Expert systems of real time as key tendency of artificial intelligence in tax administration
Table of contents: The Kazakh-American Free University Academic Journal №9 - 2017
Biryukov Alexander, Professor of economic theory and analysis; Doctor of economic Sciences, associate Professor, Sterlitamak branch of Bashkir State University, Sterlitamak, Russia, Bashkortostan Republic, Russia
, Associate Professor of Germanic languages; Candidate of pedagogical Sciences, associate
Professor Sterlitamak branch of Bashkir State University, Russia, Bashkortostan
In difficult conditions of modeling, classical modeling techniques
are ineffective or even completely unacceptable.
This is due to the fact that it is impossible to describe the
reality adequately with the help of a small number of model parameters, because
the calculation of a model requires too much time and computing resources, and,
most importantly, conditions of these methods application are not fulfilled
and, therefore, it becomes impossible to use appropriate statistical criteria
to evaluate the adequacy of the produced models.
Because of the afore-named drawbacks of traditional methods
analytical systems of a new type have been actively developing of the last ten
years. They are based on the artificial intelligence technologies that mimic
natural processes, such as the activity of neurons in the brain or the natural
Artificial Intelligence (AI) is an area of scientific knowledge,
bringing together a large number of areas involved in the study of the
principles and laws of human mental activity and modeling tasks that are
traditionally considered to be intellectual (Biryukov, 2011).
According to modern presentation, artificial intelligence (AI) is
defined "as a scientific discipline whose goal is to develop hardware and
software, allowing the user - non-programmer to set and solve the tasks that
are traditionally considered intellectual, communicating with computers on a
limited subset of a natural language."
The step of expert systems (ES) inception in the "bowels"
of AI systems is a shift from the paradigm of heuristics to the paradigm of
developing ways by which a professional expert presents nonformalized
knowledge, methods and informal rules, which he uses in making decisions.
Expert System (ES) - a program or set of programs, that makes it
possible to present and systematize the expert knowledge in a certain
application area in a suitable computer form and, based on this view, to solve
applied problems like an expert at the request of users (Biryukov, 2011).
ES relates to knowledge-based systems, and includes the
characteristic elements of these systems:
• Knowledge Base (KB). In the self-learning systems, knowledge base
contains information which is the result of previous problems solutions;
• a mechanism for obtaining solutions (inferential mechanism);
• means of interface to communicate with the user.
The essence of expert system structure is illustrated in Figure 1.
Designed to the date ES usually solve applied problems of the
• preparation of semantic descriptions of objects on the input data
(e.g., interpretation of symbols, signals);
• prediction of certain disorders (e.g., symptoms of diseases);
• contingency planning;
• monitoring and others.
The structure of the expert system
ESs are effectively used in various fields of human activity:
medicine, geology, economics, and others. There are several reasons for this:
1) there is an opportunity to solve previously unavailable, badly
formalized problems involving a new, specially designed for this purpose
mathematical apparatus (semantic networks, frames, fuzzy logic, neural
2) ESs are focused to be exploited by a wide range of professionals
(end users), who communicate with the system in dialog using comprehensible
reasoning techniques and the terminology of specific subject area;
3) the use of ES can dramatically increase the effectiveness of the
solutions taken by ordinary users, due to the accumulation of knowledge in ESs,
including the knowledge of the highest qualification experts.
When creating artificial intellectual systems a large number of
information technologies (ES, advise-giving systems, decision-making support
systems, execution of decisions) are used. The common feature of these
techniques is the use of some form of human knowledge. If we highlight the
technology aimed to solve economic problems, this class of systems will be
called "economic advice-giving systems" (EAS).
Here is the definition of EAS taken from scientific literature:
"Under the EAS we mean any software-based product that reflects the
economic specialist professional knowledge, his skills and experience used in
the process of issuing solutions for the user." Romanov & Odintsov
(2000) worked out the classification of EAS, which is based on the principle of
simulation of the expert’ thinking processes and divided EAS into two classes:
1) EAS reproducing conscious thought processes of a man;
2) EAS reproducing unconscious thought processes of a man.
