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Risk tier

As explained in Financial AI in Practice

A category representing a level of assessed risk, such as low, medium, or high.

A risk tier groups transactions or accounts by model score so a financial institution can match fraud decisions to its risk appetite, business goals, operational capacity, and regulatory obligations. section 7.4.4

Example score bands connect assessed risk to different actions:

  • Low risk: scores below 0.2 may be auto-approved. section 7.4.4
  • Medium risk: scores from 0.2 to 0.7 may trigger step-up authentication or delayed analyst review. section 7.4.4
  • High risk: scores above 0.7 may lead to automatic decline or urgent human investigation. section 7.4.4

Thresholds are monitored and recalibrated as fraud rates, false-positive effects on customer goodwill, and strategic priorities change. Business rules and policy overlays can also affect decisions beyond the model score. section 7.4.4

How Financial AI in Practice covers it

How deep it goes
Defines example score bands, actions, and recalibration considerations without detailing a calibration procedure.
Examples
Uses low, medium, and high score bands to illustrate approval, authentication or review, and decline or escalation.
What it assumes
Assumes model scores are available and leaves their calculation unexplained.
What you can do after
Operate fraud decisions by routing scored transactions to approval, added authentication, analyst review, decline, or escalation.
How it connects
Connects fraud detection model scores with business rules, policy overlays, and human review.
Cover of Financial AI in Practice
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This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.