=Paper=
{{Paper
|id=Vol-2649/paper7
|storemode=property
|title=Methods and Models of Machine Learning in Managing the Competitiveness of Audit Companies
|pdfUrl=https://ceur-ws.org/Vol-2649/paper7.pdf
|volume=Vol-2649
|authors=Lidiya Guryanova,Stanislav Milevskyi,Elena Piskun,Maria Belyaeva,Liliya Kasyanenko
}}
==Methods and Models of Machine Learning in Managing the Competitiveness of Audit Companies==
https://ceur-ws.org/Vol-2649/paper7.pdf
77
Methods and Models of Machine Learning in Managing the
Competitiveness of Audit Companies
Lidiya Guryanova 1[0000-0002-2009-1451], Stanislav Milevskyi2[ 0000-0001-5087-7036], Elena Piskun3, [
0000-0001-5087-7036]
, Maria Belyaeva4[0000-0001-8515-177X], Liliya Kasyanenko5
1.2,.
Simon Kuznets Kharkiv National University of Economics, Ukraine,
guryanovalidiya@gmail.com, milevskiysv@gmail.com
3
Sevastopol State University, lenapiskun@mail.ru, mariaoeb@mail.ru
4
EPAM Systems, USA, lks041198@gmail.com
Abstract. The growing demand for audit services, the entry into local markets of
international audit firms and their further offensive strategy aggravate the
competitive confrontation, both between national operators and between national
and international audit entities. The increasing complexity of audit firms activities in
the face of global competition leads to the need to develop effective mechanisms for
managing their competitiveness. It should be noted that audit firms cannot achieve
advantages over competitors in terms of technical, design, commercial or other
characteristics of the product due to the nature of the audit service. For the same
reasons, audit companies cannot strengthen their competitive position by organizing
pre- or after-sales services. It is obvious that the competitiveness components of
audit firms require a separate study, and the task of identifying the factors that form
the competitive advantages of audit firms requires a specific solution. The paper
proposes a set of models for assessing and analyzing the competitiveness of audit
companies, which, based on Machine Learning methods (main components, expert,
cluster, regression analysis, forecasting, panel data analysis techniques), can
improve the validity of assessing the competitiveness level of audit firms, and
determine the factors that render the dominant influence on the level of the
enterprise competitiveness, to formulate recommendations on ensuring a high level
of audit company competitiveness.
Key words: audit companies, competitiveness, assessment, forecasting, strategy,
model, Machine Learning methods
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
78
I Introduction
The current stage of economic development is characterized by increased competition.
This is due to large-scale integration processes and the complexity of companies activities
in global competition. Therefore, not a single organization and not a single enterprise,
whatever its size and importance, can afford to ignore the objective need to monitor the
level of competitiveness and develop preventive actions to improve its competitive
position. In these conditions, the competitiveness management of the company becomes
one of the basic subsystems of strategic management, the effectiveness of which largely
determines the viability of the company.
The foregoing fully applies to audit companies. It should be noted that the audit
services market is characterized by high development dynamics associated with the need
to reduce the risk of institutional investors, managers, owners when making management
decisions, expanding the range of professional services provided by audit companies in
the field of profitability analytics, diagnostics and optimization of business processes of
client companies etc. (Grand Views Research. 2019). Traditionally, the highest demand
for audit services is observed in large regional metropolitan areas. In Ukraine, such
agglomerations include, in particular, Kiev, Kharkov, and Odessa, characterized by
predominantly high growth rates of per capita GRP over the past decade (State Statistics
Service of Ukraine, 2019). Despite the high level of demand for the services of audit
companies, the dynamics of the number of subjects of audit activity in Ukraine is negative
(International institute of Audit, 2017), which indicates an increase in the quality
standards of audit services, increased competition between audit firms and the need to
formulate an adequate strategy to ensure competitiveness and strengthening competitive
market position.
The problem of managing the competitiveness of companies is widely considered in
the scientific literature. So, Zima (2012) proposed the structure of a decision support
system in the competitiveness management system of an industrial enterprise in an
unstable economic environment. Studies by Guryanova, Klebanova, Trunova (2017) are
devoted to the development of a model basis for information and analytical systems for
managing the financial competitiveness of industrial enterprises. Tatar, Sergienko, Kavun,
Guryanova (2017) address the issues of assessing the impact of currency “shocks” and
environmental factors on the level of competitiveness of metallurgical enterprises.
