Search results

1

Some remarks about benford’s distribution

100%

Baryła M.

Mathematical Economics

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2012

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issue 8 (15)

5-15

EN

The aim of the following article is to present some facts about Benford’s distribution. Its main focus is on selected descriptors of this distribution (such as mean, variance and skewness) and its two major properties, i.e. base invariance and scale invariance. At the end of the paper some applications of Benford’s distribution are presented.

2

From business to clinical trials: a systematic review of the literature on fraud detection methods to be used in central statistical monitoring

88%

Fronc M., Jakubczyk M.

Przegląd Statystyczny

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2022

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vol. 69

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issue 3

1-22

EN

Data-driven decisions can be suboptimal when the data are distorted by fraudulent behaviour. Fraud is a common occurrence in finance or other related industries, where large datasets are handled and motivation for financial gain may be high. In order to detect and the prevent fraud, quantitative methods are used. Fraud, however, is also committed in other circumstances, e.g. during clinical trials. The article aims to verify which analytical fraud-detection methods used in finance may be adopted in the field of clinical trials. We systematically reviewed papers published over the last five years in two databases (Scopus and the Web of Science) in the field of economics, finance, management and business in general. We considered a broad scope of data mining techniques including artificial intelligence algorithms. As a result, 37 quantitative methods were identified with the potential of being fit for application in clinical trials. The methods were grouped into three categories: pre-processing techniques, supervised learning and unsupervised learning. Our findings may enhance the future use of fraud-detection methods in clinical trials.

3

Credit card fraud detection and risk management strategies: A deep learning-based approach for EU banks

88%

Zouaoui H., Naas M.-N.

Research Papers in Economics and Finance

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2025

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vol. 9

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issue 1

55-80

EN

This study explores supervised ML-DL based approaches for enhancing credit card fraud detection and improving financial risk management systems for EU banks. This research proposes an ensemble method based on majority voting (Hard Voting Classifier) of deep learning models to detect fraud transaction. Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have been used as deep learning models. First, the most significant features that affect the type of transaction (fraud or not fraud) have been selected. After that, the ML-DL models were applied. The performance of the proposed approach is tested using a confusion matrix, recall, precision, F-measure and accuracy. The proposed method is tested using accurate data that consists of 540,099 transactions recorded in Kaggle repository dataset of two days based on European card holder for September, 2023. The result shows that the Random Forest (RF) model detected anomalies with 99.99% accuracy, F1-score with 1.00, and excellent recall with 99.99%. As a result, the machine learning model based on RF approach shows promise as a real-time anomaly detection method with high performance and low computational cost.

4

Feature Engineering for Anti-Fraud Models Based on Anomaly Detection

75%

Przekop D.

Central European Journal of Economic Modelling and Econometrics

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2020

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issue 3

5

Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management

75%

Andronie M., Blažek R., Iatagan M., Skypalova R., Uță C., Dijmărescu A., Kovacova M., Grecu G., Pârvu I., Strakova J., Guni C., Zabojnik S., Chiru C., Sedláčková A. N., Novák A., Dijmărescu I.

Oeconomia Copernicana

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2024

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vol. 15

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issue 4

1349-1381

EN

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

6

Decision Tree Approach to Discovering Fraud in Leasing Agreements

63%

Horvat I., Pejić Bach M., Merkač Skok M.

Business Systems Research Journal

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2014

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vol. 5

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issue 2

61-71

EN

Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.

7

Wykorzystanie audytu śledczego w detekcji przestępstw socjalnych

63%

Goleński W., Koczur W.

Ubezpieczenia Społeczne. Teoria i Praktyka

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2026

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vol. 168

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issue 1

67-87

EN

Introduction: The article addresses the issue of social crime and the importance of forensic auditing as a tool for identifying and preventing financial misconduct in the public sector. Social crimes are defined as intentional actions aimed at obtaining undue benefits from the social security system, carried out mainly through document forgery, false statements, or the exploitation of loopholes in administrative procedures. The scale of this phenomenon generates significant economic and social losses, while scholarly recognition of the issue in Poland remains limited. Objective: The aim of this article is to present forensic auditing as an effective method of counteracting social crimes. Materials and Methods: To achieve this objective, literature studies and legal analysis – specifically the dogmatic and legal method – were employed. Results: The study demonstrates that forensic auditing, as an interdisciplinary form of control combining elements of accounting, law, and forensic science, constitutes an effective and efficient tool for detecting and analyzing major irregularities. Its essence lies in identifying so-called “red flags” – indicators that may signal fraud – and using analytical methods and modern technologies in the fraud detection process. The author also emphasizes the alignment of audit practices with OECD recommendations on preventing financial fraud. Forensic auditing can play a significant role in reducing social crime, strengthening the transparency of public finances, and building citizens’ trust in state institutions.