Decision makers in real control systems in the economic field usually
carry out the following thought procedures:
• draw conclusions and develop management decisions based on the
analysis of complete, incomplete and unreliable knowledge, i. e. in the
conditions of uncertainty;
• explain and can justify why they have come to this conclusion;
• improve their knowledge, re-systematize them, study their own and
• make exceptions to the rules; use contradictory and improbable
• determine the level of their competence, i.e. determine whether they
can or cannot make decision in this particular case.
Methodology and Data
2.1 Review of neural network instruments for Mathematical Modeling
Neural network models are widely used in the ECs to represent and
accumulate knowledge (McCulloch & Pitts, 1943; Haykin, 1999; Chernik, 2010;
Yasnitsky, 2010). Note that there are three reasons for the rapid development
of methods for neural network modeling in general and particularly in the
sphere of EAS:
1) In the neural network models parallel calculation method is
implemented, i.e. several steps for computing operations are carried out at the
same time. Due to this, the speed of a neurocomputer (electronic structure or
neyroemulyator) increases sharply.
2) Neural network model does not require prerequisites of classical
regression analysis, which is particularly important for the study of economic
systems, where these prerequisites may not be fulfilled.
3) Though neural network models are parametric, they do not require
pre-guessing of the form (structure) for the model.
The use of neural networks provides the following useful properties
for the model:
1) Non-linearity. Artificial neurons can be linear or nonlinear.
Neural networks which are constructed from compounds of non-linear neurons are
nonlinear themselves. Moreover, this nonlinearity is of a special kind, since
it is distributed over the network. Non-linearity is an important feature,
particularly if the mechanism, responsible for the formation of an input
signal, is also non-linear.
2) Display of the input information into the output information. One
of the most popular paradigms of learning in neural network system is training
with the teacher. This implies a change of synaptic weights based on a set of labeled
training examples. Each example consists of an input signal and the
corresponding desired response. From many examples one is randomly selected,
and the neural network modifies the synaptic weights to minimize the differences
between the desired output signal, and the signal, formed by a network in
accordance with the selected statistical test. This training is carried out as
long as the changes in synaptic weights will be insignificant.
3) Adaptability. Neural networks have the ability to adapt their
synaptic weights and the very structure of the model to the changes in the
environment. In particular, a network trained to operate in a particular
environment can easily be retrained to operate in the conditions of minor
oscillations of the medium parameters in the environment. Moreover, for operation
in nonstationary environments, the neural networks, that change synaptic
weights in real time, can be created. At the same time it should be noted that
adaptability does not always lead to sustainability, for example, the adaptive
system with parameters rapidly changing over time, can quickly react to
extraneous field that cause the loss of productivity. In order to use all the
advantages of adaptability, the basic parameters of the system must be
sufficiently stable so to ignore external interference, and flexible enough to
provide a response to significant changes in the environment.
An important restrictive feature of the neural networks used in EAS
for representation knowledge is that unlike other models reproducing determined
connections, clearly articulated by the expert, the neural network is not able
to explain its results. Therefore in instrumental ES where it is possible to
use several models of representation of knowledge, the neural network should be
complemented by logic or production models.
2.2 Review of Models for Presentation of Knowledge
Consider the advantages and disadvantages of models for
representation of knowledge in the EAS.
Comparative characteristics of models for representation of knowledge in EAS
We give short comments to Table 1.
