# MARKET ENGAGEMENT on behalf of NHS CFA- Advanced Analytical Data Science Capability to Counter Fraud in the NHS - Delivery Partner

> A sourced UK public sector contract award published by NHS Shared Business Services.

## Key facts

- Buyer: [NHS Shared Business Services](/buyers/nhs-shared-business-services/contract-awards)
- Published: 28 September 2023
- Stage: pre_procurement
- Notice ID: cee8d817-0665-46b8-8d6d-2a958762f716-684579
- OCID: ocds-b5fd17-5288f571-cb1d-40a7-a610-7698bd8f1e1c

## Description

It is the NHSCFA's intention to use advanced analytical techniques underpinned by data science including machine learning, together with the integration of technology to not only help stop known frauds but predict and uncover those that have yet to occur or those that have yet to be reported or observed\.     By creating a data science technological capability and utilising the copious amount of data captured each day in the NHS\. Harnessing the power of expertise, then actionable outcome through an evidence-based data science approach to fraud detection will not only identify novel patterns of concern but unlock the value of data meaning abuse can be detected earlier in turn protecting NHS funds from fraud\.    The inclusion of fraud detection techniques will also highlight patterns in data that will identify previously unseen fraud trends, therefore improving the time to action\.   The requirement is to secure a partner to build our advanced analytical capability and data environment\. We require a partner who has supported organisations drive forward innovative data analysis to counter fraud\. They will support NHSCFA to become a Centre of Excellence for analytical fraud detection and pattern identification\. We are looking to start the project in October 2023 and deliver the objectives of the project by the end of March 25 Please refer to the full draft specification document for details\.

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## Source

[View the original notice on Contracts Finder](https://www.contractsfinder.service.gov.uk/Notice/cee8d817-0665-46b8-8d6d-2a958762f716-684579)