A Case-Based Reasoning System to Assist the Internal
Control Process of a Firm
M. L. Borrajo Diz1, J. C. Yáñez López2, and J. M. Corchado Rodríguez3
1Department
of Computer Science, University of Vigo, Campus As Lagoas, s/n, 32004
Ourense, Spain
lborrajo@uvigo.es
2Department
of Financial Accounting, University of Vigo, Campus As Lagoas, s/n, 32004
Ourense, Spain
jcyanez@uvigo.es
3Department
of Computer Science, University of Salamanca, Plaza de la Merced s/n, 37008
Salamanca, Spain
corchado@usal.es
Abstract. A case-based reasoning (CBR) system is being developed to facilitate
the internal auditing process in small to medium companies. The CBR system
includes kernel methods in the retrieval stage, a radial basis function network
during the adaptation process, and a rule-based system for the learning phase.
The system automates the auditing process and assists the auditor with
performing his work. The kernel methods helps to reduce the size of the case
base without losing valuable information and improves the retrieval process.
The proposed system estimates the current state of the company and predicts the
associated risk. It also generates a series of recommendations so that the
company evolves toward a more favorable situation.
1 Introduction
Auditors are faced with a continuously changing environment. The use of
increasingly complex organization systems has deeply altered the mode of operation
in companies. The number of regulatory norms has increased considerably. This
imposes the need of evaluation mechanisms that facilitate the internal controls as a
means to increase the effectiveness of the audit, motivating the growth of the
importance of internal controls.
Several expert systems have been applied to the auditing field. Rule-based systems
(RBS) have traditionally been used with the purpose of delimiting the audit decision
making tasks [3]. However, RBS have a limited capacity to extract information from
the experience of the auditors, which would enhance their ability in problem solving.
Audit experts have difficulties in defining the sequence of rules (there can be
hundreds of them) used to define the subject under consideration. In general, RBS
achieve better results in those fields in which, through a limited series of rules, a large
amount of knowledge can be represented [1].
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To try to overcome these problems we are developing a CBR system [2,4] for
assisting in the internal control process of small to medium enterprises (SME). Here
we outline the basic characteristics that describe the proposed system, which is also
capable of detecting the weaknesses in a company’s workflow. The system helps
auditors to take decisions based on the risk associated with the current state of the
company, and in generating recommendations.
2 The Proposed CBR System
The structure of the developed system is based on CBR. In the retrieve phase, it uses a
variant of a kernel method as a mechanism for selecting similar cases to the given
problem. A RBF (Radial Basis Function) neural network is used during the reuse
phase to facilitate the adaptation of the cases previously retrieved to the context of the
new problem. During the retain phase the success probability associated with each
case is modified, taking into consideration information about system performance.
Fig. 1. CBR system control flow
The processes carried out inside a company are included in functional areas [5].
Each one of these areas is considered a function. This way, the functions that are
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usually carried out in a company are: Purchases, Treasury, Sales, Information
Technology, Immobilized, Legal Normative Execution, and Personal Politics. Each
function can be broken down into a collection of activities. For example, the function
Information Technology is divided into the following activities: Computer plan
development, Study of systems, Installation of Systems, Treatment of the information
flows and Security management. In the same way, each of the activities includes a
wide range of concrete tasks (register, authorize, approve, harmonize, separate
obligations, operate, etc.).
A case represents specific knowledge about a particular situation of the firm. A
case is created to represent the “shape” of each one of the activities developed in the
company. As each activity is composed of several tasks, it is necessary that these
tasks have been carried out correctly so that the activity is developed appropriately.
Table 1 shows the attributes of a case.
Table 1. Case attributes
IDENTIFICATION DESCRIPTION
Case number
Unique identification: positive integer number.
Input vector
Information about the tasks (n sub-vectors) that compose an
industrial activity: ((t1,GI1,V1),(t2,GI2,V2),...,(Tn,GIn,Vn)) for n
tasks.
Output vector
Function number
Activity number
Reliability
Each task sub-vector has the following structure: (Ti,GIi,Vi)
 Ti: Task unique identification number.
 GIi: importance for this task inside the activity. Can
only take one of the following values:
VI (Very important).
I (Important).
NVI (Not very important).
 Vi: Value of the realization state of a given task.
Positive integer number (between 1 and 10).
Vector with two parameters: The activity state and the associated
risk level.
The field “activity state” takes three values:
 Insufficient: if the activity is being carried out poorly.
 Nominal
 Satisfactory: if the activity is being carried out perfectly.
The field “risk level” can take three values:
 High
 Nominal
 Low
Unique Identification number for each function.
Unique Identification number for each activity.
Percentage of probability of success.
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The auditing method is divided into two processes. Process A helps to determine
the state of each activity and its associated level of risk and Process B generates the
recommendations to improve a given activity. These Processes are now outlined.
Process A involves the following steps:
(a1) Retrieve the most similar cases to the problem case using kernel methods.
(a2) Reuse the case(s) to attempt to solve the problem, using a RBF neural
network.
(a3) Revise the proposed solution. A human expert carries out this step.
(a4) Retain the new solution as a part of a new case
Process B is divided into the following stages:
(b1) In this step, the case base is searched to find the most similar cases to the
problem case. The cases with higher value in the “activity state” (in output
vector) and "reliability" are selected. These retrieved cases are then used to
generate the recommendations.
(b2) Reuse the retrieved case(s). A human expert carries out this stage. The
evolution of the company during the last year is analysed.
(b3) Generation of recommendations. A rule-based system is used to identify the
tasks that should be improved and therefore modified. The differences
between the input vector of the problem case and the input vector of the
retrieved case are used to generate such recommendations.
(b4) Retain. Cases are modified depending on the system success. Once the
company has applied the generated recommendations, the company is
evaluated. If the company has evolved positively, the reliability attribute of
the cases is increased. Otherwise, the reliability is decreased.
We are now on the process of implementing the system. Once we have
implemented the basic infrastructure, different techniques will be tested and evaluated
for the retrieval, reuse, revision, and adaptation stages. Data is now being collected
and although several partial experiments have been carried out, the previously
outlined project is just starting.
References
1. Chandrasekaran, B.: Expert systems: matching techniques to tasks. In Reitman, W. (ed.),
Artificial Intelligence Applications for Business, Ablex, Norwood, NJ, (1984) 41-85
2. Corchado J. M. and Lees B.: Adaptation of Cases for Case-based Forecasting with Neural
Network Support. In: S.K Pal, T.S. Dillon and D.S. Yeung (Eds.), Soft Computing in Case
Based Reasoning, Springer Verlag, London (2000)
3. Denna, E.L., Hansen, J.V. and Meservy, R.: Development and application of expert systems
in audit services. Transactions on Knowledge and Data Engineering (1991)
4. Fyfe, C. and Corchado, J. M. Automating the construction of CBR systems using Kernel
Methods. Computing and Information Systems Journal. Vol. 7, No. 7, pp. 29-43, (2001)
ISBN: 1352-9404
5. Yañez, J.C., Borrajo, L., Corchado, J.M.. A Case-based Reasoning System for Business
Internal Control. Fourth International ICSC Symposium on Soft Computing and Intelligent
Systems for Industry. Scotland (U.K.) (2001)
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