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DOI: https://doi.org/10.34069/AI/2023.67.07.7
How to Cite:
Yatsyna, Y., & Kudinov, I. (2023). Innovative analytical and statistical technology as a corruption counteraction tool: conceptual
analysis. Amazonia Investiga, 12(67), 78-86. https://doi.org/10.34069/AI/2023.67.07.7
Innovative analytical and statistical technology as a corruption
counteraction tool: conceptual analysis
Інноваційні аналітико-статистичні технології як інструмент протидії корупції:
концептуальний аналіз
Received: June 1, 2023 Accepted: July 4, 2023
Written by:
Yuliia Yatsyna1
https://orcid.org/0000-0002-7286-4655
Igor Kudinov2
https://orcid.org/0000-0001-7785-1637
Abstract
The article is devoted to conceptual analysis of
the problem of innovative analytical and
statistical technologies implementations as a
corruption prevention tool. This study defines
corruption as the unlawful use of administrative
resources for personal or group benefits,
violating both formal and informal norms. It is
stated that “corruption counteraction” means
actions to prevent, combat, and mitigate
corruption in society. The paper introduces
several approaches for analytical and statistical
technologies classification with grouping such
technologies into high, middle and low
technologies. Hi-tech is applied to the most
advanced technologies based on scientific and
technical progress and associated with automated
technology. Automated analytical and statistical
technologies are innovative in utilizing machine
learning, deep learning, neural networks, NLP,
network analysis, and real-time data analysis.
The use of such technologies, which
autonomously perform tasks previously reserved
for humans, has shown potential for more
effective corruption counteraction. So,
“innovative analytical and statistical technology”
is defined as a modern collection of methods and
tools for data analysis, designed to identify
complex dependencies and useful patterns in
data, improving decision-making, and detecting
anomalies.
Keywords: analytics, anti-corruption, statistics,
integrity, quality control.
1
Head of CSO “Union of Social Engineers of Ukraine”, Zaporizhzhia, Ukraine. Researcher ID: J-2901-2017
2
Head of CSO “Center for Independent Social Research”, PhD, Associate Professor, Associate Professor of Sociology department,
Zaporizhzhia National University, Zaporizhzhia, Ukraine. Researcher ID: J-2713-2017
Yatsyna, Y., Kudinov, I. / Volume 12 - Issue 67: 78-86 / July, 2023
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Introduction
Corruption is a global issue that affects the
development of individual states as well as the
entire global community as a whole. It creates an
unhealthy environment for economic growth,
undermines trust in state institutions, promotes
illegal activity, and leads to inequality.
Innovative analytical-statistical technologies as a
corruption prevention tool are becoming
especially relevant in today's world, where
technology is gaining more and more
significance. Thanks to the spread of digital
technologies and broad access to big data, there
is the possibility to counteract corruption more
effectively. However, for the successful
implementation and use of these technologies, it
is necessary to clearly understand the basic
concepts related to this issue. It becomes
particularly important considering the
complexity and multifaceted nature of corruption
as a social phenomenon and the technologies
used to prevent it. Without a proper
understanding of how analytical-statistical
technologies work and how they can be
effectively applied for corruption prevention,
there is a risk that they will not be used properly
or will be used with insufficient effectiveness.
Therefore, clarifying the content of the main
concepts related to this problem becomes a
vitally important task. The main goal of this
article is to define key concepts related to
innovative analytical-statistical technologies for
preventing corruption and to clarify their content
in the context of anti-corruption policy. To
achieve the goal the following research questions
are identified: 1) to define the essence of notion
“corruption counteraction tool”; 2) to define the
essence of notion “innovative analytical and
statistical technology”.
