Employing generalized audit software in the financial services sector
Bibliographic record
Abstract
Purpose Computer assisted audit techniques (CAATs) encompass a range of computerized techniques that internal and external auditors use to facilitate their audit objectives. One of the most important CAATs is generalized audit software (GAS), which is a class of packaged software that allows auditors to interrogate a variety of databases, application software and other sources and then conduct analyzes and audit routines on the extracted or live data. This study seeks to evaluate the nature and extent of the utilization of CAATs in financial institutions. In particular, the study establishes the extent and nature of use of GAS by bank internal auditors and their external auditors. The study is conducted with large local and international commercial banks in Singapore, a major financial center. Given the limited research on GAS in general and in the financial services sector in particular, the study uses exploratory qualitative research. Design/methodology/approach Qualitative research employing depth interviews with internal and external auditors of financial institutions. Finding The research finds that the extent and range of use of GAS varies widely between the institutions in the sample. Internal auditors see GAS primarily as a tool for special investigations rather than as a foundation for their regular audit work. External auditors make no use of GAS, citing the inapplicability of this class of tool to the nature of testing the financial statement assertions or the extent or quality of computerized internal controls maintained by the bank. Research limitations/implications This is a small sample study. While the data are rich, the findings cannot necessarily be extrapolated to broader populations. Practical implications This study provides guidance on the role that CAATs play in the audit process of financial institutions that is relevant for the audit community. Originality/value This is the first in‐depth study of the application of CAATs to financial institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".