Difference between revisions of "IN-RACS"
| (7 intermediate revisions by the same user not shown) | |||
| Line 7: | Line 7: | ||
<td colspan="2" style="padding-bottom:.5em; padding-top:.5em; text-align:center; border-bottom:1px solid #a2a9b1;"> | <td colspan="2" style="padding-bottom:.5em; padding-top:.5em; text-align:center; border-bottom:1px solid #a2a9b1;"> | ||
| − | [[File: | + | [[File:Partof_essb.png|150px|frameless|border|ES/S-A Platform]] |
[[File:In-racs.png|150px|frameless|border|iN-RACS]] | [[File:In-racs.png|150px|frameless|border|iN-RACS]] | ||
| Line 22: | Line 22: | ||
<td style="width: 130px;vertical-align: top;">'''Developer''' | <td style="width: 130px;vertical-align: top;">'''Developer''' | ||
</td> | </td> | ||
| − | <td>Netroda Technologies | + | <td>Netroda Technologies<br>Insitiute for Research in Technical Analytics (INRITA) |
</td> | </td> | ||
</tr> | </tr> | ||
| Line 35: | Line 35: | ||
<td style="width: 130px;vertical-align: top;">'''Products''' | <td style="width: 130px;vertical-align: top;">'''Products''' | ||
</td> | </td> | ||
| − | <td>< | + | <td><ul><li>ES/S-B ARITA <ul><li>NT-INCORP</li></ul></li></ul> |
</td></tr> | </td></tr> | ||
<tr> | <tr> | ||
| Line 45: | Line 45: | ||
<tr> | <tr> | ||
<td style="width: 130px;vertical-align: top;">'''Platform'''</td> | <td style="width: 130px;vertical-align: top;">'''Platform'''</td> | ||
| − | <td>[[Extensible Services / Server]] for | + | <td>[[Extensible Services / Server]] for Business |
</td></tr> | </td></tr> | ||
| Line 55: | Line 55: | ||
Based on a limited set of information, IN-RACS can accurately predict a willingness to commit award fraud, or failure to accept received invoices. The Algorithm has been tuned with scientifically proven data of behavioural analysis in diverse social groups. | Based on a limited set of information, IN-RACS can accurately predict a willingness to commit award fraud, or failure to accept received invoices. The Algorithm has been tuned with scientifically proven data of behavioural analysis in diverse social groups. | ||
| + | |||
| + | == Applicability == | ||
| + | |||
| + | iN-RACS has been specifically designed for the cautions in business areas covered by NT-INCORP, these include general construction services. The high financial impact negative customers adduce to construction business, and the general correlation between construction projects and associated high risk customers has led to the formation of the system. | ||
| + | |||
| + | == Theory of operation == | ||
| + | |||
| + | Generally, the iN-RACS database contains options for a customer, these options are linked to specific factors, including general lawfullness, probabilities of committing of a crime, gender- and age specific behaviours, presumed overall intelligence, and more. | ||
=== Fields === | === Fields === | ||
Latest revision as of 03:06, 16 October 2023
| iN-RACS | |
|---|---|
|
| |
| Developer | Netroda Technologies Insitiute for Research in Technical Analytics (INRITA) |
| Type | Software Interface |
| Products |
|
| Initial Release | 2022 (2017 as ADPP) |
| Platform | Extensible Services / Server for Business |
General
IN-RACS (INCORP-Risk Assesment on Customer Services) is a integrated module of NT-INCORP that enables heuristic recognition of suspected negative customers before order award.
Based on a limited set of information, IN-RACS can accurately predict a willingness to commit award fraud, or failure to accept received invoices. The Algorithm has been tuned with scientifically proven data of behavioural analysis in diverse social groups.
Applicability
iN-RACS has been specifically designed for the cautions in business areas covered by NT-INCORP, these include general construction services. The high financial impact negative customers adduce to construction business, and the general correlation between construction projects and associated high risk customers has led to the formation of the system.
Theory of operation
Generally, the iN-RACS database contains options for a customer, these options are linked to specific factors, including general lawfullness, probabilities of committing of a crime, gender- and age specific behaviours, presumed overall intelligence, and more.
Fields
| Name | Purpose |
|---|---|
| Profession | Describes the customers profession (Alma mater, Job Status and Position) |
| Gender | Describes the customers prominent biological gender |
| Age | The customers age in years |
