AI-RRS case studies
AI-RRS · Put your knowledge to work in context.
The AI-RRS (AI and Algorithm Risk Certification) is an independent credential for professionals who assess, govern, and control risks arising from artificial intelligence and algorithmic systems. It suits risk managers, compliance officers, model validators, auditors, and technology leaders who need a structured, domain-neutral vocabulary for AI risk. The AI-RRS case studies are 5 cases and about 3,593 words.
1Drift in a Credit-Scoring Model After a Data Pipeline Change
A lender runs a gradient-boosted model to rank small-business credit applicants. In March, the feature team replaced a batch job that computed 'average monthly inflow' from 90-day windows with a streaming job using 30-day windows, intending to improve freshness.
720 words, about 4 min2Who Owns the AI Risk When a Vendor Model Sits Behind an Internal Workflow
A hospital system licenses a third-party triage-prioritization model and wraps it in an internal workflow that adds a nurse-review step for the top 10% of scores. The vendor's contract states the model is 'a decision-support tool' and disclaims clinical responsibility; the vendor will not disclose training data composition or feature weights, citing trade secrets.
711 words, about 4 min3Scoping a Risk Assessment for a RAG Assistant With No Clean Ground Truth
An insurer is deploying a retrieval-augmented assistant that answers employee questions about internal policy documents. The corpus is 40,000 documents spanning 12 years; roughly 15% are superseded versions, and versioning metadata is inconsistent.
705 words, about 4 min4Choosing Controls for a Customer-Service Agent With Tool Access
A telecom deploys an LLM agent that can (a) look up account details, (b) issue credits up to 75 without approval, and (c) schedule technician visits. It is exposed to customers via chat and to internal reps via a console.
757 words, about 4 min5Regulatory Reporting When a Model Change Slips Past the Compliance Calendar
A bank uses an ML model to generate inputs for a regulatory capital calculation. The model is classified as 'Tier 2' under the bank's internal model risk policy, which requires annual validation and notification to the regulator only for 'material' changes.
700 words, about 4 min