Reforming Welfare Oversight:
Tackling Fraud and Error with AI. 

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Reforming Welfare Oversight:
Tackling Fraud and Error with AI

The Cost of Inaction: Fraud and Error in the UK Benefit System. 

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The Cost of Inaction: Fraud and Error in the UK Benefit System

The Department for Work and Pensions (DWP) administers welfare benefits to approximately 23.7 million people across Great Britain. Despite rigorous controls, the system continues to face significant challenges with fraud and error, leading to substantial financial losses.

Operational inefficiencies Leading to administrative burden, costly investigations and appeals

Major leakage in public funds Preventing further investment in other critical areas, e.g. frontline services, infrastructure

Real human consequences Vulnerable populations affected

Public trust and accountability decreased

eq.? billion
Overpaymanes
Total benefit
overpayment
amount
eø.4 billion
Frud
Ovangmenes
attribu&d to frnud
El.2 billion
Underpasmenes
Claimants
receiving

The issue - UK Benefit Expenditure Overpayments in 2023/2024

Strategic Response: Leveraging AI to Safeguard the Benefit System. 

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Strategic Response: Leveraging AI to Safeguard the Benefit System

ClaimM&
Shres New
A claimant begins
a part—time
job.
Emptogmcne
Monikerlns
Deteces
Mismaech
The agent
identifies a
discnpancy in
income data.
Communication
Agene Sends
A message is sent to
Ute daimamt to
confirm income
chanse.
CWmM&
Updaecs
Deeaits
The claimant
updates their
income details in
respnse.
*isle
eatculaecs
Enkidomene
The risk agent
adjusts fuwre
payments based
on ngw data.
Owrpasmene
Occurs
The system
successfully
prevents any
overpayment.

Traditional system: Waits months, detects fraud, issues overpayment notice, demands repayment.
Agentic AI: Prevents the overpayment entirely, no need for recovery or appeals.

How can we make this safe. Balancing A1 autonomy with human oversight in fraud detection. Controls Fairness Compliance A1 Autonomy Protects data, ensures resilience Prevents bias, explainability Ensures adherence to regulations. 

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Balancing A1 autonomy with
human oversight in fraud
detection.
Controls
Fairness
Compliance
A1 Autonomy
Protects data,
ensures resilience
Prevents bias,
explainability
Ensures adherence
to regulations

Expected ROI: Strategic Value of AI Implementation. 

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Expected ROI: Strategic Value of AI Implementation

Projected Financial Impact The Department for Work and Pensions (DWP) estimates that its AI-driven fraud detection initiative will generate £9 billion in savings by 2027/28  An additional £600 million is expected through collaborations with banks and third-party data providers
 Separately, a more conservative estimate from DWP’s annual accounts suggests £1.6 billion in savings by 2030/31 from machine learning and analytics alone 
 Operational Efficiency Gains AI systems have already reduced 60,000 hours of manual work by automating claim analysis 
 With an 89% accuracy rate, AI tools can flag high-risk claims for human review, streamlining fraud investigations

Reforming Welfare Oversight: Tackling Fraud and Error with AI

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