| 000 | 02663cam a2200325 i 4500 | ||
|---|---|---|---|
| 001 | 21459643 | ||
| 003 | KE-NaKCAU | ||
| 005 | 20240607123819.0 | ||
| 008 | 200306s2020 njua b 001 0 eng | ||
| 010 | _a 2019056749 | ||
| 020 | _a9781119585763 | ||
| 040 |
_aDLC _beng _erda _cKE-NaKCAU _dDLC |
||
| 042 | _apcc | ||
| 050 | 0 | 0 |
_aHV6768 _b.N54 2020 |
| 100 | 1 |
_aNigrini, Mark J. _q(Mark John) |
|
| 245 | 1 | 0 |
_aForensic analytics : _bmethods and techniques for forensic accounting investigations / _cMark J. Nigrini. |
| 250 | _aSecond edition. | ||
| 260 |
_aHoboken : _bWiley, _c2020. |
||
| 300 |
_axxv, 518 pages : _billustrations ; _c26 cm. |
||
| 490 | 0 | _aWiley corporate f&a | |
| 504 | _aIncludes bibliographical references and index. | ||
| 520 | _a"The book will review and discuss (with Access and Excel examples) the methods and techniques that investigators can use to uncover anomalies in corporate and public sector data. These anomalies would include errors, biases, duplicates, number rounding, and omissions. The focus will be the detection of fraud, intentional errors, and unintentional errors using data analytics. Despite the quantitative and computing bias, the book will still be interesting to read with interesting vignettes and illustrations. Most chapters will be understandable by accountants and auditors that usually are lacking in the rigors of mathematics and statistics. The data interrogation methods are based on (a) known statistical techniques, and (b) the author's own published research in the field. New to this edition are: Updates to Windows and Microsoft Office R, which is now a viable data analytics product. New fraud cases There are many published books on data mining, which is defined as the analysis of (large) data sets to find unsuspected relationships, and to summarize the data in novel ways that are both understandable and useful to the data owner. The results of such analyses could be sales predictions or discovering previously unknown patterns and rules. Data mining involves using the data for some specific purpose (often tied to marketing) but typically has no fraud detection motive. Yet, data mining can be a valuable tool to detect errors and anomalies that can lead to the discovery of fraud"-- | ||
| 650 | 0 |
_aForensic accounting. _95820 |
|
| 650 | 0 |
_aFraud. _95818 |
|
| 650 | 0 |
_aMisleading financial statements. _912186 |
|
| 856 | _uhttps://ebookcentral.proquest.com/lib/kcau-ebooks/detail.action?docID=6174018 | ||
| 906 |
_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg |
||
| 942 |
_2lcc _cBK |
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| 999 |
_c18684 _d18684 |
||