119:
company, if relevant ethical requirements are able to be met, and any other implications that will affect the completion of the audit. In the audit planning stage, audit evidence is the information that the auditor considers when determining the most effective and efficient approach for the audit. For example, reliability of internal control procedures, and analytical review systems. In the control testing stage, audit evidence is used by the auditor to consider the mix of audit test of controls and audit substantive tests. In the substantive testing stage, audit evidence is defined as the information that the auditor needs to support the appropriation of financial statement assertions. Finally in the conclusion and opinion formulation stage, audit evidence is the information considered by the auditor before determining whether the financial statements as a whole present with completeness, validity, accuracy, and consistency with the auditor's understanding of the entity.
154:
processing, and data mining to elevate audit data analytics, which is briefly discussed in the paragraph below. Additionally, big data is characterized by its size, velocity, veracity, and variety. These characteristics allow big data to contribute to the sufficiency and relevancy of audit evidence. Big data is an external source obtained directly by the auditor, and therefore, can increase reliability of the audit evidence. Since it is real-time data from external sources and is so large, it is more difficult for an individual to tamper with, increasing the reliability. If an auditor is having difficulties retrieving clients' information, using big data can serve as an alternative way to obtain relevant information for the audit or can be used in conjunction with existing evidence as a supplement.
167:
which can help the audit by providing more assurance to the auditor and provide higher quality audit evidence. This complete testing can make the evidence more accurate. Audit data analytics can also provide the auditor with a greater understanding of the evidence, leading to more informed decisions. The audit data analytics process may take some years to become fully functioning. In the first year, the company must find the appropriate data set, and the auditor must perform data wrangling on that data set in order for analytics procedures to be carried out. In this case, data wrangling refers to when an auditor reconciles and formats a data set. This process performed in the first year will require extra time added to the traditional audit, so it may initially take longer than sampling.
141:
large quantities of data can be collected and condensed, auditors can use the information from AI to make more efficient decisions throughout the audit. Additionally, AI can be programmed to find certain things such as material misstatements, and can identify these mistakes in less time than humans. Technology is capable of reformatting different pieces of audit evidence so that it is comparable with other evidence that has been found, improving the auditor's efficiency. AI that is properly functioning and maintained can also reduce substantive testing of audit evidence, which will reduce the time of the audit.
45:(Certified Public Accountant) can confirm the company's financial statements. Audit evidence is the primary support for an auditor's opinion on if there is a reasonable assurance that the company's financial statements are not materially misstated due to fraud or error. Audit evidence consists of various audit procedures and can often have a different role in the different stages of an audit. Audit evidence must be sufficient and appropriate, which means it is reliable and relevant. The auditor must use their own professional judgement when determining if the audit evidence is persuasive and sufficient.
158:
misinterpreted as causation, which could lead to inaccurate conclusions. As for challenges auditors would face, one would be linking the big data with the relevant, traditional audit evidence in order to complement it. Another challenge would involve efficiently and accurately sorting and summarizing the large volume of data, which can be achieved through the use of data mining if done successfully. Some sources of big data may come from outlets such as news articles or social media, which could be influenced by biases and make the evidence inappropriate to use as representative of the population.
132:
they are testing materiality of statement differences. Next, reliability is decided based on where the evidence came from and what happened to obtain it. Evidence obtained externally from an expert or professional that is unrelated to the client has a high level of reliability compared to evidence from within the client’s company. The reliability of internal evidence can be improved by ensuring that the company’s internal controls are functioning properly. Another very reliable form of evidence is direct knowledge, which means the auditor learned this information directly.
49:
audit evidence due to the large quantities of data that can be processed with very little error. Audit evidence collection is also being improved through audit data analytics, which also provide the auditor the ability to view the entire population of data, rather than just a sample. Viewing greater amounts of data leads to a more efficient audit and a greater understanding of the audit evidence.
