=Paper=
{{Paper
|id=Vol-2917/paper46
|storemode=property
|title=Economic Crime Detection Using Support Vector Machine Classification
|pdfUrl=https://ceur-ws.org/Vol-2917/paper46.pdf
|volume=Vol-2917
|authors=Andriy Krysovatyy,Hrystyna Lipyanina-Goncharenko,Svitlana Sachenko,Oksana Desyatnyuk
|dblpUrl=https://dblp.org/rec/conf/momlet/KrysovatyyLSD21
}}
==Economic Crime Detection Using Support Vector Machine Classification==
https://ceur-ws.org/Vol-2917/paper46.pdf
Economic Crime Detection Using Support Vector Machine
Classification
Andriy Krysovatyy, Hrystyna Lipyanina-Goncharenko, Svitlana Sachenko and Oksana
Desyatnyuk
West Ukrainian National University, Lvivska Str., 11, Ternopil, 46000, Ukraine
Abstract.
Fictitious business – is the creation or acquisition of business entities in order to cover up illegal
activities or activities that are prohibited. Investigation of economic crime takes a lot of time
for law enforcement officers, so in this regard, the development of an algorithm for detecting
a fictitious enterprise based on the classic method of machine learning, namely Support Vector
Machine Classification, will develop a single software environment for rapid detection of
economic crimes. To build the method, data from 1,100 companies operating in Ukraine were
used. The data presented in in the set logical binary values are from 355 fictitious enterprises.
Modeling of the Support Vector Machine was performed by 3 approaches: linear, polynomial
and radial. The best results are obtained from classification by polynomial approach. The
training sample showed evaluation results at 100%, and the test sample showed evaluation at
99.7%. Also, the confusion matrix showed quite good results.
Keywords 1
fictitious enterprises, business entities, classification, machine learning, Support Vector
Machine Classification.
1. Introduction
A fictitious enterprise in Ukraine should be understood the following: a business entity that is
registered in violation of the established procedure (legal norms) of registration with state bodies, the
constituent documents of which do not comply with applicable law, or to carry out activities contrary
to law or constituent documents, or violation of the procedure for tax accounting and deadlines for filing
tax returns and financial statements, or violation of the deadlines for submission of information to
government agencies about the change of name, organizational form, form of ownership and location
[1].
The main reasons for the emergence and existence of economic crime and fictitious entrepreneurship
are: imperfection of legislation governing economic activity, high levels of corruption, bondage of
taxes, control of corrupt individuals in major industries, low professional level of law enforcement
officers in detecting, documenting, investigating these crimes.
However, the investigation of economic crime often takes a lot of time for law enforcement officers,
so in this regard, the development of an algorithm for detecting a fictitious enterprise based on the
classical method of machine learning, namely Support Vector Machine Classification, will develop a
single software environment that is one of the most promising areas for the rapid detection of economic
crimes.
This article is devoted to this topic, the rest of which is distributed as follows. Section 2 discusses
the analysis of related work; section 3 presents the algorithm for detecting a fictitious enterprise based
MoMLeT+DS 2021: 3rd International Workshop on Modern Machine Learning Technologies and Data Science, June 5, 2021, Lviv-Shatsk,
Ukraine
EMAIL: rektor@wunu.edu.ua (A. Krysovatyy); xrustya.com@gmail.com (H. Lipyanina-Goncharenko); s_sachenko@yahoo.com (S.
Sachenko); o.desyatnyuk@wunu.edu.ua (O. Desyatnyuk)
ORCID: 0000-0002-5850-8224 (A. Krysovatyy); 0000-0002-2441-6292 (H. Lipyanina-Goncharenko); 0000-0001-8225-1820 (S. Sachenko);
0000-0002-1384-4240 (O. Desyatnyuk)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
on Support Vector Machine Classification; in section 4 the implementation of the algorithm itself.
Section 5 presents the conclusions of the study.
2. Related work
Paper [3] forms the basis for the analysis of the interaction of legal and illegal entities: the definition
of the terms “international crime” and “cross-border crime”; separates for analytical purposes
“enterprise crime” from “political crime”. Organized crime focuses on the “model” of the enterprise,
which is really [4] an approach to the study of organized crime, based on the idea that legal and illegal
business are quite similar. Based on the paradigm of organized crime, paper [5] presents the concept of
criminal entrepreneurship, the work developed a research model that links the degree of
entrepreneurship with the stage of organized crime growth. The article [6] has the application of known
theories of organized crime, such as agency theory, alliance theory, network theory, resource-based
theory and other organizational theories to shed light on criminal organizations. Theoretical and applied
aspects of the impact of criminal activity on the economic security of business structures have been
studied [7]; the structural and dynamic tendency of the main threats to the economic security of
enterprises on the basis of the application of integrated assessment depending on partial indicators is
studied; the peculiarities of the multi-vector mechanism of counteracting crime with the help of
economic security of enterprises are substantiated as the base of measures set choosing.