Research by Celtekligil, Adiguzel (2019); Guimarães, Severo, Vasconcelos, (2018) are
dedicated to assessing the impact of adaptive response rates on technological innovation
on a company's competitiveness. Piskun, Klebanova (2014), Li, Yong-Quan, Liu, Chih-
Hsing Sam (2018), Ferrer, Teresa, Garcés (2018) discuss the impact of organizational
structures on the level of competitiveness, sustainability and viability of companies. A
large number of works deal with various aspects of competitiveness management at
various levels of management. So, in the work of Aleksandrov, Buruk (2012), the issues
of developing mechanisms for managing product competitiveness as a basic component of
79
a company's competitiveness are considered. Researches of Birkentale, Winter (2012),
Zhu Zhihong, Zhu Zhiwei, Xu Ping, Xue Dawei (2019) are devoted to the macroeconomic
aspects of enterprise competitiveness management. The issues of evaluating models for
assessing and diagnosing competitiveness of companies are considered in the work of
Agovino, Matricano, Garofalo, (2020).
It should be noted that despite of the unconditional prospects of the approaches
discussed in the above literature, the issues of assessing the competitiveness of audit
companies whose activities have a certain specificity remain insufficiently studied. Thus,
audit companies cannot achieve advantages over competitors in terms of technical, design,
commercial or other characteristics of the product due to the nature of audit services. For
the same reasons, audit companies cannot strengthen their competitive position by
organizing pre- or after-sales services. It is obvious that the components of the
competitiveness of audit firms require a separate study, and the task of identifying the
properties that form the competitive advantages that ensure the competitiveness of audit
firms requires a specific solution. This led to the choice of research topic.
II Methodology and Data
The aim of the study is to develop models for assessing and analyzing the
competitiveness of audit companies, which unlike to existing ones, are based on the
integrated application of Machine Learning methods (main components, expert, cluster,
regression analysis, forecasting, panel data analysis techniques), that allow to improve the
validity of assessing the competitiveness level of audit firms, to determine the factors that
render the dominant influence on the level of enterprise competitiveness, to assess the
sustainability of competitive advantage in dynamics, to formulate recommendations on
ensuring a high level of audit company competitiveness.
The methodological approach proposed in the work to develop a complex of models is
shown in Fig. 1. The following is a brief description.
80
Modules Machine Learning Models
methods
Module 1. Comlex assessment of the
competitiveness of an audit company Expert analysis
(AC) methods Information
(ranking, Space
1.1. Justification of the signs information paired Formation
space of competitiveness AC comparisons), Model (M1)
1.2. Construction of an integrated principal
indicator of the AC competitiveness component
level method, rating
estimation
methods Integrated
Assessment
Module 2. Grouping of audit Model (M2)
companies by competitiveness level
Hierarchical
2.1. Grouping companies based on Companies
agglomerative
hierarchical agglomerative methods competitivenes
methods of CA
2.2. Grouping of companies based on s level
(Ward
iterative methods of cluster analysis (CA)
method), classification
2.3. Consistency assessment
iterative CA models (M3)
methods (K-
means method)
Module 3. Competitiveness level
prediction
Regression Competitivene
3.1. Building an integrated panel data analysis, ss level
model panel data forecasting
3.2. Building a model with a fixed effect analysis models (M4)
3.3. Model specification selection methods
Module 4. Diagnostics and selection of a Rating
company’s competitiveness strategy methods, A model for
multivariate diagnosing
comparison of and choosing a
4.1. Building a system of local integrated alternatives competitivene
competitiveness indicators ss
4.2. The choice of a strategy to improve improvement
competitiveness strategy (M5)
Fig. 1. The relationship between modules and models for assessing the competitiveness of audit
companies
81
To build a model for the formation of a diagnostic feature space (M1), expert
assessment methods and the principal component method are used. The expert survey
procedure was carried out according to the following algorithm: formation of examination
questions; formation of a group of experts; formation of rules for processing expert
opinions; statistical processing of expert assessments and determination of the degree of
coordination of expert opinions. To process the results of expert evaluation, the ranking
method, the method of pairwise comparisons were used. Assessment of the consistency of
expert opinions was carried out on the basis of Friedman statistics and the coefficient of
concordance.
