<?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/BED63D7D-D1C1-41B0-B4D5-8B005C7209B8" ns1:id="BED63D7D-D1C1-41B0-B4D5-8B005C7209B8"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/A3478092-F4F8-477A-8889-6EDE493E1B46" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/5C99A150-D1CD-4AE9-BAC4-75C893F91623" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/5C99A150-D1CD-4AE9-BAC4-75C893F91623" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2021-05-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/4B51EB16-0B86-44E2-AC99-3703BE0D69C5" ns1:rel="FUND" ns1:start="2020-03-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">47656</ns2:identifier></ns2:identifiers><ns2:title>Using advanced AI and Machine Learning to identify and detect signs of vulnerability, wellbeing and mental stress over the phone</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Study</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>By combining leading research in psychology and linguistics (developed as part of a KTP with Manchester University) with advanced Natural Language Processing (NLP), waveform analysis and machine learning in our platform, we intend to develop a highly advanced , highly accurate vulnerability analysis tool that will help Contact Centres organisations better serve their customers, prevent non- compliance with vulnerability legislation and help monitor the mental wellbeing of call operatives.

Although calls are recorded as a matter of course in all contact centres, at present there is no automated system to detect vulnerability, general wellbeing using voice data, with on average only a 2% sample analysed manually. By developing a system that can detect the signs of vulnerability both in the tone of voice and in the language used, the innovative technology, will provide the following positive impacts;

**Client benefits**

* Improved accuracy in detecting stress and vulnerability.
* Provide an automated, cost effective solution to an expensive, manual process.
* Monitor 100% of calls rather than &amp;lt;2% call presently.
* Reduce the risk of fines &amp;amp; penalties for non-compliance
* Intelligent customer service process - building in emotional intelligence into the relationship.

**Consumer benefits**

* Prevent misselling/pressured sales to vulnerable people
* Improved customer service/satisfaction.
* Reduced incidents of misselling/cancelled orders during cool-off/fines.
* Compliance with FCA Consumer Vulnerability legislation.
* 90% saving (time and cost) assessing vulnerability.

We see this as a &amp;pound;47m opportunity by 2026 and chance to develop a cutting- edge, best in class technology, with genuine global appeal - addressing a clear, global business need (inefficient manual processes), creating a number of high-value UK R&amp;amp;D jobs, boosting UK exports and helping companies address a defined regulatory requirement they have so far struggled with.

Although initially focused on financial services and call centres, with significant wider technology potential in the mental healthcare sector once fully developed, the project represents a step-change innovation and a opportunity for the UK to take the lead in the development of mental health technology.</ns2:abstractText></ns2:project>