PL

Wprowadzenie: Artykuł podejmuje problematykę przestępczości socjalnej oraz znaczenia audytu śledczego jako narzędzia identyfikacji i prewencji nadużyć finansowych w sektorze publicznym. Przestępstwa socjalne definiowane są jako umyślne działania prowadzące do uzyskania nienależnych świadczeń z systemu zabezpieczenia społecznego, realizowane głównie poprzez fałszowanie dokumentów, składanie nieprawdziwych oświadczeń lub wykorzystanie luk w procedurach administracyjnych. Skala zjawiska generuje znaczne straty ekonomiczne i społeczne, a jego naukowe rozpoznanie w Polsce pozostaje ograniczone. Cel: Celem niniejszego artykułu jest przedstawienie audytu śledczego jako skutecznego sposobu przeciwdziałania przestępstwom socjalnym. Materiały i metody: Do realizacji ww. celu wykorzystano studia literaturowe oraz analizę przepisów prawnych – metodę dogmatycznoprawną. Wyniki: W pracy wykazano, że audyt śledczy, jako interdyscyplinarna forma kontroli łącząca elementy rachunkowości, prawa i kryminalistyki, stanowi skuteczne i efektywne narzędzie wykrywania oraz analizy najpoważniejszych nieprawidłowości. Jego istotą są identyfikacja „czerwonych flag” – symptomów mogących wskazywać na nadużycia – oraz wykorzystanie metod analitycznych i nowoczesnych technologii w procesie detekcji fraudów. Autor akcentuje również zgodność działań audytowych z rekomendacjami OECD w zakresie prewencji oszustw finansowych. Audyt śledczy może odgrywać znaczącą rolę w ograniczaniu przestępczości socjalnej, wzmacnianiu przejrzystości finansów publicznych oraz budowaniu zaufania obywateli do instytucji państwowych.

8

Zwalczanie nadużyć finansowych w funduszach UE : Narzędzia stosowane do ochrony budżetu Unii, cz. II

51%

Szymański M.

Kontrola Państwowa

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2021

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vol. 66

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issue 4 (399)

27-61

EN

In the next years, Member States will be spending funds from the EU’s largest package ever. It comprises money from the multi-annual financial perspective (2021–2027) and the reconstruction plan – designed to mitigate the crisis and provide the basis for developing modern and sustainable Europe. It will be a great challenge to eliminate fraud in the funds spending. However, it seems feasible to reduce fraud because over the last years EU institutions have made fraud combating their priority. The second part of the article related to the protection of EU financial interests focuses on mea sures and good practices examples that may significantly contribute to counteracting corruption, understood in a broad sense. It describes tools that are applied mainly by the institutions responsible for EU funds implementation aimed at identifying fraud, including a mechanism for anonymous reporting on irregularities (in the context of the Whistleblower Directive). The author discusses potential instruments developed in response to the most frequent irregularities areas, i.e. breaches in public procurement, conflicts of interests, or collusive tendering. An innovative instrument, designated for social control of public funds spending – the so called integrity pact – has been dis cussed in more detail. The author also presents IT tools to increase public spending transparency and to identify suspicious transactions.

PL

Druga część artykułu na temat ochrony interesów finansowych UE przed nadużyciami prezentuje działania, których wdrożenie w ramach systemu kontroli zarządczej może wspomagać przeciwdziałanie szeroko rozumianej korupcji. Wskazuje też przykłady praktyk niektórych urzędów. Odnoszą się one do obszarów największego ryzyka i dotyczą zarówno zapobiegania, jak i wykrywania nadużyć finansowych. Artykuł opisuje narzędzia stosowane głównie przez instytucje odpowiedzialne za wdrażanie środków unijnych, w tym mechanizm służący anonimowemu sygnalizowaniu nieprawidłowości (w kontekście dyrektywy poświęconej ochronie sygnalistów). W tej części przedstawiono możliwe do zastosowania rozwiązania, będące odpowiedzią na najczęściej występujące nieprawidłowości, przede wszystkim naruszenia w obszarze zamówień publicznych, konflikt interesów czy zmowy przetargowe. Przybliżono także tzw. pakt uczciwości, innowacyjny instrument kontroli społecznej wydatkowania środków publicznych. Opisano stosowane narzędzia informatyczne zwiększające przejrzystość wydatków publicznych, a także ułatwiające identyfikację podejrzanych transakcji.

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