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on Computational Economics |
| By: | World Bank |
| Keywords: | Information and Communication Technologies-Information Technology |
| Date: | 2023–07 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:39959 |
| By: | Jeffrey Allen |
| Abstract: | Payment fraud has been high in recent years, and as criminals gain access to capability-enhancing generative AI tools, there is a growing need for innovative fraud detection research. However, the pace, diversity, and reproducibility of such research are inhibited by the dearth of publicly available payment transaction data. A few payment simulation methodologies have been developed to help narrow the payment transaction data gap without compromising important data privacy and security expectations. While these simulation approaches have enabled research advancements, more work is needed to generate datasets that reflect diverse and evolving fraud tactics. This paper introduces CardSim, a flexible, scalable payment card transaction simulation methodology that extends the small but emerging body of simulators available for payment fraud modeling research. CardSim is novel in the extent to which it is calibrated to publicly available data and in its Bayesian approach to associating payment transaction features with fraud. The simulator’s modular structure, which is operationalized in a corresponding software package, makes it easy to update based on new evidence about payment trends or fraud patterns. After laying out the simulation methodology, I show how outputs can be used to test and evaluate machine learning workflows, modeling approaches, and interpretability frameworks that are relevant for payment card fraud detection. |
| Keywords: | Payment cards; Fraud detection; Bayesian analysis; Simulation; Machine learning |
| JEL: | C11 C15 C80 E42 |
| Date: | 2025–02–28 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-17 |
| By: | Tom, Daniel M. Ph.D. |
| Abstract: | We systematically explore the universe of all models using AI search methods. We automate much of the data preparation and testing of each model built along the way. The result is a method and system that generate superior production ready logistic regression models, beating an industry standard consumer credit risk score, GBM and NN ML models. We also incorporate into our system a method to eliminate disparate impact used by the FRB and the FTC. |
| Date: | 2023–03–07 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:qv76j_v1 |
| By: | Ana Vujovic (National Bank of Serbia) |
| Abstract: | The paper presents the Naive Bayes classifier (NBC), one of the standard models used for solving classification problems, in the context of textual analysis. The model is examined first from a theoretical perspective and then from a practical one. An empirical study was conducted with the aim of carrying out a thematic classification of news articles using the NBC. The results of our research confirm that the NBC has a high predictive power despite the simplified assumptions on which it is based. These findings suggest a potential for further application of the NBC in the thematic classification of texts, which may have significant implications for economic research. |
| Keywords: | Naive Bayes classifier, thematic classification, natural language processing |
| JEL: | C13 E37 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nsb:bilten:27 |
| By: | Dion, Francois PhD; Patire, Anthony; Qan, Qijan |
| Abstract: | This document provides a description of the Aimsun Next model that was developed for the I-210 Pilot Integrated Corridor Management (ICM) System. |
| Keywords: | Engineering |
| Date: | 2024–03–15 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsrrp:qt1xw8q5j1 |
| By: | András Borsos (Magyar Nemzeti Bank, Complexity Science Hub Vienna and University of Oxford); Adrian Carro (Banco de España and University of Oxford); Aldo Glielmo (Banda d’Italia); Marc Hinterschweiger (Bank of England); Jagoda Kaszowska-Mojsa (University of Oxford, Narodowy Bank Polski and Polish Academy of Sciences); Arzu Uluc (Bank of England) |
| Abstract: | Over the past decade, agent-based models (ABMs) have been increasingly employed as analytical tools within economic policy institutions. This paper documents this trend by surveying the ABM-relevant research and policy outputs of central banks and other related economic policy institutions. We classify these studies and reports into three main categories: (i) applied research connected to the mandates of central banks; (ii) technical and methodological research supporting the advancement of ABMs; and (iii) examples of the integration of ABMs into policy work. Our findings indicate that ABMs have emerged as effective complementary tools for central banks in carrying out their responsibilities, especially after the extension of their mandates following the global financial crisis of 2007-2009. While acknowledging that room for improvement remains, we argue that integrating ABMs into the analytical frameworks of central banks can support more effective policy responses to both existing and emerging economic challenges, including financial innovation and climate change. |
| Keywords: | agent-based models, central bank policies, monetary policy, financial stability, prudential policies, payment systems |
| JEL: | C63 E27 E37 E42 E58 G10 G21 G23 G51 Q54 R21 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:bde:opaper:2503e |
| By: | Shengwu Du; Travis D. Nesmith |