Theoretical Framework or Literature Review
The theoretical foundation of this study rests on
two main concepts: corruption counteraction
tools and innovative analytical and statistical
technology. The examination of corruption
counteraction tools requires the exploration of
various legal, economic, and sociological
theories that have emerged in the battle against
corrupt practices. This encompasses laws,
regulations, ethical guidelines, and the general
public stance on corruption. On the other hand,
innovative analytical and statistical technologies
represent the evolving methodologies that
leverage modern data science, artificial
intelligence, and computational algorithms to
understand and address problems. In this
framework, the synergy between these two
realms presents an interdisciplinary approach
that offers a novel perspective on combating
corruption through technological means.
The literature on the topic is vast and
multifaceted, encompassing a range of
disciplines, including law, economics, political
science, and sociology. Scholars such as Rose-
Ackerman and Palifka (2018), Sičáková-Beblavá
& Beblavý (2007) have delved into the structural
and behavioral aspects of corruption, outlining
the legal frameworks and societal norms that are
essential in combating this complex issue.
Concurrently, there has been a burgeoning
interest in the application of innovative analytical
and statistical technologies in various fields.
Research by Rogers (1983) in innovation
essence, Hastie, Tibshirani, and Friedman (2016)
in statistical learning, and developments in big
data analytics by Wu et al. (2014) have set the
stage for utilizing cutting-edge technology in the
analysis and prediction of complex phenomena,
including corruption. The intersection between
these two areas forms the basis of our
investigation, seeking to harness the insights
from both theoretical underpinnings and
empirical findings to create a comprehensive
understanding of how corruption can be
effectively countered through the use of modern
technology.
Methodology
The study is based on the close observation and
analysis of various sources: official
documentation, current legislation, and websites
of public authorities and software producers have
been reviewed meticulously, with an emphasis
on those highlighting the prospects of using their
products as anti-corruption tools. Additionally,
the research incorporates insights from the field
of modern information technologies, aligning
them with the broader sphere of anti-corruption
policy. The methodology includes an in-depth
analysis of innovative analytical and statistical
technologies that are utilized as tools for
corruption monitoring and counteraction.
Sources range from scholarly articles on the
development and application of cutting-edge
software (Rogel-Salazar, 2023) to legislative
documents reflecting current regulations
governing anti-corruption measures
(Kikalishvili, 2021). Furthermore, the empirical
basis for the analysis is drawn from diverse
materials, such as media reports, successful real-
world applications of various information
technologies for automating anti-corruption
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activities (Odilla, 2023), and documents from
international non-governmental organizations
(IDIA, 2019; Paul, Jolley, & Anthony, 2020).
These components together form a robust and
multifaceted foundation, offering a holistic view
of how technology intersects with law and policy
in the ongoing global effort to combat corruption.
In synthesizing these various sources, this study
aspires to provide a comprehensive
understanding of the existing landscape and to
identify new avenues for employing technology
as an innovative and effective tool in the fight
against corruption.
Results and Discussion
Our analysis starts with abstract concepts,
specifically concepts that do not have stable
interpretations and have the same vague essence
as the content of real politics – from the
phenomenon of corruption. There are several
reasons for this.
Firstly, the phenomenon of corruption is a multi-
level and multi-dimensional phenomenon in the
structure of social relations, which can be studied
in various aspects – economic, cultural,
sociological, and, of course, political
(Rose-Ackerman & Palifka, 2018). That is why
there are countless definitions of corruption,
which are used depending on various
methodological approaches, research objectives
and tasks.
Secondly, corruption as a phenomenon is
constantly adapting to changes in political,
economic and social conditions, as well as to
measures of counteraction and mitigation. “It is
hard to give a clear definition of corruption
because it manifests differently, encompasses the
entire social sphere, economy, politics, culture,
morality, law, psychology, power, management
system, etc.” (Nevmerezhytskyi, 2008, p. 44).
It is generally recognized that public power is the
source, nurturing environment and at the same
time the main area of corruption spread in the
state. The concept of “corruption” within the
political sphere implies “bribery” and
“corruption” of officials, and represents one of
the forms of alienation of “public servants” from
the general people (Marych, 2013).