The application of the method of principal components makes it possible to single out a
system of generalized factors, analyze the distribution of factor loads, and determine the
significance of the influence of individual factors on the competitiveness level of audit
companies. The algorithm of the principal component method provides for: determining
the matrix of pair correlations, finding eigenvalues and vectors, matrix of factor mapping,
evaluating the information content and interpreting the main components, finding the
equations of the main components, studying the dynamics of the values of generalizing
factors.
The construction of the M2 model is carried out using rating methods. Most often, the
integral score is defined as the arithmetic mean of standardized attribute values. The
construction of an integrated assessment involves the following steps: the formation of the
information space of signs; the choice of a indicators standardization method;
substantiation of the function of weight coefficients; determination of the indicators
aggregation method.
To develop the M3 model, methods of cluster analysis (MCA) are used. MCA can be
divided into groups: hierarchical (this group includes methods of the nearest neighbor,
distant neighbor, middle communication, centroid, median communication); iterative (K-
means method, dendrite method, balls method); factor methods; thickening methods;
methods based on graph theory. Each of the groups includes many approaches and
algorithms. To implement the classification model in the study, the method of "k - means"
is used. It is advisable to use it when the researcher has a preliminary idea of the number
of clusters (Guryanova, Milevskiy, Bogachkova, Lytovchenko, Polyanskiy, 2018). The
choice of the method is due to its following advantages: simplicity, flexibility, rapid
convergence.
The construction of the M4 model is carried out using econometric analysis methods,
in particular, panel data analysis methods. The following types of panel data models are
considered: conventional model; fixed effect model. Model specification selection is
based on the F-test. The forecast obtained on the basis of panel data models allows
assessing the stability of the company’s position in the cluster.
In the final module for developing a strategy to increase the competitiveness level, the
M6 model is developed on the basis of multi-criteria comparison of alternatives, in
particular “the web” method.
82
The above methodological approach was implemented based on data from International
networks and associations (Top 40 International Networks, Associations and Alliances:
Finding growth amid uncertainty). Taking into account the information security of
indicators, three groups of indicators of competitiveness of audit companies were
identified in open databases: group 1(G1) - economic performance of the audit
organization (income (Var1), number of firms (Var2), number of offices (Var3)); group 2
(G2) - level of professionalism of audit organization employees (professional staff (Var4),
female partners (Var5)); group 3 (G3) - business reputation and level of trust to the audit
organization (number of countries this company works with (Var6), number of partners
(Var7)).
Data processing was carried out using Statistica, EViews.
III Results and analysis
In the first module of the study, a model of the information space of attributes of AC
competitiveness was constructed. Employees of audit companies were involved to build
an expert model of the diagnostic feature space. Expert competency was assessed using
the self-assessment method. Processing of expert analysis data was performed using two
methods: ranking and partial pairwise comparison method.
The ranking was carried out in such a way: each expert had to assign to the ranking
objects (competitiveness indicators) a natural number from 1 to 7: 1 - the minimum rating
(the least significant indicator), 7 - the maximum rating (the most significant indicator).
The results are shown in Fig. 2.
Fig. 2. Indicator ranking results
The values of the coefficient of concordance, statistics , equal respectively 0,7748;
55,79 (Fig. 2), allow us to conclude that the opinions of experts are agreed, i.e. the
reliability of the results obtained as a result of the examination is high. According to the
results of the survey, the most significant indicator is the indicator Var1 - the company's
income, the least significant indicator Var5 - the female partners.
As an alternative data processing method, a partial pairwise comparison method was
considered. The results of expert analysis are shown in Fig. 3.
83
Fig. 3. Results of partial pairwise comparison of indicators
An analysis of the data (Fig. 3) allows us to conclude that the results obtained using the
partial pairwise comparison method are more consistent: the concordance coefficient is
0,91094; – 65,587. Therefore, in the future, when constructing a
comprehensive assessment of the level of competitiveness, weights were used, obtained
on the basis of the method of partial pairwise comparisons.
The results of expert analysis coincide with the results of processing statistical data of
20 leading audit companies by the method of principal components. The results of
constructing the system of principal components are shown in Fig. 4.
Fig. 4. Assessment of the information content of the main components
As can be seen from fig. 4, the first two main components account for 84.39% of the
variation in the initial system of features, which is sufficient to display all significant
correlation relationships. The “scree plot” shown in Fig. 5 also allows us to conclude that
the optimal number of principal components is two.