166:
Rather than replacing or eliminating traditional audit techniques completely, audit data analytics can be used alongside the traditional methods to optimize the sufficiency and relevance of audit evidence. Audit data analytics allows auditors to look at an entire population rather than just a sample,
144:
While AI can make the auditing process easier, there are concerns that AI will dismiss audit evidence that humans would not have overlooked due to immateriality. Currently, many auditors that use AI are utilizing its abilities to analyze evidence efficiently, but are still depending on human judgment
170:
Additionally, audit data analytics can assist with an auditor's risk assessment; the auditor can identify the company's trends and compare them to the industry norms using this technology. If the auditor finds an unusual difference or discrepancy, they can investigate it further. More specifically,
140:
The collection of audit evidence has transformed with the use of artificial intelligence (AI). This new technology allows auditors to examine large quantities of evidence efficiently, allowing auditor's to quickly determine the validity of the audit evidence they are examining. Due to the fact that
131:
For audit evidence to be appropriate, it needs to be relevant and reliable. It is relevant when it is heavily related to the assertion or control that is being tested. The audit’s design can change the level of relevancy. This includes whether they are testing the identified assertion or control or
118:
Audit evidence plays a role in each phase of the audit. In the acceptance and continuance phase, audit evidence is the information the auditor considers before making their decision. Some examples of this are the competency and capability of the audit team, integrity of key employees in the client
78:
is when the auditor watches someone do a specific job, such as watching an employee count products in a warehouse. The evidence gathered in an observation usually applies to that date. This means an inventory observation on
December 31 demonstrates the amount of inventory the company has on that
48:
Audit evidence has undergone significant change with the emergence of
Artificial Intelligence, Big Data, and audit data analytics. As the field of accounting is transforming, technologies such as AI (artificial intelligence) are playing a role in audit evidence. AI is enhancing the collection of
174:
In relation to specific kinds of audit evidence, there are a couple examples where audit data analytics can alter the methods of collection. Traditionally, bank confirmations, analytical procedures, and journal entry testing would most likely be carried out at the client site by the audit team
153:
Audit technology has allowed auditors to acquire audit evidence from multiple sources, both financial and non-financial. Technology that works with big data can work alongside audit evidence to increase the quality and efficiency of an audit. Big data uses pattern recognition, natural-language
157:
On the other hand, big data could have some drawbacks. There are some concerns regarding the data quality you get with big data due to the possibility of increased false positives, which would decrease reliability. Additionally, big data evidence can be an indicator of association, but can be
127:
There are a couple aspects of evidence that make various audit evidence good quality. This consists of sufficiency and appropriateness. Audit evidence is sufficient when there is an acceptable amount of evidence found. This changes based on the risk of material misstatement and the quality of
52:
Along with audit data analytics, big data has allowed auditors to use more sources for audit evidence and helps increase the quality and efficiency of audits. Alternatively, the quality of the data in these new sources can not always be seen as reliable, which can be a drawback to big data’s
171:
the planning stage can be improved by the auditors using the technology to enhance their understanding of the company and its industry. Data analytics can also provide a thorough, detailed analysis of a company's general ledger or sub ledgers, which can provide more evidence to the auditor.
676:
128:
evidence that was found. The higher the risk that the financial statements are materially misstated, the more evidence an auditor should collect. The better a specific piece of evidence is, the less an auditor needs to find additional evidence.
178:
With the emergence and growing popularity of audit data analytics, the accounting profession is tasked with evolving its auditing standards to encompass the usage of data analytics technology, big data, and “continuous auditing.”
677:
http://www.aicpa.org/Research/Standards/AuditAttest/DownloadableDocuments/AU-00326.pdf“What Is Audit
Evidence? Definition and Meaning.” BusinessDictionary.com, www.businessdictionary.com/definition/audit-evidence.html.
175:
itself. With the use of this technology, these kinds of procedures could be handed off to other groups to be completed remotely (specialists, third parties) rather than having to personally travel to the sites.
145:
and professional skepticism. The technology of AI is still relatively new, meaning that a lot of training still needs to be done before auditors worldwide can use this application.
72:
occurs when the auditor check's the clients records for important evidence. These records can be from the client, or from outside companies giving information about the client.
65:
describes the procedures used to get audit evidence. This includes inspection, observation, inquiry, confirmation, recalculation, reperformance, and analytical procedures.
671:
41:
Audit evidence is required by auditors to determine if a company has correct information considering their financial statements. If the information is correct, a
62:
410:
188:
335:
458:"AS 2201: An Audit of Internal Control Over Financial Reporting That Is Integrated with An Audit of Financial Statements"
603:
42:
203:
198:
691:
509:
483:
457:
431:
384:
358:
223:
85:
involves speaking to people, whether inside or outside the client's company, to learn specific information.