| Abstract: | Filtered historical simulation (FHS)—a simple method of calculating Value-at-Risk that reacts quickly to changes in market volatility—is a popular method for calculating margin at central counterparties. However, FHS does not address how correlation can vary through time. Typically, in margin systems, each risk factor is filtered individually so that the computational burden increases linearly as the number of risk factors grows. We propose an alternative method that filters historical returns using latent risk factors derived from principal component analysis. We compare this method's performance with "traditional" FHS for different simulated and constructed portfolios. The proposed method performs much better when there are large changes in correlation. It also performs well when that is not the case, although some care needs to be taken with certain concentrated portfolios. At the same time, the computational requirements can be reduced significantly. Backtesting comparisons are performed using data from 2020 when markets were stressed by the COVID-19 crisis. |
| Keywords: | Portfolio risk; Value-at-Risk; Margin; CCPs; Principal component analysis (PCA); Historical simulation; FHS |
| JEL: | G00 G20 |
| Date: | 2025–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-16 |
| By: | Jesus Fernandez-Villaverde; Yiliang Li; Le Xu; Francesco Zanetti |
| Abstract: | We examine the rise of dark shipping – oil tankers disabling AIS transceivers to evade detection – amid Western sanctions on Iran, Syria, North Korea, Venezuela, and Russia. Using a machine learning-based ship clustering model, we track dark-shipped crude oil trade flows worldwide and detect unauthorized ship-to-ship transfers. From 2017 to 2023, dark ships transported an estimated 7.8 million metric tons of crude oil monthly – 43% of global seaborne crude exports – with China absorbing 15%. These sanctioned flows offset recorded declines in global oil exports but create distinct economic shifts. The U.S., a net oil exporter, faces lower oil prices but benefits from cheaper Chinese imports, driving deflationary growth. The EU, a net importer, contends with rising energy costs yet gains from Chinese demand, fueling inflationary expansion. China, leveraging discounted oil, boosts industrial output, propagating global economic shocks. Our findings expose dark shipping’s central role in reshaping oil markets and macroeconomic dynamics. |
| Keywords: | dark shipping, oil sanction, satellite data, clustering analysis, LP |
| JEL: | C32 C38 E32 Q43 R40 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-12 |
| By: | Ordobaev, Amirhan |
| Abstract: | Linear programming (LP) problems play a central role in optimization theory and have significant importance across various fields, from economics to engineering. Among the numerous methods proposed for solving these problems, the Simplex method remains one of the most well-known and widely used. However, despite its efficiency, this method has certain limitations, particularly in terms of high computational costs and slow convergence when dealing with large or sparse data. In response to these challenges, this article proposes an alternative approach based on binary search, which could significantly im prove the efficiency of solving linear programming problems. This approach was developed during the study of the ”Operations Research” course and represents an interesting attempt to propose an alternative to classical methods. |
| Date: | 2025–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:v6eua_v1 |
| By: | Dessouky, Maged; Mahtab, Zuhayer |
| Abstract: | In this report, the authors propose an online and large-scale rideshare system that can dynamically match passenger requests with drivers and provide efficient routes to the drivers. The authors developed a greedy insertion-based routing procedure to route thousands of requests in an hour. They incorporated flexible meeting point selection into the framework, which can reduce travel distances for both drivers and passengers. The authors implemented an online incentive and cost-sharing system that can incentivize drivers and passengers for their ride time limit violations and share the cost of a rideshare trip among the passengers fairly. The authors incorporated a request prediction and detour mechanism into the ridesharing framework. To get the most updated travel time and study the effects of ridesharing in a road network, theauthors also incorporate a simulation approach into the framework. Numerical experiments performed on the New York Taxicab dataset and a rural dataset based on Kern and Tulare Counties, California, show that the proposed framework is effective, matching thousands of requests per hour. Results also show that ridesharing can cost significantly less compared to ride-hailing services such as Uber or Lyft, and incorporating flexible meeting points can reduce travel distance by 4% on average. Simulation studies show that ridesharing can reduce total vehicle miles traveled by 13% in Manhattan on average. The proposed framework can help transportation officials design real-time and city-scale rideshare systems to alleviate traffic congestion problems in California. View the NCST Project Webpage |
| Keywords: | Engineering, Ridesharing, Online Systems, Large-Scale Optimization, Simulation |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt5zp5778b |
| By: | Masaya Yasuoka (School of Economics, Kwansei Gakuin University) |
| Abstract: | This paper examines how labor force losses caused by taking leave or resigning due to long COVID affect the macroeconomy. The analysis yielded the following results. First, a simulation analysis was conducted using a model that does not take unemployment into account. It was found that a 3% loss in the labor force leads to a 2% decrease in Gross Domestic Product (GDP). Furthermore, even when the degree of labor force loss is reduced, it takes a longer time for GDP to return to its original level. Similarly, when frictional unemployment is taken into account, it was found that even after the labor force recovers, it still takes a longer time for GDP to return to its pre-loss level. |
| Keywords: | Long COVID, Labor force losses, Ramsey model |
| JEL: | E24 H20 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:kgu:wpaper:290 |