Fig.5. The «scree plot»
84
Fig. 6 shows factor loads. Statistically significant factor loads indicate a high
information content of the system of diagnostic indicators formed above.
Fig.6. Factor loadings
The final stage of the first module (Fig. 1) is the construction of an integral indicator of
the audit companies competitiveness level on standardized data. A complex assessment is
defined as the arithmetic average weighted, taking into account weights that reflect the
significance of indicators obtained on the basis of the method of partial pairwise
comparisons. The calculations are presented in table. 1.
Tab. 1. Integral indicator of the company's competitiveness
Complex Complex
Rank Company assesment Rank Company assesment
Deloitte Touche
1 Tohmatsu 26,54 10 TAG Alliances 6,40
2 PwC International 25,23 13 Baker Tilly International 5,95
3 EY Global 23,86 12 Nexia International 5,90
Moore Stephens
4 KPMG International 20,99 15 International 5,72
5 BDO Global 11,64 17 HLB International 5,09
Geneva Group The Leading Alliances/
6 International 8,52 14 LEA Global 4,93
11 Crowe 7,23 18 Kreston International 4,67
8 RSM International 7,00 16 Prime Global 4,51
7 Praxity 6,86 19 Fiducial International 3,20
Grant Thornton
9 International 6,82 20 BKR International 2,96
85
The results obtained allow us to conclude that the ranking obtained coincides with the
rating of International networks and associations. Thus, Spearman's rank correlation
coefficient for the two ratings is 0.97. The value of the Student criterion, equal to 73.67,
allows us to conclude that the results are consistent with a 99% confidence level. At the
same time, the simulation results show that the rating of the first six companies coincides
with the rating of International networks and associations, and some companies have
changed their position. So, the company Growe took 11th place, and now takes 7th place.
This is due to the fact that this company has a large number of professional employees
and works with many countries, and it was precisely these criteria that the experts
preferred and placed on the 2nd and 4th place in terms of significance in assessing the
competitiveness of AC.
In the second module, companies were grouped by competitiveness using cluster
analysis methods.
The classification dendrogram obtained using the Ward method is shown in Fig. 7.
Fig. 7. Classification dendrogram
The above results (Fig. 7) allow us to conclude that the initial set of companies should
be divided into three clusters. The composition of the clusters was determined using the k-
means method. The results are shown in Fig. 8.
86
a) Cluster composition
b) Average plot
Fig. 8. Clustering results based on the “k-means” method
As can be seen, the results of cluster analysis and expert analysis coincide. In the first
cluster, KPMG International has the least low rating - 20.99, and the next ranking
company, BDO Global, which is already in the second cluster, has a complex assessment
of the competitiveness level of 11.64. That is, there is a very large gap between the ratings
of the companies of the first and second cluster. The companies of the second and third
clusters differ slightly in their characteristics. However, for such variables as Var3 - the
number of offices, Var5 - the number of female partners, Var7 - the number of partner
companies, for the firms of the second cluster, the values significantly exceed the
indicators of the companies of the third cluster.
To assess the stability of the competitive positions of companies in the cluster in the
third module (Fig. 1), a forecast of the rating of companies was carried out using the panel
data model. The rating variable of the competitiveness of audit companies explaining the
variables - lag values of competitiveness indicators was considered as the resulting
variable. The following panel data model specifications were considered: a combined
model, a model with a fixed effect. Enumerating the various options for the panel data
model allowed us to choose a fixed-effect panel data model with this set of lag
explanatory variables: Var1t-1 - income, Var3t-1 – number of offices, Var7t-1 – number of
partners. The value of the fixed effect (ai) shown in Fig. 9, allows to conclude about the
stable competitive position of the companies of the first cluster.
87
25
20
15
10
5
0
-5 a1 a3 a5 a7 a9 a11 a13 a15 a17 a19
-10
Fig. 9. Fixed effect values (a1-a20)
The forecast values of the rating score obtained on the basis of the panel data model
with lag variables are given in table. 2.
