<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/19B89308-7AB3-4185-9517-13F9804FD839" ns1:id="19B89308-7AB3-4185-9517-13F9804FD839"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/115ED3D5-38B0-48BE-975D-99099C17B547" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/27095BB0-C02B-401D-9427-A2BCEB762402" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/47CCC3E3-E4FA-4918-AC76-AF09ADAD3FED" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/27095BB0-C02B-401D-9427-A2BCEB762402" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2022-07-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/01B9EB52-B04C-44D3-B9F9-0F99ECCAAA8E" ns1:rel="FUND" ns1:start="2021-06-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10004726</ns2:identifier></ns2:identifiers><ns2:title>Development of an AI and ML enabled risk assessment platform to reduce fraud in the NHS</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Fraud, risk and compliance in the NHS is estimated to cost on average around &amp;pound;5.7 billion every year and takes away money which services could be spending on patient care. In particular, the total loss of procurement expenditure is estimated to be &amp;pound;1.05 billion. Moreover, the annual fraud losses are still growing in spite of the 300 professionally trained and accredited Local Counter Fraud Specialists who are in place within health bodies across England and Wales.

There are no effective solutions available across the NHS which can manage the risk of procurement fraud and errors effectively and efficiently.

To meet this unmet market need, Fiscal Technologies Ltd, in collaboration with University of Reading are developing a Machine Learning based risk managment platform which will redefine &amp;quot;state of the art&amp;quot; in its approach by automating constant and continuous risk assessment to enable early proactive intervention.

The proposed NHS co-designed and GDPR-compliant development, using NHS-provided data, will provide accurate, unparalleled insights to reduce procurement risks.</ns2:abstractText></ns2:project>