AI and Machine Learning In Fraud Detection : Securing Digital Payments and Economic Stability DOI Open Access

Prakash Raju Kantheti,

Prof. Stella Bvuma

International Journal of Scientific Research in Science and Technology, Journal Year: 2024, Volume and Issue: 11(3), P. 974 - 982

Published: June 16, 2024

AI and Machine Learning in Fraud Detection play a critical role securing digital payments ensuring economic stability. As payment fraud escalates, costing billions globally, traditional models struggle to address increasingly sophisticated tactics such as phishing, account takeovers, salami slicing. AI/ML-driven solutions, including graph-based anomaly detection, hybrid (deep learning + knowledge-based systems), ensemble methods, provide enhanced detection capabilities. These systems adapt evolving threats, detect patterns, minimize false positives/negatives while maintaining transaction integrity. Emerging challenges include fraudsters exploiting agents, adversarial learning, bottlenecks systems. Metrics like accuracy, precision, ROI validate the effectiveness of AI/ML combating fraud. Ethical considerations regulatory compliance remain crucial standardize deployment globally. Future research must focus on scalability, adaptability, resilience counter advanced schemes.

Language: Английский

The evolution of internal audit in anti-corruption activities: leveraging data analytics and it technology DOI

Yves Genest

EDPACS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 7

Published: Jan. 30, 2025

The article explores the transformative role of internal audit in anti-corruption efforts, emphasizing how technological advancements, particularly data analytics and IT tools, have redefined traditional practices. Advanced enables comprehensive transaction reviews, detecting anomalies forecasting risks. Machine learning algorithms refine corruption detection by adapting to historical data, while network analysis tools uncover hidden connections within organizations. Practical applications such as real-time monitoring, behavioral analytics, integrated risk management bolstered strategies. However, successful implementation these technologies requires robust governance, skilled personnel, ethical considerations regarding privacy. underscores that technology enhances, rather than replaces, critical human auditors interpreting complex insights making decisions. Looking ahead, emerging like blockchain predictive modeling promise further advance mechanisms, ensuring a proactive effective approach.

Language: Английский

Citations

0
Green rules & grey markets: Do environmental policies influence the informal economy? DOI Creative Commons
Serhiy Lyeonov, Alla Moroz,

Iwona Dudziuk

et al.

Economics & Sociology, Journal Year: 2025, Volume and Issue: 18(1), P. 313 - 338

Published: March 1, 2025

The relationship between environmental policy stringency and the shadow economy is a critical issue, as stringent regulations can either formalise economic activities or push businesses into informality. This study aims to analyse how different types of policies influenced size across 24 countries from 2003 2020. uses panel data regression techniques, including Fixed Effects Random models, evaluate impact market-based policies, command-and-control regulations, taxation on informal activities. results indicate that overall negatively correlated with economy, one-unit increase in reducing by approximately 2.18 percentage points. Market-based such carbon trading schemes financial incentives, are more effective informality than regulations. However, high taxation, particularly sulphur oxide taxes, associated an suggesting excessive regulatory costs may incentivise tax evasion operations. highlights importance balancing incentives governance reforms ensure both sustainability de-shadowing.

Language: Английский

Citations

0
Cognitive mapping of the economy of trust DOI Creative Commons
Serhiy Lyeonov, Maryna Brychko, Jarosław Korpysa

et al.

Economics & Sociology, Journal Year: 2024, Volume and Issue: 17(3), P. 237 - 266

Published: Sept. 1, 2024

The concept of trust has been extensively explored by governments, researchers, and academic communities focusing on public authorities the financial system, albeit in separate contexts. Trust plays a vital role both sectors, influencing various aspects governance, economic stability, societal well-being. However, relationship interdependencies between government system remain relatively unexplored. In addressing this gap, study aims to improve understanding socio-economic provide framework for analysing complex causal mechanisms developments sectors using concepts. To achieve this, adopts Fuzzy Cognitive Mapping (FCM) method combination with fuzzy Delphi (FDM) as methodological approach. results highlight that even small decline can have severe repercussions stability deposit levels, exchange rate prevalence non-performing loans. Additionally, violations sector also impact development sector, resulting decreased government, fiscal tax revenues, bond purchases. demonstrated when is eroded simultaneously, complexities extent negative consequences are amplified. These findings emphasize interconnected nature dynamics underscore importance comprehensive approach trust-related challenges.

Language: Английский

Citations

0
Assessing the foreign economic security of Ukraine DOI Creative Commons
Yuliіa Yehorova, Svitlana Chorna, Yuriy Petrushenko

et al.

Problems and Perspectives in Management, Journal Year: 2024, Volume and Issue: 22(4), P. 382 - 396

Published: Nov. 27, 2024

The study aims to assess the state of Ukraine’s foreign economic security and challenges associated with its ensuring. integrated assessment methodology Ministry Economy Ukraine was employed, which is based on a quantitative analysis indicators that reflect security. It involves characteristics each indicator in terms stimulators or destimulators, their normalization, consideration weighting coefficients. In order identify long-term trends, official national accounts statistics, data from World Bank, Economic Development Observatory for period 2004–2023 were employed; ten indicators. results demonstrate main factors affecting index are global crises, domestic political changes, full-scale war russia. At same time, growth recorded stabilization during implementation structural reforms: 2005–2008 – after Orange Revolution, 2014–2016 Revolution Dignity, 2021 post-pandemic recovery. 2022–2023, declined 31.5% 35.7%, respectively, as consequence outbreak russian-Ukrainian war. findings also emphasize need develop capacities ensure sustainability activity, well importance maintaining planning export infrastructure face challenges. AcknowledgmentThis article published an output project VEGA 1/0392/23: “Changes approach development distribution management concepts companies influenced by impact social crises caused pandemic increased risks” funded EU NextGenerationEU through Recovery Resilience Plan Slovakia under No.09103-03-V01-00042.This financially supported NATO SPS Program “Security territorial communities: evidence Eastern European countries”.In addition, this “Economic bases managing debt martial law” (No. 0121U112685).

Language: Английский

Citations

0
AI and Machine Learning In Fraud Detection : Securing Digital Payments and Economic Stability DOI Open Access

Prakash Raju Kantheti,

Prof. Stella Bvuma

International Journal of Scientific Research in Science and Technology, Journal Year: 2024, Volume and Issue: 11(3), P. 974 - 982

Published: June 16, 2024

AI and Machine Learning in Fraud Detection play a critical role securing digital payments ensuring economic stability. As payment fraud escalates, costing billions globally, traditional models struggle to address increasingly sophisticated tactics such as phishing, account takeovers, salami slicing. AI/ML-driven solutions, including graph-based anomaly detection, hybrid (deep learning + knowledge-based systems), ensemble methods, provide enhanced detection capabilities. These systems adapt evolving threats, detect patterns, minimize false positives/negatives while maintaining transaction integrity. Emerging challenges include fraudsters exploiting agents, adversarial learning, bottlenecks systems. Metrics like accuracy, precision, ROI validate the effectiveness of AI/ML combating fraud. Ethical considerations regulatory compliance remain crucial standardize deployment globally. Future research must focus on scalability, adaptability, resilience counter advanced schemes.

Language: Английский

Citations

0