Steven Finlay
Impact in
- Accounting top 2%
- Financial Distress and Bankruptcy Prediction
- Finance top 5%
- Credit Risk and Financial Regulations
- Banking stability, regulation, efficiency
Papers in
-
- Financial Distress and Bankruptcy Prediction 9
-
- Imbalanced Data Classification Techniques 6
- Co-authors
- Sven F. Crone (1 shared paper)
- Journals
- Journal of the Operational Research Society (2 papers)European Journal of Operational Research (2 papers)International Journal of Forecasting (1 paper)Expert Systems with Applications (1 paper)Palgrave Macmillan UK eBooks (7 papers)
- Partner nations
- United Kingdom
In The Last Decade
Steven Finlay
18 papers receiving 540 citations
Peers
Comparison fields: 5 of 91
- Accounting 356
- Finance 167
- Artificial Intelligence 305
- Management Information Systems 70
- Health Information Management 25
Countries citing papers authored by Steven Finlay
This map shows the geographic impact of Steven Finlay'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 Steven Finlay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Finlay more than expected).
Fields of papers citing papers by Steven Finlay
This network shows the impact of papers produced by Steven Finlay. 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 Steven Finlay. The network helps show where Steven Finlay may publish in the future.
Co-authors
The 1 scholars most cited alongside Steven Finlay, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 162 | |
| 2 | 2011 | 144 | |
| 3 | 2014 | 55 | |
| 4 | 2009 | 54 | |
| 5 | Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods | 2014 | 38 |
| 6 | 2008 | 21 | |
| 7 | 2007 | 20 | |
| 8 | Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies | 2017 | 17 |
| 9 | 2010 | 14 | |
| 10 | 2008 | 13 | |
| 11 | 2005 | 12 | |
| 12 | 2010 | 10 | |
| 13 | 2009 | 8 | |
| 14 | 2012 | 7 | |
| 15 | 2005 | 6 | |
| 16 | 2008 | 6 | |
| 17 | Using genetic algorithms to develop scoring models for alternative measures of performance | 2005 | 5 |
| 18 | Artificial Intelligence for Everyone | 2020 | 1 |
| 19 | 2010 | 1 |
About Steven Finlay
Steven Finlay is a scholar working on Accounting, Artificial Intelligence, Finance, Management Information Systems and Economics and Econometrics, having authored 19 papers that have together received 594 indexed citations. Recurring topics across this work include Financial Distress and Bankruptcy Prediction (9 papers), Imbalanced Data Classification Techniques (6 papers), Banking stability, regulation, efficiency (3 papers), Credit Risk and Financial Regulations (3 papers), Big Data and Business Intelligence (3 papers), Housing Market and Economics (1 paper), Reservoir Engineering and Simulation Methods (1 paper) and Microfinance and Financial Inclusion (1 paper). The work is most often cited by research in Accounting (356 citations), Finance (167 citations), Artificial Intelligence (305 citations), Management Information Systems (70 citations) and Health Information Management (25 citations). Steven Finlay has collaborated with scholars based in United Kingdom. Frequent co-authors include Sven F. Crone. Their work appears in journals such as Journal of the Operational Research Society, European Journal of Operational Research, International Journal of Forecasting, Expert Systems with Applications and Palgrave Macmillan UK eBooks.
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.