Difference between revisions of "IN-RACS"
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| + | <th colspan="2" style="background:#83caff; padding:.3em .8em; text-align:center; vertical-align:top;"> CTSA </th></tr> | ||
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| + | [[File:Partof_essa.png|150px|frameless|border|ES/S-A Platform]] | ||
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| + | [[File:Sm_splash_startup2.jpg|300px|thumb|Startup Screen]] | ||
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| + | <td style="width: 130px;vertical-align: top;">'''Developer''' | ||
| + | </td> | ||
| + | <td>Netroda Technologies | ||
| + | </td> | ||
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| + | <td style="width: 130px;vertical-align: top;">'''Type''' | ||
| + | </td> | ||
| + | <td>Software Interface | ||
| + | </td> | ||
| + | </tr> | ||
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| + | <td style="width: 130px;vertical-align: top;">'''Programmed in''' | ||
| + | </td> | ||
| + | <td>Java, [[Extensible Object Script]] | ||
| + | </td></tr> | ||
| + | <tr> | ||
| + | <td style="width: 130px;vertical-align: top;">'''Reference Class''' | ||
| + | </td> | ||
| + | <td><span style="font-family:monospace;">ESComplexAsset</span> | ||
| + | </td></tr> | ||
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| + | <td style="width: 130px;vertical-align: top;">'''Initial Release''' | ||
| + | </td> | ||
| + | <td>2018 | ||
| + | </td></tr> | ||
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| + | <td style="width: 130px;vertical-align: top;">'''Platform'''</td> | ||
| + | <td>[[Extensible Services / Server]] for Automation | ||
| + | </td></tr> | ||
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| + | </table> | ||
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== General == | == General == | ||
Revision as of 02:45, 16 October 2023
| CTSA | |
|---|---|
|
| |
| Developer | Netroda Technologies |
| Type | Software Interface |
| Programmed in | Java, Extensible Object Script |
| Reference Class | ESComplexAsset |
| Initial Release | 2018 |
| Platform | Extensible Services / Server for Automation |
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.
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 |