Tab. 2. Predicted values of the integral indicator of the company's competitiveness
level
Integral Integral
indicator indicator
Rank Company forecast Rank Company forecast
Deloitte Touche
1 Tohmatsu 25,55 12 TAG Alliances 5,95
2 PwC International 24,71 11 Baker Tilly International 6,37
3 EY Global 23,45 14 Nexia International 5,59
Moore Stephens
4 KPMG International 20,53 13 International 5,69
5 BDO Global 11,99 15 HLB International 4,88
Geneva Group The Leading Alliances/
6 International 8,32 16 LEA Global 4,85
7 Crowe 7,15 17 Kreston International 4,67
8 RSM International 6,89 18 Prime Global 4,16
9 Praxity 6,85 20 Fiducial International 2,84
Grant Thornton
10 International 6,61 19 BKR International 2,98
As can be seen from the table. 2, the deterioration of competitiveness indicators for the
studied group of companies is forecasted. However, the composition of clusters in the
forecast period remains stable; only the competitive positions of companies within the
selected clusters change.
88
In the fourth module (Fig. 1), a model was developed for diagnosing and choosing a
strategy to increase the competitiveness of a company using one of the methods of
multidimensional comparison of alternatives - the web method. The results of the
implementation of the model for the companies of the first cluster are shown in Fig. 10.
Fig.10. Chart - “web” of local integrated indicators of the competitiveness level (G1 – economic
performance indicators; G2 – level of professionalism of employees, G3 – business reputation)
We see that all companies have close values of the G3 indicator - the business
reputation of the audit organization. The gap in competitiveness levels in this area. The
score is the smallest and is 1.19 times. We can also conclude that PricewaterhouseCoopers
(PwC) does not take the first position and is inferior to Deloitte in assessing business
reputation. However, the gap in the assessment of G2 - the level of professionalism of the
employees of the audit organization - between Deloitte, which ranks first, and PwC is not
very significant. However, PwC is lagging behind in economic indicators, so it is
advisable to choose strategies that are primarily aimed at improving economic indicators.
To increase competitiveness, the following strategies should be used: regional
diversification and expanding the range of countries and partners; implementation of a
differentiated pricing policy for the provision of audit services; expanding the range of
PwC services through consulting services, which will allow the customer company to
optimize the number of consultancy companies and cooperate mainly with PwC; to study
as much as possible and better the industries and problems of customer companies, to
promote the implementation of effective industry solutions. If you adhere to these
strategies, the company will be able to strengthen its competitive position, both in the
breadth of services and in economic indicators through the implementation of a systematic
approach to ensuring competitiveness.
89
Thus, the use of the proposed complex of models allows to choose an effective strategy
for managing the competitiveness of audit companies.
IV Conclusions
Thus, the studies conducted in the work allow to draw the following conclusions:
a methodical approach to the formation of a set of models for assessing and analyzing
the level of competitiveness of audit companies is proposed, which, based on Machine
Learning methods such as factor, cluster, expert, regression analysis, panel data analysis
methods, improves the validity of assessing the level of competitiveness of audit firms,
identifies factors, having a dominant effect on the level of enterprise competitiveness,
formulate recommendations on ensuring a high level of competitiveness audit company;
a complex of models for assessing and analyzing the competitiveness level of an audit
company has been developed, which includes: a model for the formation of a diagnostic
feature space; model of a complex assessment of the level of competitiveness;
classification models of companies by competitiveness level; forecasting models of the
enterprise competitiveness level; a model for diagnosing and choosing a strategy to
increase the competitiveness of an enterprise;
the modeling results showed that in the group of the 20 largest audit companies, three
clusters of homogeneous characteristics can be distinguished, which include 20%, 50%,
and 30% of the firms from the number of analyzed companies, respectively. Moreover,
the gap in average values of the level of competitiveness of the companies of the first and
second cluster exceeds 300%. The companies of the second and third clusters differ, first
of all, in terms of the number of offices and the number of partners. The predicted values
of the competitiveness level obtained on the basis of the panel data model taking into
account lag variables showed that the composition of the selected clusters will remain
stable. However, there is a fairly strong change in the rating positions of companies in
clusters. Diagnostics of the competitiveness level of the companies of the first cluster on
the basis of local integrated assessments in such areas as economic indicators, the level of
employees professionalism, business reputation, made it possible to draw a conclusion
about the insignificant differentiation of companies in terms of business reputation and
level of professionalism of employees. The most significant differences are observed in
economic indicators. Therefore, strategies aimed at improving economic indicators should
be considered as basic strategies for strengthening competitive positions. In particular,
such as expanding the range of consulting services and strengthening industry
specialization.
90
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