Case Study
AI-Assisted Fraud Triage in Digital Payments
Organisation: Riverbank Payments Lab (Exemplar)
A fintech operations team used AI scoring to prioritise suspicious transactions while preserving human adjudication.
Overview
Overview
- Sectors
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Financial Services
- Competencies
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C19 C23 C54
- Duties
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D7 D9 D13
- Audience
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Post-16 , FE/HE , Adult learners
- Skills areas
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Responsible AI practice , Governance and risk management
- Routing
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Schools , Colleges
People and engagement
- People
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- Industry mentor lead - non-traditional pathway highlighted in delivery notes
- Engagement
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- Delivery mode agreed during brokerage routing
Curriculum
- AI Skills for Business competency-linked activity
The challenge
What needed to change
Analysts were overwhelmed by alert volume, with many low-risk events consuming response capacity and delaying high-risk reviews.
The approach
How AI was introduced
The partner introduced risk-tiering models and policy thresholds, with mandatory analyst validation before account action.
The impact
What changed in practice
Response focus improved for high-risk cases, and audit trails made intervention decisions easier to explain to compliance teams.
Case Narrative
This case foregrounds responsible use: AI output is treated as decision support, not a final decision engine. The operational design includes escalation guidance, exception handling, and regular checks for unfair impact across customer groups.
In classroom settings, it can be used to explore how confidence thresholds change workload and risk posture.
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