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Record W2893677858

Propuesta de mejoramiento a la gestión de rechazos bancarios en la nómina de U.S.A. para la empresa Schlumberger

2017· dissertation· es· W2893677858 on OpenAlexaboutno aff
Natalia Camacho Escobar

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldBusiness, Management and Accounting
TopicBusiness, Education, Mathematics Research
Canadian institutionsnot available
Fundersnot available
KeywordsPayrollOrder (exchange)Database transactionWork (physics)PopulationBusinessProcess (computing)Data collectionPlan (archaeology)Operations managementGeographyManagementAccountingEngineeringComputer scienceFinanceEconomicsSociologyMathematicsDatabaseStatistics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this project was to propose a plan to improve the payroll process from the United States, in order to obtain a decrease in the number of banks rejections coming from the JP Morgan Chase bank - linked to the payroll accounts of employees of the company Schlumberger- and improve the process of managing the follow-up of these, seeking to obtain the correct and timely result of the employees. The research question is at the heart of the work: How is it possible to intervene into the payroll process in order to decrease the banking rejections? And improve the management that the transactions have of them? To achieve the objectives, different methods of data collection were used, such as the analysis of historical data belonging to the last periods of weight by the area of transactions to employees belonging to the population of NAM (United States and Canada). The method of observation and semi-structured interviews with company personnel, chief of the transactions and personnel area of the JP Morgan Chase bank. As a result of the problem of the development of the research, it was concluded that the types of existing errors occur more frequently, so it is a priority to give immediate attention to mitigate this type of error, and it is necessary to establish the management of the existing rejections, for which a maximum period of 2 business days was set, in which both the employees and the transaction analysts must manage and give the corresponding solution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0060.002
Open science0.0020.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.345
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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