The study [8] considered the measure of corporate tax evasion, which reduces both financial and
taxable income, which is called tax evasion. Simulation analyzes, transformations of US LIFO / FIFO
inventory methods, and samples from private and public firms are used to confirm the results. The
results indicate that the measure of compliance with tax avoidance successfully records transactions
that comply with the accounting tax. As expected, it was found that the degree of participation of state-
owned firms in compliance with tax evasion systematically changes depending on the pressure on the
capital market.
It has been studied [2] as illegal mining, which is very common in Colombia, to overcome the
problem of measuring illegal activity, a new data set has been built using machine learning methods on
the satellite images functions. The study [9] listed opportunities in the virtual environment through ICT,
such as FinTrack software, etc. to prevent financial crime. Face recognition plays a central role in many
security programs [10], which are used to establish a huge system of social credit to control the entire
population.
A generalized structure of a high-performance adaptive system for detecting cyberattacks based on
neural networks and artificial immune systems has been developed [15, 28]. Various data from machine
learning technologies applied to crime data to monitor the impact of the economic crisis on crime in
India have been investigated in [16]. Possibilities of detailed study of data from huge repositories,
analysis of various socio-economic factors related to crime incidents, detection of deviations,
classification of patterns and development of effective computational models for crime prediction using
data analysis and machine learning have been studied [17]. The proposed [18] system is tested for the
problem of predicting crimes using data, and experimental results show that the proposed system
provides better results and search for possible solutions and patterns of crime. The experiment [19-21]
collected data on the crime scenario from the police, which were then simulated on the data set using
machine learning algorithms to predict some attributes.
The researches [11, 23, 24] present a modern, structured and well-organized review of one-class
support classifiers. The study [12] used the methods of vector support machines (SVM) and neural
networks, where the SVM model received the highest efficiency among the classifiers for each data set.
Research [13] provides an understanding of the use of stochastic gradient descent algorithms for large
data applications, for example, to accelerate SVM or controlled regression on a large scale, or to
increase the effectiveness of online learning or real-time forecasting (control). [22, 29] proposed an
approach to machine learning for crime detection and crime location, through Twitter posts and vector-
based filtering to eliminate noise.
It should be noted that the above-mentioned works do not describe the detection of fictitious
enterprises with the help of information technology. Accordingly, the aim of this article is to develop a
method of detecting a fictitious enterprise based on Support Vector Machine Classification, which will
further develop a software environment for public sector employees to prevent economic crimes and
quickly track fictitious enterprises.
There are several close analogues to the purpose of the study [5, 6, 17], which analyze the applied
area, but they do not investigate the use of information technology in this matter and further software
development.
3. Materials and methods
The above-described factors in the study lead to mass shadowing of the Ukrainian economy [30].
Accordingly, there is an urgent need to develop special state programs to combat crime in the economic
sphere (public procurement, use of state budget funds, taxation of economic entities, works, services at
the expense of the state budget, etc.), certain areas of the national economy.
In order to properly organize the activities of law enforcement agencies to detect, promptly verify
and investigate crimes committed using the capabilities of economic entities with signs of fictitiousness,
it is necessary to determine their characteristics and features of the content of criminal activity and types
of such entities. In such case the developed method may be used (Fig. 1.) to identify a fictitious
enterprise on the basis of Support Vector Machine Classification, which will allow to quickly make
decisions about the performance of an individual enterprise.
Request to identify a fictitious enterprise 1
User data entry: ID, 2 Data collection:
Company, Address, 3
EDR, P, PO, K, VKK, L, K205, ZMI, ZD,
FAddress1 Faddressn,
KVEDPIPKER1 TovZ, SP, A, E, F, AR, ZR, KR, LB,
PIPKERn, Foto NM, NS, FR, FC
Comparison of parameters: 4
FR, Address, FAddress1 FAddressn -> Foto = FF,
Internet:
A->SP = A&SP
relevant data
sources
5
Storage base Conversion to binary values6
Adding -> Fit 7
Print: Count, mean, std, and assigning it 0 if the enterprise is
min, 25%, 50%, 75%, fictitious, otherwise 1
max
Output of statistical indicators and 8
Plot: Density visualization of parameters
9 Test sample 10
Division of data into two groups: training
set 80%, test set 20%
Support Vector Machine Classification : 11
-linear approach
- polynomial approach
- radial approach
Print: accuracy
12
Evaluation of each model based
Plot: boxplot алгоритм on a test sample
порівняння
Figure 1: Schematic representation of the fictitious enterprise detection method based on Support
Vector Machine Classification
At the first stage (block 1) the user needs to submit a request to identify a fictitious enterprise.
