RFP QuestBeta
OpenStage · award

Thirteen Housing Group Limited

Income Maximisation Project

Software & IT SystemsCPV 48000000
ValueValue not published
Deadline
Published25 Jan 2022
RegionNationwide
Who to contact
Sara Vasey
procurement@thirteengroup.co.uk
+44 03001111000

The procurement contact named on the official notice.

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The brief

We require an Intelligent automation risk based artificial intelligence and machine learning approach to caseload management and collecting debt which also includes an integrated call handling and text messaging solution.

This will work alongside our current housing management system, Orchard but also has the capacity to work alongside other housing management systems if this was to change.

Through behavioural science the system must optimise the benefits of nudge theory to ensure that as a business we maximise our income through maximising automation opportunities and ensuring that manual intervention is made only when needed and in a way that will provide Thirteen with the best outcome.

Key requirements

What the supplier must deliver

01

We require an Intelligent automation risk based

We require an Intelligent automation risk based artificial intelligence and machine learning approach to caseload management and collecting debt which also includes an integrated call handling and text messaging solution.

02

Through behavioural science the system must optimise

Through behavioural science the system must optimise the benefits of nudge theory to ensure that as a business we maximise our income through maximising automation opportunities and ensuring that manual intervention is made only when needed and in a way that will provide Thirteen with the best outcome.

Derived from the notice text — always confirm against the original documents.

What this bid requires

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Buyer intelligence

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Source & provenance
OCID
ocds-h6vhtk-02f79b
Stage
award · Awarded
Source
Find a Tender
Buyer ref
002132-2022
View the original notice on Find a Tender

Contains public sector information licensed under the Open Government Licence v3.0. Source data © Crown copyright.

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