In this regard, S. Zadorozhny draws attention to
the fact that the essence of corruption is revealed
only in the system “human – public power” by
identifying and revealing five clusters of signs of
corruption as 1) type of state-administrative
relations; 2) legal deviation; 3) socio-political
institution; 4) a set of group behavior strategies
and 5) a cultural-psychological phenomenon
(Zadorozhnyi, 2016). Corrupt relations deform
political, economic, social and other orders and
arise, firstly, in the interactions of the private
sector of the economy, citizens and their
associations with public bodies, institutions and
officials of public power, in connection with the
processes of power regulation of various spheres
of public life and the provision of public services.
Corruption in a broad sense is a self-reproducing
system of societal relations that contradicts
societal norms and morals (Sičáková-Beblavá &
Beblavý, 2007). It arises in connection with the
unjust acquisition and/or redistribution of
benefits by an individual vested with official
powers, acting in the interest of persons included
in this system by using the opportunities derived
from these powers (Trepak, 2020, p. 52).
M. Kikalishvili also points to the systemic nature
of corruption as a “complex systemic
phenomenon that impacts all layers of society
and changes the psychological properties of
participants in the societal process” (Kikalishvili,
2021, p. 104).
From a criminological perspective, corruption is
defined as a “complex, deep-rooted, widespread,
systemically dangerous phenomenon, caused by
political, economic, socio-psychological, and
other factors. It involves the unlawful use of
public authority powers and opportunities to
satisfy private interests, as well as instigation
towards or facilitation of such usage. Essentially,
corruption is a peculiar way of converting public
authority powers and opportunities into unlawful
benefits” (Trepak, 2020, p. 53).
O. Lozynskyi provides a classification of
approaches to understanding the essence and
content of corruption – economic, political, legal,
historical, and psychological: “1) as an illegal
mechanism of socio-economic exchange
between representatives of power and business,
which has certain value and economic
expediency for them; 2) as an abuse (excess) of
power, an official position, as an attribute of the
functioning of power (its bureaucratic
institutions) under various forms of political
governance; 3) as an administrative violation,
which entails a fine and temporary suspension
from activity; 4) as a technology for rapid,
unjust, illegal enrichment and strengthening of a
small number of social groups - the oligarchy;
5) as covert unlawful activity of public (political,
official) persons, caused by specific features of
individual psychology and mass psychology”
(Lozynskyi, 2021, pp. 28-33).
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Corruption from the point of view of economics
in the broad sense is defined as a socio-economic
phenomenon, engendered by shadow economic
relations between officials and interested parties
in order to satisfy personal interests through the
commercialization of public goods and values. In
a narrow sense, corruption is defined as the
process of commercialization by officials of their
functional duties (Mazur, 2005, p. 36).
One of the directions of research on the
phenomenon of corruption is the study of
mechanisms for preventing and combating
corruption in the state. This direction includes,
for example, the research of S. Zadorozhnyi. The
author systematized definitions of the corruption
concept in five clusters: “as a special type of
public administration relations; as a legal
deviation; as a socio-political institution, a
certain systemic phenomenon; as a cultural-
psychological phenomenon; as a corresponding
set of strategies for the behavior of various kinds
of social groups seeking to gain illegal
advantages and preferences through the use of
power and official position” (Zadorozhnyi, 2016,
pp. 70-72).
The most widely used definition of corruption
(Transparency International, 2023) is “abuse of
public power for private gain”. This is a very
generalized definition, which allows any actions
by officials aimed at gaining personal benefit to
be considered as corruption. That is, their goal
could be either giving unjustified advantages to
third parties (for example, during tender
procurement), or actions associated with
satisfying, for example, feelings of revenge of a
subordinate towards the boss. The latter example
of official actions is better to classify as a fraud.