193:
411:"Quality Control for an Engagement Conducted in Accordance With Generally Accepted Auditing Standards"
274:
Yoon, Kyunghee; Hoogduin, Lucas; Zhang, Li (1 June 2015). "Big Data as
Complementary Audit Evidence".
35:
650:
567:
540:"Institutional Monitoring, Political Connections and Audit Fees: Evidence from Malaysian Firms"
109:
are done to check unexpected variances and investigate financial and non-financial information.
642:
559:
91:
is a response to the auditor from third party roles about information regarding an assertion.
634:
551:
283:
97:
is when the auditor checks if mathematical procedures are correct, such as re-adding totals.
31:
623:"Audit data analytics of unregulated voluntary disclosures and auditing expectations gap"
685:
654:
571:
313:
103:
occurs when the auditor redoes certain processes to ensure they were done correctly.
622:
538:
Tee, Chwee Ming; Gul, Ferdinand A.; Foo, Yee-Boon; Teh, Chee Ghee (2017-01-22).
638:
646:
563:
27:
23:
539:
555:
287:
314:"The effect of artificial intelligence technologies on audit evidence"
420:. American Institute of Certified Professional Accountants.
336:"How firms are delivering value with audit data analytics"
672:ISA (UK and Ireland) 500 - Audit Evidence, concept
627:International Journal of Disclosure and Governance
604:"Data analytics helps auditors gain deep insight"
63:Public Company Accounting Oversight Board (PCAOB)
312:Al-Sayyed, Saleh Mohammed (December 8, 2020).
16:Evidence that auditors use to verify accuracy
8:
484:"AS 2305: Substantive Analytical Procedures"
516:. Public Company Accounting Oversight Board
490:. Public Company Accounting Oversight Board
464:. Public Company Accounting Oversight Board
438:. Public Company Accounting Oversight Board
391:. Public Company Accounting Oversight Board
365:. Public Company Accounting Oversight Board
230:. Public Company Accounting Oversight Board
621:Michael, Amir; Dixon, Rob (2019-09-06).
215:
329:
327:
325:
323:
269:
267:
265:
136:Effect of Technology on Audit Evidence
123:Qualities of Acceptable Audit Evidence
597:
595:
593:
591:
589:
587:
585:
583:
581:
533:
531:
353:
351:
349:
263:
261:
259:
257:
255:
253:
251:
249:
247:
245:
7:
307:
305:
303:
301:
299:
297:
510:"AS 2810: Evaluating Audit Results"
385:"AS 2310: The Confirmation Process"
189:International Standards on Auditing
114:Audit Evidence Through Audit Stages
14:
544:International Journal of Auditing
602:Murphy, Maria (April 13, 2015).
334:Tysiac, Ken (January 21, 2020).
204:Financial statement assertions
1:
149:Big Data and Audit Evidence
708:
639:10.1057/s41310-019-00065-x
432:"AS 2101: Audit Planning"
359:"AS 1105: Audit Evidence"
224:"AS 1105: Audit Evidence"
199:HKSA 500 Audit Evidence
608:Journal of Accountancy
340:Journal of Accountancy
194:ISA 500 Audit Evidence
107:Analytical Procedures
162:Audit Data Analytics
36:audit working papers
34:and recorded in the
342:. AICPA & CIMA.
276:Accounting Horizons
556:10.1111/ijau.12086
288:10.2308/acch-51076
610:. AICPA&CIMA.
699:
659:
658:
618:
612:
611:
599:
576:
575:
535:
526:
525:
523:
521:
506:
500:
499:
497:
495:
480:
474:
473:
471:
469:
454:
448:
447:
445:
443:
428:
422:
421:
415:
407:
401:
400:
398:
396:
381:
375:
374:
372:
370:
355:
344:
343:
331:
318:
317:
309:
292:
291:
271:
240:
239:
237:
235:
220:
57:Audit Procedures
707:
706:
702:
701:
700:
698:
697:
696:
682:
681:
668:
663:
662:
620:
619:
615:
601:
600:
579:
537:
536:
529:
519:
517:
508:
507:
503:
493:
491:
482:
481:
477:
467:
465:
456:
455:
451:
441:
439:
430:
429:
425:
413:
409:
408:
404:
394:
392:
383:
382:
378:
368:
366:
357:
356:
347:
333:
332:
321:
311:
310:
295:
273:
272:
243:
233:
231:
222:
221:
217:
212:
185:
164:
151:
138:
125:
116:
59:
53:contributions.
