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Audit Data Data Set
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Abstract: Exhaustive one year non-confidential data in the year 2015 to 2016 of firms is collected from the Auditor Office of India to build a predictor for classifying suspicious firms.

Data Set Characteristics:  

Multivariate

Number of Instances:

777

Area:

N/A

Attribute Characteristics:

Real

Number of Attributes:

18

Date Donated

2018-07-14

Associated Tasks:

Classification

Missing Values?

Yes

Number of Web Hits:

812


Source:

Nishtha Hooda, CSED, TIET, Patiala


Data Set Information:

The goal of the research is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors. The information about the sectors and the counts of firms are listed respectively as Irrigation (114), Public Health (77), Buildings and Roads (82), Forest (70), Corporate (47), Animal Husbandry (95), Communication (1), Electrical (4), Land (5), Science and Technology (3), Tourism (1), Fisheries (41), Industries (37), Agriculture (200).


Attribute Information:

Many risk factors are examined from various areas like past records of audit office, audit-paras, environmental conditions reports, firm reputation summary, on-going issues report, profit-value records, loss-value records, follow-up reports etc. After in-depth interview with the auditors, important risk factors are evaluated and their probability of existence is calculated from the present and past records.


Relevant Papers:

Hooda, Nishtha, Seema Bawa, and Prashant Singh Rana. 'Fraudulent Firm Classification: A Case Study of an External Audit.' Applied Artificial Intelligence 32.1 (2018): 48-64.



Citation Request:

This research work is supported by Ministry of Electronics and Information Technology (MEITY), Govt.of India


Supported By:

 In Collaboration With:

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