. To avoid problems of interpretation and find
standards of judgment (Hussey and Hussey, 1997), we decided to
detextualize and rank the different financial situations on a Likert
scale. The following ranks were kept:
1. Mitigated 2. Serious losses, critical 3. Poor 4. Distress
b. The classification
This variable is important because when a loan becomes a
problem loan the first action is to classify in order to highlight and
keep attention on it and separate it from «safe» loans.
Moreover, the classification indicates the level of riskiness of the loan. As
seen before, the Bank runs a five-tier classification system: I, IA, II, III
and IV; I being the unclassified class.
For this reason, we will have four classifications: IA, II III
and IV.
The Bank's classification system is analogous to that
developed for this study and this is shown below:
37 MBA in Banking and Finance
|
Bank's classification
|
Our classification
|
|
I
|
A
|
|
IA
|
B
|
|
II
|
C
|
|
III
|
D
|
|
IV
|
E
|
c. The provision
Once a loan is classified, provisions must be taken to cover the
expected losses and the level
of provision depends on the classification level and must
comply with it. This reflects prudence from the accounting perspective and
it is required by regulation.
d. The remedial strategy
By remedial strategy, we mean the actions taken by
the Bank to follow-up the credit and make sure repayment will be
made. Due to the complexity and the heterogeneity of the remedial
strategies, this variable was not ranked this variable as for the
financial situation. Nevertheless, we summarized strategies on a case-by-case
basis.
3. Data collection method
Our data source was the Monthly Classified Loan Management
Reports (MCLMR) from two
departments that deal with large corporates and SMEs. Information
contained in the reports includes:
- Facilities and outstandings.
- Brief classification history.
- For new classifications, a brief relationship background.
- Provisions.
- Security or Support held, inclusive of estimated asset
value.
- Summary of latest financials.
- Reasons for classification and action plan.
The study made is a longitudinal study and the aim
was to observe the behavior of the different variables at three different
periods. The periods chosen were: January 31, April 30 and August 31 of the
year 2005.
Questionnaires were used for data collection (see format appendix
3).
38 MBA in Banking and Finance
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