Therefore, identifying corruption with fraud is
understandable. According to the provisions of
International Standard on Auditing (ISA) 240,
“fraud is an intentional action by one or more
individuals among management, those charged
with governance, employees, or third parties,
involving the use of deception to obtain an unjust
or illegal advantage” (ISA, 2010). The
international auditing firm
“PricewaterhouseCoopers” interprets fraud as an
“intentional deception with the aim of stealing
money, property, or legitimate rights” (PWC,
2011). The Association of Certified Fraud
Examiners (ACFE) defines fraud in
organizations (or so-called “corporate” fraud) as
“the use of one’s occupation for personal
enrichment through the deliberate misuse or
misapplication of the employing organization’s
resources or assets” (ACFE, 2022, p. 6).
A separate category of fraud is financial resource
fraud. Thus, S. Chornutskyi operates with the
term “fraud in relation to state resources” and
defines it as “intentionally committed violations
of the law (violations committed for the purpose
of obtaining personal benefit or the benefit of
third parties), which led to harm as a result of the
loss of state resources or their non-receipt”
(Chornutskyi, 2011, p. 129).
In general, in our study, we understand
corruption as the unlawful use by an official of
the granted administrative resources for personal
or group benefit, which can have both a material
and immaterial form. Meanwhile, unlawful use
means a violation of both formal normative-legal
institutions, including norms of official behavior
and ethics, and informal norms of behavior,
ethics, and morality.
Continuing our analysis, we move to the phrase
“corruption counteraction”. Modern scientists
also have not yet decided on the semantic
designation of social activity directed against
corruption. We come across such phrases as:
“counteraction to corruption” / “corruption
counteraction”, “prevention of corruption” /
“corruption prevention”, “fight against
corruption”, “corruption mitigation of
corruption” / “corruption mitigation”,
“corruption control” and so on.
According to O. Novikov, corruption
counteraction is an “activity in the sphere of
public administration aimed at reducing
opportunities for the corruption of social
relations” (Novikov, 2020, p. 53). In this case, in
the scientific aspect, counteraction to corruption
has a narrow and broad meaning. In the first case,
it is a system of measures aimed at reducing the
volume of corruption, limiting the influence of
corruption on other social phenomena and
processes, as well as actions to neutralize factors
of corrupt behavior, apply sanctions to subjects
of corruption offenses and eliminate their
consequences. A broad understanding of
counteraction to corruption is interpreted as
lawful activity that helps reduce the opportunities
for such actions, in particular by ensuring the rule
of law, implementing other principles of law,
developing a democratic society, and
establishing a rule of law state (Novikov, 2020,
p. 54).
According to A. Prykhodko’s research,
prevention, counteraction, and fight are three
different directions of anti-corruption activity. If
prevention and fighting manifestations of
corruption in the state involve the combined
activity of all interested parties (state, business,
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civil society) to “identify, study, limit or
eliminate phenomena that generate corruption
offenses or facilitate their spread ... by all
available means of communicative interaction,
the implementation of which is objectified by
preventive, repressive, and elimination
measures”, then counteraction is purely the
activity of “anti-corruption and law enforcement
agencies aimed at detecting corruption offenses,
proper investigation, bringing offenders to
justice, as well as protecting persons who have
been harmed as a result of corrupt actions”
(Prykhodko, 2020, pp. 140-141).
Finally, we consider it necessary to take as an
axiom the statement that corruption as a
phenomenon cannot be destroyed, so it is
impossible to fight or prevent it. However, the
only thing that can be done with it is to counter
its spreading. Therefore, we understand under the
concept of “corruption counteraction” the
activity of actors / subjects of anti-corruption
activity in terms of preventing corruption
manifestations (detecting and eliminating the
causes of the spread of corruption crimes);
fighting against corrupt acts (their termination,
exposure, and direct investigation) and
minimizing and eliminating the consequences of
committed corruption offenses (Okuniev, Boiko,
& Lukin, 2018).
