Method of Constructing the Fuzzy Regression Model of Bank Competitiveness

Malyaretz, Liudmyla; Dorokhov, Oleksandr; Dorokhova, Liudmyla

doi:10.2478/jcbtp-2018-0016

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…In order to effectively detect fraud, in addition to the information available in the databases of the bank or payment organization, it is necessary to add to the data external information (for example, a posteriori assessment of various risks associated with the transaction) [26,27].…”

Section: Features Of Building An Information Base To Detect Fraudumentioning

confidence: 99%

Automatic machine learning algorithms for fraud detection in digital payment systems

Kolodiziev

Mints

Sidelov

et al. 2020

EEJET

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Data on global financial statistics demonstrate that total losses from fraudulent transactions around the world are constantly growing. The issue of payment fraud will be exacerbated by the digitalization of economic relations, in particular the introduction by banks of the concept of "Bank-as-a-Service", which will increase the burden on payment services. The aim of this study is to synthesize effective models for detecting fraud in digital payment systems using automated machine learning and Big Data analysis algorithms. Approaches to expanding the information base to detect fraudulent transactions have been proposed and systematized. The choice of performance metrics for building and comparing models has been substantiated. The use of automatic machine learning algorithms has been proposed to resolve the issue, which makes it possible in a short time to go through a large number of variants of models, their ensembles, and input data sets. As a result, our experiments allowed us to obtain the quality of classification based on the AUC metric at the level of 0.977-0.982. This exceeds the effectiveness of the classifiers developed by traditional methods, even as the time spent on the synthesis of the models is much less and measured in hours. The models' ensemble has made it possible to detect up to 85.7 % of fraudulent transactions in the sample. The accuracy of fraud detection is also high (79-85 %). The results of our study confirm the effectiveness of using automatic machine learning algorithms to synthesize fraud detection models in digital payment systems. In this case, efficiency is manifested not only by the resulting classifiers' quality but also by the reduction in the cost of their development, as well as by the high potential of interpretability. Implementing the study results could enable financial institutions to reduce the financial and temporal costs of developing and updating active systems against payment fraud, as well as improve the effectiveness of monitoring financial transactions

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Section: Features Of Building An Information Base To Detect Fraudumentioning

confidence: 99%

Automatic machine learning algorithms for fraud detection in digital payment systems

Kolodiziev

Mints

Sidelov

et al. 2020

EEJET

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“…Indicators , singled out on the basis of theoretical and logical analysis to N groups. So, all individual indicators that describe the bank competitiveness are structured to groups (Malyaretz et al, 2018). Specifically, competitiveness of banking products described by indicators:…”

Section: Description and Formalization Of Components Of Bank Competitmentioning

confidence: 99%

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

Malyarets

Dorokhov

Koibichuk

et al. 2019

Journal of Central Banking Theory and Practice

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The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks techniques. Uncertainties that have a place when considering and analyzing the components of evaluating the success and effectiveness of the bank have been considered and analyzed. The sequence of construction and structure for generalizing parameter of bank competitiveness are presented and grounded. Stages of obtaining an integrated assessment of bank competitiveness by sequential application of fuzzy logic and neural networks approaches are determined and described. Corresponding fuzzy terms, membership functions and fuzzy inference rules are described. Overall sequence and steps to resolve the problem are processed. The practical implementation of the summary fuzzy inference of the bank’s competitiveness is given. In particular, numerical calculations on the proposed model for Ukrainian commercial bank “Khreshchatyk” was carried out. Comparison of obtained evaluation results for the competitiveness of specified bank with available data and other scientific information sources showed their compliance with factual situation. In this way, the expediency of application fuzzy modeling has been confirmed to determine the generalized indicators of bank competitiveness. Adequacy and accuracy of the proposed model and the results of calculations were proved. The proposed approach is quite general. This or similar model can be successfully used in other tasks of building and generalized evaluation of integrated indicators for the presence of several local, individual parameters for different economic processes and tasks.

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“…Since this value is between control limits, the first sample is in control. Also the condition of process control for each sample is defined by using the following: [10][11]…”

Section: Introductionmentioning

confidence: 99%

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

Rashid

Haydar

2022

ACAD J NAWROZ UNIV

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Abstract: In many real-world applications, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties, In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justiﬁed. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. As well as The fuzzy regression control chart which is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this paper, the application of fuzzy logic in statistical quality control have been done by plotting fuzzy EWMA chart and Fuzzy linear regression model control chart depending on a suggested algorithm prepared for this purpose, as well as the theoretical structure of the “a-level fuzzy midrange for a-cut fuzzy -regression control chart” is proposed for triangular membership functions and applying that to the chemical analysis data of a water component, Total Dissolved Solid ( TDS) in water from the KANY Factory to detect small shifts in the process means, the data contains three groups (TDS-a, TDS-b, TDS-c), for 24 days each day containing 5 hour data, as shown in Table (1) The comparison showed that the fuzzy linear regression model Control chart is a good technique and more suitable, accuracy, sensitivity than the traditional linear regression model Control chart.

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Method of Constructing the Fuzzy Regression Model of Bank Competitiveness

Cited by 13 publications

References 11 publications

Automatic machine learning algorithms for fraud detection in digital payment systems

Automatic machine learning algorithms for fraud detection in digital payment systems

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

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