Marcos Machado

451 total citations
20 papers, 240 citations indexed

About

Marcos Machado is a scholar working on Marketing, Management Science and Operations Research and Accounting. According to data from OpenAlex, Marcos Machado has authored 20 papers receiving a total of 240 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Marketing, 8 papers in Management Science and Operations Research and 7 papers in Accounting. Recurrent topics in Marcos Machado's work include Customer churn and segmentation (8 papers), Financial Distress and Bankruptcy Prediction (7 papers) and Stock Market Forecasting Methods (5 papers). Marcos Machado is often cited by papers focused on Customer churn and segmentation (8 papers), Financial Distress and Bankruptcy Prediction (7 papers) and Stock Market Forecasting Methods (5 papers). Marcos Machado collaborates with scholars based in Netherlands, Canada and Brazil. Marcos Machado's co-authors include Salma Karray, Joerg Osterrieder, Alessandra Amato, Celma de Oliveira Ribeiro, Erik Eduardo Rego, Branka Hadji Misheva, João Moreira, Daniel Chen, Martijn Mes and Gayane Sedrakyan and has published in prestigious journals such as Expert Systems with Applications, Applied Soft Computing and Journal of Retailing and Consumer Services.

In The Last Decade

Marcos Machado

16 papers receiving 233 citations

Peers

Marcos Machado

ACCOU

MARKE

MSOR

RNMI

P. Jeyanthi India

Jingqi Wang China

S. Suman Rajest India

Muhammad Zubair Khan Pakistan

Kristina Šutienė Lithuania

Rukun Santoso Indonesia

Md Shajalal Bangladesh

Nur Iriawan Indonesia

Niklas Bussmann Italy

P. Jeyanthi India

Marcos Machado

63 ×0.8

31 ×0.5

ACCOU

7 ×0.3

MARKE

14 ×0.6

MSOR

9 ×0.4

RNMI

Citations per year, relative to Marcos Machado Marcos Machado (= 1×) peers P. Jeyanthi

Countries citing papers authored by Marcos Machado

Since Specialization

Citations

This map shows the geographic impact of Marcos Machado's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marcos Machado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcos Machado more than expected).

Fields of papers citing papers by Marcos Machado

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marcos Machado. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marcos Machado. The network helps show where Marcos Machado may publish in the future.

Co-authorship network of co-authors of Marcos Machado

This figure shows the co-authorship network connecting the top 25 collaborators of Marcos Machado. A scholar is included among the top collaborators of Marcos Machado based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marcos Machado. Marcos Machado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Machado, Marcos, Daniel Chen, & Joerg Osterrieder. (2025). An analytical approach to credit risk assessment using machine learning models. Decision Analytics Journal. 16. 100605–100605. 3 indexed citations

Machado, Marcos, et al.. (2025). Modeling commodity price co-movement: building on traditional time series models and exploring applications of machine learning algorithms. Decisions in Economics and Finance. 2 indexed citations

Machado, Marcos, et al.. (2025). Advancing credit risk assessment in the retail banking industry: A hybrid approach using time series and supervised learning models. Data & Knowledge Engineering. 160. 102490–102490.

Osterrieder, Joerg, et al.. (2024). How can consumers without credit history benefit from the use of information processing and machine learning tools by financial institutions?. Information Processing & Management. 62(2). 103972–103972. 1 indexed citations

Osterrieder, Joerg, et al.. (2024). Predicting Retail Customers' Distress: Early Warning Systems and Machine Learning Applications. SSRN Electronic Journal. 1 indexed citations

Machado, Marcos, et al.. (2024). How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review. International Journal of Information Management Data Insights. 4(2). 100279–100279. 3 indexed citations

Sedrakyan, Gayane, et al.. (2024). Design Implications for Integrating AI Chatbot Technology with Learning Management Systems: A Study-based Analysis on Perceived Benefits and Challenges in Higher Education. University of Twente Research Information. 1–8. 1 indexed citations

Franco, Ademir, et al.. (2024). The Brazilian (FRANCO) method for dental age estimation: Willems’ model revisited. Clinical Oral Investigations. 28(9). 495–495. 3 indexed citations

Machado, Marcos, et al.. (2024). Green AI in the finance industry: Exploring the impact of feature engineering on the accuracy and computational time of Machine Learning models. Applied Soft Computing. 167. 112343–112343. 1 indexed citations

10.

Machado, Marcos, et al.. (2024). Predicting retail customers' distress in the finance industry: An early warning system approach. Journal of Retailing and Consumer Services. 82. 104101–104101. 4 indexed citations

11.

Amato, Alessandra, Joerg Osterrieder, & Marcos Machado. (2024). How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review. International Journal of Information Management Data Insights. 4(2). 100234–100234. 8 indexed citations

12.

Machado, Marcos, et al.. (2024). Forecasting Customers' Risk-Adjusted Revenue: An Explainable Machine Learning Approach for the Telecommunication Industry. SSRN Electronic Journal.

13.

Osterrieder, Joerg, et al.. (2023). An Overview - Stress Test Designs for the Evaluation of AI and ML Models Under Shifting Financial Conditions to Improve the Robustness of Models. SSRN Electronic Journal. 1 indexed citations

14.

Machado, Marcos, et al.. (2023). Applications of machine learning algorithms to support COVID-19 diagnosis using X-rays data information. Expert Systems with Applications. 238. 122029–122029. 17 indexed citations

15.

Machado, Marcos & Salma Karray. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms. Expert Systems with Applications. 200. 116889–116889. 60 indexed citations

16.

Machado, Marcos & Salma Karray. (2022). Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry. Electronic Commerce Research and Applications. 56. 101202–101202. 16 indexed citations

17.

Machado, Marcos & Salma Karray. (2022). Integrating Customer Portfolio Theory and the Multiple Sources of Risk Approaches to Model Risk-Adjusted Revenue. IFAC-PapersOnLine. 55(16). 356–363. 3 indexed citations

18.

Osterrieder, Joerg, Branka Hadji Misheva, & Marcos Machado. (2022). Digital Finance - Reaching New Frontiers. SSRN Electronic Journal. 2 indexed citations

19.

Machado, Marcos, et al.. (2019). An Agent-Based Model Applied to Brazilian Wind Energy Auctions. IEEE Latin America Transactions. 17(5). 865–874. 3 indexed citations

20.

Machado, Marcos, et al.. (2019). LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry. 1111–1116. 111 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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