Suchergebnisse

Objective This study tests the reliability of a system (FINANS) to collect and analyze incident reports in the financial trading domain and is guided by a human factors taxonomy used to describe error in the trading domain. Background Research indicates the utility of applying human factors theory to understand error in finance, yet empirical research is lacking. We report on the development of the first system for capturing and analyzing human factors–related issues in operational trading incidents. Method In the first study, 20 incidents are analyzed by an expert user group against a referent standard to establish the reliability of FINANS. In the second study, 750 incidents are analyzed using distribution, mean, pathway, and associative analysis to describe the data. Results Kappa scores indicate that categories within FINANS can be reliably used to identify and extract data on human factors–related problems underlying trading incidents. Approximately 1% of trades ( n = 750) lead to an incident. Slip/lapse (61%), situation awareness (51%), and teamwork (40%) were found to be the most common problems underlying incidents. For the most serious incidents, problems in situation awareness and teamwork were most common. Conclusion We show that (a) experts in the trading domain can reliably and accurately code human factors in incidents, (b) 1% of trades incur error, and (c) poor teamwork skills and situation awareness underpin the most critical incidents. Application This research provides data crucial for ameliorating risk within financial trading organizations, with implications for regulation and policy.

Objective: The aims of this study were (a) to determine whether near-miss incidents in financial trading contain information on the operator skills and systems that detect and prevent near misses and the patterns and trends revealed by these data and (b) to explore if particular operator skills and systems are found as important for avoiding particular types of error on the trading floor. Background: In this study, we examine a cohort of near-miss incidents collected from a financial trading organization using the Financial Incident Analysis System and report on the nontechnical skills and systems that are used to detect and prevent error in this domain. Method: One thousand near-miss incidents are analyzed using distribution, mean, chi-square, and associative analysis to describe the data; reliability is provided. Results: Slips/lapses (52%) and human–computer interface problems (21%) often occur alone and are the main contributors to error causation, whereas the prevention of error is largely a result of teamwork (65%) and situation awareness (46%) skills. No matter the cause of error, situation awareness and teamwork skills are used most often to detect and prevent the error. Conclusion: Situation awareness and teamwork skills appear universally important as a "last line" of defense for capturing error, and data from incident-monitoring systems can be analyzed in a fashion more consistent with a "Safety-II" approach. Application: This research provides data for ameliorating risk within financial trading organizations, with implications for future risk management programs and regulation.