As for the phrase “corruption counteraction
tool”, it is appropriate here to quote
M. Kikalishvili, who defines the similar term
“measures against corruption crime” as “a
complex of actions and / or means by which a
complex and multi-aspect activity is
implemented, which combines elements of social
management with private and public initiatives
and is aimed at creating obstacles to the
commission of corrupt acts, resisting their
spread, as well as an appropriate response to
those acts that have already manifested in actual
committed offenses”(Kikalishvili, 2021,
pp. 22-23). So, “corruption counteraction tool” in
our research is understood as the means to create
obstacles to the commission of corrupt acts, resist
their spread, and also respond to those actions
that have already manifested in actual committed
offenses. In a such context corruption
counteraction tool can be associated with a
quality control tool.
From abstract concepts and phenomena, we
move to the world where uncertainty always gets
its definition – the world of mathematics and
information science, namely to clarify the
concept of “innovative analytical and statistical
technology”. The term “analytical and statistical
technology” can have different interpretations,
depending on the context in which it is used.
However, in a general sense, it is a technology
based on the application of data analytics
methods and statistical analysis to solve various
tasks in different fields.
For example, E. Rogers understood technology is
as “a project of instrumental action that reduces
the uncertainty of causal relationships on the way
to achieving the desired result. Technology
usually consists of two components: 1) the
hardware part, that is, the device that embodies
the technology as a physical or material object,
and 2) the informational part (software), that is,
the information base of this device” (Rogers,
1983, pp. 13-14). Thus, we see that technology
can be considered as a tool, the nature of the use
of which is determined by a pre-determined goal.
In our study, we understand “technology” as a
documented mechanism or method of applying
certain physical or material objects, the operation
of which is pre-determined by a set of
instructions.
Next concept is statistics and analytics. At the
current stage of society development, the term
“statistics” is used in two senses. Firstly, in
everyday life, it is understood as a set of
quantitative data about a certain phenomenon or
process. Secondly, experts in the field of
statistical methods call “statistics” a function of
observation results used to estimate
characteristics and distribution parameters and
hypothesis testing (Rogel-Salazar, 2023,
pp. 14-24).
It should be noted that the application of
statistical methods in complex systems is
impossible without the use of laws of thought,
more precisely analytical methods – analytics.
Analytics appears as a discipline that combines
three most important components: the
methodology of information-analytical work, the
organizational provision of this process, and the
technology-methodological support for the
development and creation of instrumental means
for its implementation.
So, analytics is the basis for intellectual, logical
and thinking activity aimed at solving practical
tasks, allowing the actor / subject of cognition to
predict the future state of the object of analysis.
It plays an integrative role in reconstructing the
past, revealing the present, and forecasting the
future. Overall, by analytics, we understand “the
set of principles of methodological,
organizational, and technological support for
individual and collective thinking activities that
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allow effective processing of information to
improve the quality of existing and new
knowledge, as well as preparation of an
information base for making optimal
management decisions” (Zakharova & Filipova,
2013, pp. 26-27).
Analytics acts as a comprehensive discipline,
combining methodological approaches from
various scientific directions aimed at mining,
proper presentation, and management of
knowledge. It integrates the results obtained in
various scientific fields: from mathematics to
synoptics and meteorology. Also, analytics
includes some working methods from
psychology and psychoanalysis, social and
political science, history, source studies, library
science, linguistics, pedagogy, forensics,
jurisprudence, and many others. Almost a
complete complex of sciences that have ever
attempted a scientific invariant description of the
features of the behavior of an individual or a
group in different situations: during group and
individual activities, during expressing thoughts,
synthesizing goals and choosing methods of their
achievements, and other situations.
Therefore, in the general sense, by analytical and
statistical technology we understand a
documented procedure or algorithm for data
analysis. Any analytical and statistical data
analysis usually includes a whole range of
procedures and algorithms that are performed
sequentially, in parallel, or according to a more
complex scheme. It is important to emphasize
that skilled and effective application of analytical
and statistical analysis is by no means checking
one separately taken statistical hypothesis or
evaluating the characteristics or parameters of
one given distribution from a fixed family.