Logic models. The main idea of the approach in constructing
logical models for knowledge representation is that all information necessary
for solving applied problems, is considered as a collection of facts and
statements that are presented as formulas in some kind of logic (Pospelov,
1989; Hayes-Roth & Jacobstein, 1994). This class of models is based on the
predicate calculus and deals with the statements (affirmations), which can be
true or false using Boolean algebra operation (disjunction ,
conjunction , negation , implication , and
others.). Complex statements can be constructed in KB. For example,
where P, Q, R
are predicates; g ()
- any logic function, for example, implication; x, y are object variables
The truth of this formula is determined by the specific values of
the variables (x, y); the variables and the function values can take only two
values: 1 and 0.
Frame models. In 1974 this type of models was first proposed by
Marvin Minsky (1975, 1979), a professor at Massachusetts Institute of
Technology (USA). A frame was considered as a minimal description of an object,
i.e., a set of its features (attributes); the omission of any attribute makes
the description of the frame impossible. Frame sample:
where Z is
the frame name; Yi is the slot name; Ti – the slot value; Ai - the name of the associated procedure.
Slots comprise a frame basis. Slots are some unfilled structural
elements of a frame. When filling the slot, this frame is associated with the
considered situation, the object, the phenomenon. If a frame has empty slots,
it is called a frame-prototype and a frame with filled slots is called a
frame-example (or frame-instance).
Semantic networks. Existential graphs can be considered the
progenitors of modern semantic networks. Existential graphs were proposed by
Charles Pierce (1909). They are used to represent logical statements in the
form of special diagrams. Pierce called this method “the logic of the future”.
This is a chart pattern in the form of a graph, its vertices (nodes) are associated
with some concepts (objects, events, processes), and the links (arcs) of the
graph reflect the relationship between these concepts. The links can be
diverse: temporal, spatial relations, cause and effect links, and others.
Production models. The term “products” was introduced by the
American mathematician E. Potts in 1943. Nowadays, along with frames, products
are the most popular forms of knowledge representation. The knowledge is
presented in the form of sentences (production rules): “If A, then B”, where A,
B are statements. In Logic this rule of deductive inference is called “Modus
ponens”. For example: “If the temperature of a person is high, he should see a
doctor” (Gavrilova & Khoroshevsky, 2000).
In general, a production rule is understood as the following
Where i is the name of the product (its serial number or
nomination); Q is the scope of the product application; P is the
condition for the applicability of a product (predicate); is “the core of a product” (A is a condition, B is an
action); is the sign of a sequence, which often coincides with a logical
implication; N – product post condition, indicating what should be done
after action B.
In practice, in order to describe quite complex objects (processes,
events) not one product, but the set of products is used. When working with
these systems of products, there may be questions about the systems’ completeness,
consistency, the extension of the gained knowledge. The totality of these
productions (rules) generates a "tree"-type structure of the arguments.
In this structure the initial (root) vertex corresponds to the original statement,
and the end (terminal) vertexes ("leaves") correspond to the results
of reasoning. Each reasoning must contain a group of true statements
corresponding to the intermediate tree nodes, situated on the path from the root
to the definite terminal node. The search of this path can be performed by
means of various algorithms, called inferential mechanism.
The abilities for effective work with the knowledge presented in the
form of production models, are embedded in artificial intelligence languages
LISP and PROLOG, which are specifically designed to treat symbolic information
and automate logical reasoning (Vasilyev, 1981).
Fuzzy logic models. In 1964 L. A. Zadeh put the basis of fussy logic, i.e. a new approach to analyzing complex systems and decision-making
processes under uncertainty (1996). The essence of this approach is as follows:
• fussy logic uses the so-called linguistic variables, that are
e[pressed by whose terms (symbols) (words, phrases, sentences), expressed in
• simple relationships between the variables are described using
• complex relationship are described by the fuzzy algorithms for
special operations on fuzzy sets (equivalence, inclusion, union, intersection,
complement, the algebraic sum and product concentration, stretching, etc.).
Fuzzy algorithm is an ordered set of fuzzy rules, formulated by vague
L. Zadeh’s ideas, focusing on the modeling of decision-making
processes under uncertainty, have a lot of supporters and are widely used as a
tool for the building of AI real systems.