To build a method based on Support Vector Machine Classification, it is first needed to obtain a set
of data on which the Support Vector Machine Classification algorithm will be formed. In future research
it is planned to develop ready-made software. Accordingly, the required data to be entered (block 2)
directly by the user into the system (Table 1): company code (generated automatically in the system),
legal address, physical address, codes of economic activities, names of managers (maybe several),
photos of equipment with geolocation. All these parameters can be supplemented with new ones and
generalize the existing ones, the algorithm is easily adapted.
Based on the data entered by the user, the analysis of the following parameters (Table 2) (block 3)
and search for appropriate values from sources of information created using the appropriate API and
photo processing method of pattern recognition, which is the purpose of further research.
Table 1.
User-entered data
Parameter Name Data type
ID Company code int64
Company The company name object
Address Legal address object
FAddress1,… FAddressn Physical address object
KVED Code of economic activity object
PIPKER1,… PIPKERn Surnames of managers object
Foto Photo of equipment with geolocation object.jpg
Table 2.
Data collection on open data of Ukraine
Para- Explanation Data
Data source
meter type
ID Company code int64
fit Determining the fictitiousness of the enterprise. bool
EDR Availability of a register of legal entities and bool https://usr.minjust.gov.ua/
individuals in a single database
P Availability of VAT, SSC and a single tax in the bool https://cabinet.sfs.gov.ua/c
database abinet/faces/public/reestr.j
spx
PO Carrying out timely payment of taxes bool https://cabinet.sfs.gov.ua/c
abinet/faces/public/reestr.j
spx
K Availability of settlements with co-agents bool https://youcontrol.com.ua/
landing_002/
VKK Information on the presence of company executives bool https://public.nazk.gov.ua/
in the state register of declarations
L Availability of licenses in accordance with the NACE bool http://irc.gov.ua/ua/Poshu
k-v-YeLR.html
K205 The presence of criminal cases under Art. 205 of the bool http://www.reyestr.court.g
Criminal Code of Ukraine ov.ua/
ZMI Presence of mentions of company executives with bool http://dzmi.informjust.ua/
keywords: criminal case, corruption, offshore
accounts, etc.
ZD Availability of land at the legal or physical address bool http://map.land.gov.ua/kad
astrova-karta
TovZ Availability of registered trademarks and services, bool http://www.uipv.org/ua/ba
database of industrial marks, database of inventions ses2.html
and other databases of the Institute of Industrial
Property of Ukraine
SP Availability of issued motor third party insurance bool https://mail.mtibu.kiev.ua/
policies, MTIBU policy check, motor third party Login.aspx?ReturnUrl=/Cbd
database, search by state car number, check of the /MTSBU_Pages/Tree.aspx
Para- Explanation Data
Data source
meter type
status of the Green Card policy for cars owned by the
company
A The presence of cars and their owners issued to the bool https://igov.org.ua/service/
company. 1397/general
A&SP Coincidence of registered cars with insurance policies bool
E Availability in the database of exporters bool http://ukrexport.gov.ua/rus
/ukr_export_exporters/?co
untry=ukr
F Availability in the stock market database bool http://www.nssmc.gov.ua/f
und/registers
AR The presence of cars and their owners registered for bool http://wanted.mvs.gov.ua/
the company is wanted searchtransport/
ZR The presence of weapons of the owners of the bool http://wanted.mvs.gov.ua/
company is wanted searchorj/
KR The presence of cultural values of the owners of the bool http://wanted.mvs.gov.ua/
company is wanted searchart/
LB Availability of building licenses in the company bool http://dabi.gov.ua/license/l
ist.php
NM The presence of real estate in the company bool https://kap.minjust.gov.ua/
services?keywords=&produ
ct_id=1&usertype
NS Availability of the company's website bool www.google.com
FR Availability of equipment, recognition of equipment bool Photo of equipment with
by the available photo and determination of geolocation
compliance of geolocation with the production
address
FC Availability of the company's social networks and bool Facebook.com
affiliates
Next, a comparison of data (block 4) with each other, namely: FR with Foto, to determine whether
the geolocation coincides with one of the addresses of the company and whether the photo shows the
relevant equipment; SP with A to check whether the registered car companies match the insurance
policies.