Operations of this kind are just a separate brick
from which analytical and statistical technology
is composed.
Let's turn to such analytical and statistical
technologies which are called “innovative”.
Quality expert Kaeru Ishikawa divides analytical
and statistical methods into three groups:
elementary, intermediate, and advanced.
Elementary methods include such simple tools
as: a control sheet, a quality histogram, a cause-
and-effect diagram, a Pareto chart, stratification,
a scatter diagram, a control card. Intermediate
methods are methods of acceptance control,
distribution theory, statistical estimates, and
criteria. Advanced methods are methods based
on the use of computer technologies:
experimental design, multidimensional analysis,
operations research methods (Ishikawa, 1989).
We are interested in advanced methods, as they
have a direct relation to innovation. In modern
literature, there are many definitions of
innovation. The simplest definition of innovation
as an idea, practice or object that “is perceived as
new by the individual or other implementer”
(Rogers, 1983, p. 11).
In modern research, two approaches to defining
the innovation concept are common:
1) static, where innovation acts as a “product-
innovation”, when it is presented as the
result of an innovative process in the form of
a new technique (product), technology, a
new method introduced to the market;
2) dynamic, where innovation acts as a
“process-innovation” of research, design,
development, production organization,
commercialization and distribution of new
products, technologies, principles instead of
the existing ones (Huturov, 2019, p. 16).
It should be noted that in modern (especially
English) scientific and technical literature in
relation to innovative technologies, the term “hi-
tech” is also used. The term “high technology” is
used to denote the most advanced technologies
that rely on the latest achievements of scientific
and technical progress. There are such
technologies among the technologies of
analytical and statistical data analysis – like in
any scientific-practical field that is intensively
developing.
“High”, as in other areas, means that analytical
and statistical technology is based on modern
achievements of analytical and statistical theories
and practices, in particular, on achievements in
the theory of probability, applied mathematical
statistics. At the same time, “based on modern
achievements” means, firstly, that the
mathematical basis of technology has been
obtained relatively recently within the
framework of the relevant scientific discipline,
and secondly, that calculation algorithms have
been developed and justified according to it (i.e.,
they are not obtained heuristically).
Subsequently, new approaches and results may
force a reassessment of the applicability and
capabilities of technology, lead to its replacement
with a more modern one. Otherwise, “high
analytical and statistical technologies” turn into
“classical” technologies. Thus, high analytical
and statistical technologies are the results of
recent serious scientific research.
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High analytical and statistical technologies are
contrasted, accordingly, with low analytical and
statistical technologies (with classical ones
situated between them). “Low analytical and
statistical technologies” are those technologies
that do not correspond to the modern level of
science and practice. Usually, they are
simultaneously outdated and not entirely
adequate to the tasks being solved.
Classical analytical and statistical technologies
are understood as long-standing technologies that
have retained their importance for modern
analytical and statistical practice. Such
technologies based on the method of least
squares (including methods of point estimation
of parameters of the predictive function, non-
parametric methods of confidence estimation of
parameters and the predictive function as a
whole, tests of various hypotheses about them),
Kolmogorov, Smirnov, omega-square type
statistics, non-parametric Spearman and Kendall
correlation coefficients (to attribute them only to
ranking analysis methods means to condescend
to “low analytical and statistical technologies”)
and many other statistical procedures.
There is another approach (Köbis, Starke, &
Rahwan, 2022; Kovtun, 2011; Odilla, 2023) to
the classification of analytical and statistical
technologies, according to which they can be
divided into traditional (classical) and
automated. The classical one involves the
activities of 1-2 experts with a minor application
of computer (information) technologies, during
which analysis of interrelations,
interdependencies of various indicators is carried
out to identify deviations from the norm (this
includes stereotype methods, adjusted indicators,
associated comparisons, and so on).