32:financial audit
17:
12:
11:
5:
705:
703:
695:
694:
692:Auditing terms
684:
683:
680:
679:
674:
667:
666:External links
664:
661:
660:
633:(4): 188–205.
613:
577:
550:(2): 164–176.
527:
501:
475:
449:
423:
402:
376:
345:
319:
293:
282:(2): 431–438.
241:
214:
213:
211:
208:
207:
206:
201:
196:
191:
184:
181:
163:
160:
150:
147:
137:
134:
124:
121:
115:
112:
111:
110:
104:
98:
92:
86:
80:
73:
58:
55:
20:Audit evidence
15:
13:
10:
9:
6:
4:
3:
2:
704:
693:
690:
689:
687:
678:
675:
673:
670:
669:
665:
656:
652:
648:
644:
640:
636:
632:
628:
624:
617:
614:
609:
605:
598:
596:
594:
592:
590:
588:
586:
584:
582:
578:
573:
569:
565:
561:
557:
553:
549:
545:
541:
534:
532:
528:
515:
511:
505:
502:
489:
485:
479:
476:
463:
459:
453:
450:
437:
433:
427:
424:
419:
412:
406:
403:
390:
386:
380:
377:
364:
360:
354:
352:
350:
346:
341:
337:
330:
328:
326:
324:
320:
315:
308:
306:
304:
302:
300:
298:
294:
289:
285:
281:
277:
270:
268:
266:
264:
262:
260:
258:
256:
254:
252:
250:
248:
246:
242:
229:
225:
219:
216:
209:
205:
202:
200:
197:
195:
192:
190:
187:
186:
182:
180:
176:
172:
168:
161:
159:
155:
148:
146:
142:
135:
133:
129:
122:
120:
113:
108:
105:
102:
101:Reperformance
99:
96:
95:Recalculation
93:
90:
87:
84:
81:
77:
74:
71:
68:
67:
66:
64:
56:
54:
50:
46:
44:
39:
37:
33:
29:
25:
21:
630:
626:
616:
607:
547:
543:
518:. Retrieved
513:
504:
492:. Retrieved
487:
478:
466:. Retrieved
461:
452:
440:. Retrieved
435:
426:
417:
405:
393:. Retrieved
388:
379:
367:. Retrieved
362:
339:
279:
275:
232:. Retrieved
227:
218:
177:
173:
169:
165:
156:
152:
143:
139:
130:
126:
117:
106:
100:
94:
89:Confirmation
88:
82:
75:
69:
60:
51:
47:
40:
26:obtained by
19:
18:
76:Observation
210:References
70:Inspection
655:203242307
647:1741-3591
572:157871655
564:1090-6738
520:April 19,
494:April 19,
468:April 19,
442:April 19,
395:March 13,
30:during a
686:Category
369:March 4,
234:March 4,
183:See also
28:auditors
24:evidence
83:Inquiry
653:
645:
570:
562:
651:S2CID
568:S2CID
514:PCAOB
488:PCAOB
462:PCAOB
436:PCAOB
418:AICPA
414:(PDF)
389:PCAOB
363:PCAOB
228:PCAOB
79:date.
643:ISSN
560:ISSN
522:2022
496:2022
470:2022
444:2022
397:2022
371:2022
236:2022
61:The
635:doi
552:doi
284:doi
43:CPA
22:is
688::
649:.
641:.
631:16
629:.
625:.
606:.
580:^
566:.
558:.
548:21
546:.
542:.
530:^
512:.
486:.
460:.
434:.
416:.
387:.
361:.
348:^
338:.
322:^
296:^
280:29
278:.
244:^
226:.
38:.
657:.
637::
574:.
554::
524:.
498:.
472:.
446:.
399:.
373:.
316:.
290:.
286::
238:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.