DXCFinancial ServicesDocument ProcessingFraud DetectionAI and Machine LearningComplianceCost ReductionCustomer ExperienceProductivity ImprovementRisk ReductionUndatedDXCDXC#5f249fFinancial ServicesEdinburgh, ScotlandNatWest Group needed to comply with the UK's image-exchange check-clearing legislation while still processing very high daily check volumes quickly, accurately, and cost-effectively, and it also needed stronger fraud…50 percent reduction in check-clearing costsArchived · Mar 14, 20265Document Processing|Fraud Detectionautomated fraud detection software · desktop scanners · new software platform02026-03-145
NatWest Group needed to comply with the UK's image-exchange check-clearing legislation while still processing very high daily check volumes quickly, accurately, and cost-effectively, and it also needed stronger fraud detection and a better audit trail as paper-based processes declined.
↗66 percent reduction in the clearance cycle time of a check
↗about 200,000 checks is processed in a 2-hour window with an accuracy level of 99.9 percent
↗identified an average of 350 fraudulent checks per month
Reported impact
Business outcome
NatWest Group lowered check-clearing costs and cycle time, improved accuracy and customer-service speed, strengthened compliance and auditability, and used AI-based fraud detection to stop fraudulent checks and reduce potential fraud losses.
Compliance
Cost Reduction
Customer Experience
Productivity Improvement
Risk Reduction
Solution
NatWest Group and DXC transformed check clearing from a paper-centered process into a digital image workflow. DXC introduced new desktop scanners in its processing centers and migrated the bank onto a new software platform used for both in-clearing and out-clearing, while running paper and image-based processes in parallel during the transition. The new flow captures check images digitally, exchanges them instead of physical paper, and applies automated fraud-detection software based on machine learning and artificial intelligence to identify suspicious items. DXC also rationalized the clearing platforms, retained distributed capture sites for courier delivery, and improved the audit trail. The result is a compliant operating model that reduces physical footprint, speeds clearing, improves accuracy, and gives the bank a lower-cost path for ongoing check processing and newer capture channels.
Data flow
Checks are captured as images through desktop scanners in DXC processing centers and passed into the new software platform for digital clearing. The platform applies automated fraud-detection software to the images, routes the resulting items through in-clearing and out-clearing workflows, and produces a stronger audit trail while funds are made available more quickly.
Solution architecture
3 components · 3 layers
Ingest
desktop scannersCapture check images in DXC processing centers so the bank can exchange digital images instead of physical paper.
Compute
automated fraud detection softwareAnalyzes captured check images with machine learning and AI to identify potentially fraudulent checks.
Orchestration
new software platformRuns the digital in-clearing and out-clearing workflow for check images and supports the migrated image-exchange process.
Architecture clues
DXC managed paper and digital image-based processes simultaneously during the migration period.
Distributed capture sites were retained so the bank's couriers could still deliver work efficiently.
The bank later implemented mobile capture and was extending the model to corporate and branch capture.
The same platforms were rationalized for both out-clearing and in-clearing of checks.
Evidence from the source
"A full day’s worth of checks — on average about 200,000 — is processed in a 2-hour window with an accuracy level of 99.9 percent."
"Specifically, the new industry model delivered a 50 percent reduction in check-clearing costs and a 66 percent reduction in the clearance cycle time of a check."
"The automated fraud detection software has identified an average of 350 fraudulent checks per month, saving the bank more than £50 million per year in potential fraud losses."
"The new system also enables NatWest Group to do a better job of responding to check-related customer questions through an improved audit trail."
"This enabled NatWest Group to reduce its overall footprint for image-capture functionality, increase speed and accuracy, and enable automated image fraud detection based on machine learning and artificial intelligence (AI) technologies."
Review how this reference was scored and trace each normalized claim back to its documented source.
01
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value100/100
Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
4 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability25/25
12 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
By taking the best of that learning and making it available to all 60,000 colleagues across the group, this structured programme is designed to embed critical skills across the organisation.
Source type
official company website
Supports
Company size
Date evidence
First archivedMar 14, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
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