The technology using fuzzy logic promotes the development of systems
using intuition and engineering knowledge (know-how). Fuzzy logic uses the
concepts of everyday language to determine the behavior of the system. It
allows to create robust, fault-tolerant systems.
To sum up comments on Table 1, we note that the EAS can include a
combination of different types of models for knowledge representation. This
must generate the synergistic effect (the emergence) by strengthening the advantages
of the basic model and reducing its negative characteristics and limitations.
For example, when there is a combination of fuzzy and neural network models of
knowledge representation in the EAS, fuzzy inference systems allow giving clear
interpretation of the performed actions, but they cannot be taught, i.e. to
perform automatic setting of parameters for membership functions on the basis
of known information. In contrast, the neural network can customize their
parameters (weights), but the functions, which they realize, can’t be clearly
interpreted. The most effective way for hybridization of fuzzy logic and artificial
neural networks, is a neuro-fuzzy system (more precisely - "neural inference
system"), which, on the one hand, can be regarded as a fuzzy inference
system (and thus, to interpret clearly the obtained results), and on the other
hand - as an artificial neural network, that contain a special type of neurons
and therefore, it can be trained.
Thus, fuzzy logic is a technology that enables the development of
systems using intuition and engineering knowledge (know-how). Fuzzy logic uses
the concepts of everyday language to determine the behavior of the system. It
allows building robust, fault-tolerant systems (Zimmermann, 1996).
Results and Practice
Russian scientist Dmitry Chernik (2010) describes the ES, which is
used by the Canadian Internal Revenue Service for verification of a company
income tax and VAT. To develop the expertise rules for the selection of taxpayers
in Canada, a group, consisting of 30 most qualified tax inspectors was created.
These inspectors told the experts in artificial intelligence (AI), why some
declarations seem suspicious to them, what things should be paid priority
attention during the checking, and what amount of additional charges should be
expected. To perform field tax audits, all these rules were introduced into the
system of the computerized selection of taxpayers. The following sources of
data are used: the data from tax returns, the data from previous field tax
audits, the data on the structure of earnings in the area where the taxpayer
lives. This ES allows to look through tax returns in automatic mode and to
classify them into two classes: Class 1 – “field tax audits should be
performed”; Class 2 – “it is not necessary to inspect”. The value of the
expected additional charges is estimated in Class 1.
The US Federal Revenue Service uses the following combined (hybrid)
model for selection of taxpayers for performing field tax audits (Chernik,
2010). A special data base of individuals and small and medium-sized businesses
is collected according to the results of their special scrutiny, conducted in
the framework of the Program "Measuring of taxpayers’ law-abiding."
Randomly selected tax returns are classified by the main source of income. A
discriminatory function is the amount of income (or gross income of the
company). The classes’ labels are known from the results of previous checks.
Then, using statistical methods specialists estimate the probability of
additional accruals after the documentary check of the declaration from this
class. This statistical model links the simulated core indicator with others,
which the taxpayer showsin his declaration. This model is constructed as
follows. Take a sample of declarations for the class, where the results of additional
charges are known from previous audits and all these declarations are divided
into two subclasses: 1 - "should have been checked"; 2 - " should
not have been checked " (0 or 1).
The belonging of the declaration to one of these two categories is
the modeled variable Y.
The simplest ES, which takes into account the uncertainty factor, is
as follows. For simple regression:
version of multi-stage OLS is applied, i.e., first, a large number of order
factors (150) is included in (4), then their number is reduced by an order and
more, i.e. only the most significant factors for the explanation of the
simulated variable are retained. Thus, the model of knowledge representation in
the EC is a linear or non-linear regression equation (4), supplemented by the
following production rule:
The required value interval is given by the expert.
In this regression equation, non-absolute values of these factors from the declaration are used, but dimensionless complexes, formed
from these factors.
