Name
Abstract | ||
|---|---|---|
The state of the art of data engineering and statistical algorithms opens the possibility for automating certain tasks Although field types of columns may not be known in advance, we assume data type is enforced, e.g., that date fields have nothing but dates, in some specific format. Data related tasks include feature engineering, data processing, text recognition and errors detection. For example, of interest to auditors is detection of input errors in financial spreadsheets, fraudulent transactions, abusive insurance claims, etc. Clearly, for a large dataset, an auditor cannot possibly go through the entire set of records, so a way to highlight records of interest may be of great value by increasing audits productivity. Once an auditor has identified a suspect record, he/she may be interested to give “similar” record a closer examination. In this short essay we demonstrate the plausibility of automating the process of feature creation, outlier detection and calculating the distance between any two records in a spreadsheets whereby providing the proverbial auditor the means to quickly identify strange and unusual records. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/AI4I.2018.8665700 | 2018 First International Conference on Artificial Intelligence for Industries (AI4I) |
Keywords | Field | DocType |
Data models,Feature extraction,Predictive models,Numerical models,Data engineering,Data processing,Transforms | Data modeling,Anomaly detection,Data processing,Information retrieval,Computer science,Feature extraction,Data type,Feature engineering,Information engineering,Suspect | Conference |
ISBN | Citations | PageRank |
978-1-5386-9209-7 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Barr | 1 | 0 | 0.34 |
| Peter Shaw | 2 | 92 | 6.34 |