In turn, automated group assumes, firstly, the
involvement of intelligent systems in the data
processing, which are trained to perform
analytical and statistical operations with datasets
and capable of replacing a person in most
operations performed. These technologies
include data mining, anomaly detection in them,
or new knowledge discovery (novelty detection,
knowledge discovery). The use of these
algorithms allows the automation of the work of
several specialists and simplifies the process of
preparing reports about exceptional (new,
anomalous) situations. Based on this
classification, automated technologies should be
considered as innovative analytical and statistical
technologies. We can add that the use of the
indicated technologies in modern conditions of
societal development is impossible without the
use also of such technologies as machine
learning, deep learning, neural networks, natural
language processing (NLP), network analysis,
real-time data analysis, which allow obtaining
fast and accurate results of analysis of large
volumes of data.
As noted by (Köbis, Starke, & Rahwan, 2021),
the active implementation of artificial
intelligence and machine learning technologies
brings new hope for more effective corruption
counteraction. Artificial intelligence differs
significantly from static information and
communication technologies. “Classic”
technologies allow to digitize the procurement
procedures, the provision of government services
online, and the publication of open government
data. However, traditional technologies cannot
operate autonomously, while artificial
intelligence, on the contrary, was specifically
designed for this. Thanks to its learning
capabilities, artificial intelligence can
autonomously perform a wide range of tasks
previously reserved for humans (Rahwan et al.,
2019). In the context of today's digital
transformation of states, artificial intelligence
can take on anti-corruption tasks, such as
predicting, detecting, and exposing corruption
cases (Lima & Delen, 2020; López-Iturriaga &
Sanz, 2017).
Thus, in practical use, applied statistical methods
and analytics involve not just separate data
description methods, estimation, hypothesis
testing, but complete, integrated procedures – so-
called “analytical and statistical technologies”.
The concept of “analytical and statistical
technology” in our understanding is analogous to
the concept of “technological process” in the
theory and practice of production organization
(The Ukranian Week, 2022).
Naturally, some statistical technologies better
meet the needs of the researcher (user,
statistician), others worse; some are modern,
others outdated; the properties of some are
studied, others not. It should be noted that
competent and effective application of analytical
and statistical methods is by no means just a
check of one separately taken statistical
hypothesis or an assessment of characteristics or
parameters of one given distribution from a fixed
family. Such operations are only a separate brick
that makes up the analytical and statistical
technology. The procedure of analytical and
statistical data analysis is an information
technology process, i.e., a certain information
technology in which statistical information
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undergoes various operations (sequentially, in
parallel or according to more complex schemes).
Conclusions
Upon concluding the conceptual analysis of the
deployment of innovative analytical and
statistical technology in anti-corruption
activities, two foundational concepts have been
delineated.
The first concept, referred to as the “corruption
counteraction tool”, is characterized as a
mechanism designed to impede corrupt acts,
resist their proliferation, and respond to those
actions that have culminated in actual committed
offenses. Within this framework, the corruption
counteraction tool can be likened to a quality
control instrument, serving to maintain integrity
and hinder corrupt practices.
The second term, “innovative analytical and
statistical technology”, is indicative of a broad
spectrum of methods and tools. This encapsulates
the utilization of mathematical and statistical
data analysis techniques with the objectives of
unveiling valuable relationships and patterns
within data, enhancing decision-making
efficiency, and identifying irregularities across
various domains. More specifically, this term
embodies the application of sophisticated,
contemporary data analysis methods such as
machine learning, deep learning, neural
networks, natural language processing, and graph
analysis. The goal here is to discern intricate
relationships and useful patterns in data sets.
Included within this definition are real-time data
analysis techniques, enabling prompt and precise
analytical outcomes for substantial data volumes.
When fused with quality control tools, analytical
and statistical technology foster standardized
rules and algorithms for data evaluation in
contexts that may be overwhelmingly complex
for human cognition, such as handling immense
quantities of data and iterations. This synthesis
brings forth a robust approach to monitoring and
mitigating corruption, leveraging state-of-the-art
technology to navigate the multifaceted
landscape of integrity and governance.
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