Difference between revisions of "IN-RACS"
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− | = | + | <table class="toptextcells toccolours float-right infobox"> |
− | IN-RACS (Risk Assesment on Customer Services) is a integrated module of NT-INCORP that enables heuristic recognition of suspected negative customers before order award. | + | <tr> |
+ | <th colspan="2" style="background:#83caff; padding:.3em .8em; text-align:center; vertical-align:top;"> iN-RACS </th></tr> | ||
+ | <tr> | ||
+ | |||
+ | <td colspan="2" style="padding-bottom:.5em; padding-top:.5em; text-align:center; border-bottom:1px solid #a2a9b1;"> | ||
+ | |||
+ | [[File:Partof_essb.png|150px|frameless|border|ES/S-A Platform]] | ||
+ | [[File:In-racs.png|150px|frameless|border|iN-RACS]] | ||
+ | |||
+ | </td></tr> | ||
+ | <!-- | ||
+ | <tr> | ||
+ | <td colspan="2" style="padding-bottom:.5em; padding-top:.5em; text-align:center; border-bottom:1px solid #a2a9b1;"> | ||
+ | [[File:Sm_splash_startup2.jpg|300px|thumb|Startup Screen]] | ||
+ | </td> | ||
+ | </tr> | ||
+ | --> | ||
+ | |||
+ | <tr> | ||
+ | <td style="width: 130px;vertical-align: top;">'''Developer''' | ||
+ | </td> | ||
+ | <td>Netroda Technologies<br>Insitiute for Research in Technical Analytics (INRITA) | ||
+ | </td> | ||
+ | </tr> | ||
+ | |||
+ | <tr> | ||
+ | <td style="width: 130px;vertical-align: top;">'''Type''' | ||
+ | </td> | ||
+ | <td>Software Interface | ||
+ | </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td style="width: 130px;vertical-align: top;">'''Products''' | ||
+ | </td> | ||
+ | <td><ul><li>ES/S-B ARITA <ul><li>NT-INCORP</li></ul></li></ul> | ||
+ | </td></tr> | ||
+ | <tr> | ||
+ | <td style="width: 130px;vertical-align: top;">'''Initial Release''' | ||
+ | </td> | ||
+ | <td>2022 (2017 as ADPP) | ||
+ | </td></tr> | ||
+ | |||
+ | <tr> | ||
+ | <td style="width: 130px;vertical-align: top;">'''Platform'''</td> | ||
+ | <td>[[Extensible Services / Server]] for Business | ||
+ | </td></tr> | ||
+ | |||
+ | </table> | ||
+ | |||
+ | == 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 === | ||
+ | |||
+ | {| class="wikitable" style="float:center; margin:0 0 0.5em 1em;" | ||
+ | |- | ||
+ | ! 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 | ||
+ | |} |
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 |