Then all the data are transferred to the storage database (block 5) after which the data is converted
into binary values (block 6).
Add the fit parameter (block 7), which shows the value relative to whether the company is fictitious
or not. Of course, machine learning algorithms operate on numerical values, so we assign the
corresponding discrete values 0 or 1.
It is needed always display the basic statistical characteristics of each numerical feature to verify
that all data are displayed correctly. Accordingly, we display (block 8): the number of values, the
average value, the minimal and maximal values. The std line shows the standard deviation (which
measures how scattered the values are). 25%, 50% and 75% of the rows show the corresponding
percentages of values in the corresponding parameter. With the correct visualization of the data, it is
clear trends and patterns, the ratio of variables, which allows very well notice the trends in the figures.
Therefore, graphs of data density are also displayed.
Before data investigation based on machine learning, one of the most important steps should be to
distribute the data (block 9). The data are divided into two groups: training set 80%, test set 20%. The
training kit should be used to build machine learning models. The test kit (block 10) should be used to
see how well the model works on unfamiliar data.
Next, we classify the data using the method of the classical method of machine learning Support
Vector Machine Classification (block 11) [25, 26]. For the purpose of fictitious enterprises selection, it
is necessary to solve the problem of binary classification. Initially, the algorithm is trained on objects
(block 10) from the training sample, for which the class labels are known in advance. Next, the already
learned algorithm predicts the class label for each object from the deferred test sample. Class labels can
take the values 𝑌 = {−1, +1}.Object – is a vector with N signs х= (х1 , х2 , … , х𝑛 ) in the 𝑅n space.
When learning, the algorithm must construct a simple function 𝐹(𝑥) = 𝑦, where the argument 𝑥 is an
object from the 𝑅n space and gives a label of class i. The main goal of Support Vector Machine (SVM)
as a classifier is to find the equation that divides the hyperplane [14] 𝑤1 𝑥1 + 𝑤2 𝑥2 + ⋯ + 𝑤𝑛 𝑥𝑛 +
𝑤0 = 0 in 𝑅n space, which would divide the two classes optimally. General view of the transformation
𝐹 of an object 𝑥 into a label of class 𝑌: 𝐹(𝑥) = 𝑠𝑖𝑔𝑛(𝑤 𝑇𝑥 − 𝑏). And denote 𝑤 =
(𝑤1 , 𝑤2 , … , 𝑤𝑛 ), 𝑏 = −𝑤0. After adjusting the weights of the algorithm 𝑤 and 𝑏 w and b, all objects
that fall on one side of the constructed hyperplane will be defined as the first class, and objects that fall
on the other side – the second class. Inside the function 𝑠𝑖𝑔𝑛() is a linear combination of object features
with algorithm weights, that is why SVM refers to linear algorithms. The hyperplane partition can be
constructed in different ways, but in SVM the weights 𝑤 and 𝑏 are adjusted so that the class objects lie
as far as possible from the hyperplane distribution. In other words, the algorithm maximizes the gap
(Margin) between the hyperplane and the objects of the classes that are closest to it (Fig. 2). Such objects
are called reference vectors. If we consider the positive and negative sides of SVM, the positive thing
is that: SVM works well with a space of features of large size, with data of small volume; SVM
maximizes the separation of the hyperplane and objects, which reduces the number of classification
errors; also, the algorithm is reduced to solving the problem of quadratic programming in the 3D
domain, which makes it possible to divide the hyperplane with certain hyperparameters of the
algorithm. The disadvantages of this method include the fact that: it takes a long time to learn, especially
for large data sets; instability to noise.
Support vector
Figure 2: Classification hyperplane of SVM
Support Vector Machine should be carried out in three main approaches: linear, polynomial and
radial approaches. Also, it is necessary to display the Confusion Matrix. The confusion matrix is
deciphered as follows:
• The target variable has two values: Positive or Negative;
• These columns are the actual values of the target variable;
• These strings represent the predicted values of the target variable.
Next, we evaluate the model (block 12) based on the classical method of machine learning Support
Vector Machine, which can be quite complex. Usually, the model is estimated on the basis of the error
rate. However, this method is not very reliable, because the accuracy obtained for one test set can be
very different from the accuracy obtained for another test set. The key to a fair comparison of machine
learning algorithms is to ensure that each algorithm is evaluated equally on the same data.
4. Experimental results and discussion
Python was chosen as programming language, as this programming language works best with
machine-based data analysis. The following libraries were used for analysis: pandas, numpy,
train_test_split, SVC and cross_val_score.