After receiving the model (4), this simple regression formula is
applied to all selected declarations of this class. The selected declarations
are submitted to a highly qualified tax inspector (called a «classifier "
in the US) for censorship. The classifier scans all the selected declarations
and gives his verdict about each one: whether it should be checked or not, and
what points should be especially focused on during the verification. In the United States, approximately half of all field inspections of taxes on personal and corporations
income is organized using this ES selection.
According to D. Chernik’s study, tax services of many countries
process tax returns using statistical methods such as regression and
discriminant analysis, and according to the results of this analysis they build
formulas, which allow to draw conclusions about new tax declarations: whether
the check of a taxpayer promises large accrual or not. The documentary audit of
taxpayers selected in such a way, is given priority.
Despite the intensive development of the theory and practice of
neural network modeling in relation to the difficult conditions of economic
systems modeling (hard formalization of the processes and interactions in the
system), very noisy data (deliberate distortion of the tax base) because of the
unknown laws of noise distribution, many problematic issues of neural network
modeling of these systems have been either poorly researched or not researched
1. The budgetary system of any level faces the problem of the
insufficient amount of own funds for the projects focused on the end result and
in the broader sense, for the budget system functioning, i.e. providing timely
and quality services for budget spending units, ensuring the preservation of
the financial and social stability and the development of the territories.
Therefore many regions get subsidies. When transfers and subventions are
allocated, the regional and municipal accounting documentation is distorted in
the direction of increasing the budget deficit. In some cases budget planning
is still based on the principle “from the achieved level”. So, one of the
urgent problems is forecasting of budgets’ filling, particularly, in
municipalities, taking into account the risk of going beyond the confidence
limits of the forecast based on hybrid neural network models.
2. Lack of computer techniques for multi-criteria ranking of
budgetary institutions and organizations, which would allow to assess
objectively the results of the organization’ activities at the current time and
a forecast period. Such techniques would allow to distribute transfers more equitably
and efficiently in terms of governance and to determine the directions for the
rational development of budgetary organizations.
3. The taxpayers’ activity is characterized by uncertain external
and internal environment. The result of these trends is the spread of the
output parameters for the organizations’ economic activity, which in many cases
determines the high risk for inefficiency of tax audits. The objectives of the
tax control are creative in nature; they require specialists with extensive knowledge,
experience and developed intuition. Therefore, the transition to the mathematical
formalization of the decision-making stages faces a number of difficulties
associated with the problem of modeling for poorly formalized systems.
The Federal Tax Service of the Russian Federation use information
technology for desk audits, selection of taxpayers for on-site inspections,
These technologies are generally aimed to automate the monitoring of declared reporting
data, their analysis for the logical consistency of the interrogation mode,
where each subject of taxation is analyzed in turn.
The main disadvantage of the existing methods of tax control is as
follows: all the technology of tax audits planning is subjective.
In addition, a number of factors increase the tax uncertainty, such
as: the increasing complexity and diversification of the taxpayers’ activity,
the diversity of the legal aspects of transactions between the taxpayers, the
taxpayers’ risky activity, the raising stochasticity of the international
In such circumstances, there is need for new computer technologies
for taxpayers’ selection.
So, we can conclude, that the level of development of theoretical
and methodological foundations for neural network modeling in intelligent ECS
for economic systems, does not meet the requirements of the practice, due to
the ongoing process of reforming in the budget and tax system of the Russian Federation.
The modernization of the fiscal system in Russia and new financial
and fiscal instruments pose new challenges requiring innovative solutions and
rapid application in practice.
Solving this problem is aimed at solving algorithmically complex
problems, as well as accumulation of scientifically based knowledge about the
object, i.e., it is designed to maintain the existing system of economic models
of the object of study and to add it with missing models and objectives. In
applied economic aspect the problem is focused on improving the efficiency of
state administration in the area of fiscal systems at all levels.
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Table of contents: The Kazakh-American Free University Academic Journal №9 - 2017