To build the method, data from 1,100 companies operating in Ukraine were used. The data are
presented in logical binary values (Fig. 3.). There are 355 fictitious enterprises in the set (Fig. 4).
Figure 3: Data structure Figure 4: The number of fictitious enterprises in
the set
The degree of dependence between the indicators with the definition of a fictitious enterprise is
presented in the form of a correlation matrix (Fig. 5). The matrix shows that the fit indicator has a low
correlation with other parameters, so it is difficult to clearly determine the dependence on individual
parameters of whether the company is conducting economic crime or not. Therefore, it is advisable to
use machine learning algorithms that will more accurately determine economic crime. To do this,
modeling using the method of classical machine learning Support Vector Machine.
Figure 5: Correlation matrix
Modeling of the Support Vector Machine was performed by 3 approaches: linear (Fig. 6),
polynomial (Fig. 7) and radial (Fig. 8). Other approaches also exist, but are used less frequent.
We will first consider the linear approach (Fig. 6), the evaluation of the simulation was performed
on a test sample and training. The training sample showed evaluation results at 98.05%, and the test
sample showed evaluation at 97.88%. The standard deviation is 98% for both samples.
An important result is the Confusion Matrix. The different values of the Confusion matrix will be as
follows for the training sample:
• True positive (TP) = 143; this means that 143 indicators of positive class data are correctly
classified by the model;
• True negative (TN) = 612; this means that 612 data points of negative class were correctly
classified by the model;
• False positive (FP) = 15; this means that 15 indicators of negative class data were incorrectly
classified as models belonging to the positive class;
• False negative (FN) = 0; this means that 0 data indicators of the positive class were incorrectly
classified as models belonging to the negative class.
For the test calibration, the confusion matrix shows that 158 and 268 indicators of correctly defined
positive and negative class, respectively, 7 indicators of incorrectly defined negative class and 0
indicators of data of positive class were incorrect.
Figure 6: Simulation Support Vector Machine linear approach
Next, consider the polynomial approach (Fig. 7). The training sample showed evaluation results at
100%, and the test sample showed evaluation at 99.7%. The standard deviation is close to 100% for
both samples. For the training calibration, the confusion matrix shows that 158 and 612 indicators of
correctly defined positive and negative class, respectively, and 0 indicators are incorrectly defined. In
the test sample, the confusion matrix showed that 61 and 268 indicators were defined correctly in the
positive and negative classes, respectively, and 1 indicator was determined incorrectly.
The last approach is radial (Fig. 8), which showed not very good results. The training sample showed
evaluation results at 79.8%, and the test sample showed evaluation at 81.21%. The standard deviation
is close to 80% for both samples. For the training calibration, the confusion matrix shows that 158
indicators are incorrectly defined, 612 indicators are correctly defined and 0 indicators are defined
correctly and incorrectly. In the test sample, the confusion matrix showed that 268 indicators were
defined correctly, but 62 indicators were determined incorrectly.
Figure 7: Modeling Support Vector Machine by polynomial approach
Figure 8: Simulation of Support Vector Machine radial approach
When considering 3 approaches of the Support Vector Machine, the best results are obtained when
classifying by the polynomial approach. The training sample showed evaluation results at 100%, and
the test sample showed evaluation at 99.7%. Also, the confusion matrix showed quite good results.
Thus, returning to the analogues discussed above [5, 6, 17], the developed method makes it possible
to develop a single software environment for public sector employees to prevent economic crimes and
quickly track fictitious enterprises.
5. Conclusions
A method of detecting a fictitious enterprise based on the Support Vector Machine is proposed,
which allows to quickly track fictitious enterprises, which is useful for public sector employees to
prevent economic crimes.
The developed method is implemented on the basis of data of 1100 companies that conducted
economic activity in Ukraine and 355 of which were defined as fictitious. The results of experimental
studies showed that when considering 3 approaches of the Support Vector Machine, the best results
were obtained when classifying the polynomial approach, where the training sample showed evaluation
results at 100%, and the test sample showed evaluation at 99.7%. The confusion matrix for the training
set shows that 158 and 612 indicators of correctly defined positive and negative class, respectively, and
0 indicators are incorrectly defined. In the test sample, the confusion matrix showed that 61 and 268
indicators were defined correctly in the positive and negative classes, respectively, and 1 indicator was
determined incorrectly.
In further scientific research it is planned to conduct a more detailed analysis of the detection of
fictitious enterprises based on the methods: Gaussian Naive Bayes, Logistic Regression.
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