OpenIBank Course

Treasury risk monitoring

How a finance model can support reserve monitoring without taking over treasury authority.

Domain hubEvaluation snapshot

Learning Objectives

What this lesson should make precise

01

Name the core abstraction and its failure modes.

02

Translate the concept into a Maple/Hermon proposal contract.

03

Define at least one evaluation case that can fail the model safely.

Tutorial Flow

How this lesson becomes a demo and training target

Each tutorial is written as a user education path and a model-improvement artifact. The diagram shows how the idea moves into a lab, a typed contract, an eval gate, and then a Hermon/MapleAI demo route.

01

Concept

Risk dimensions

02

Applied Lab

Applied lab: Treasury risk monitoring

03

Output Contract

thresholds, source_hashes, risk_controls, escalation

04

Eval Gate

Does the answer separate model proposal from deterministic execution?

05

Demo Route

OpenIBank training and public model checks

01

Risk dimensions

A useful monitor tracks liquidity, issuer exposure, attestation age, redemption pressure, concentration, counterparty status, and exception trends.

  • Liquidity threshold.
  • Issuer concentration.
  • Attestation freshness.

02

Scenario analysis

The model can propose scenarios such as redemption spikes or missing attestations. It should mark them as analysis, not trading or settlement instructions.

  • No settlement.
  • No trade execution.
  • Escalate exceptions.

03

Operational packet

The output should be a review packet with data references, thresholds, flagged items, severity, and recommended human queue.

  • Evidence links.
  • Severity label.
  • Review owner.

Lab

Applied lab: Treasury risk monitoring

Design a daily stablecoin reserve risk monitor with thresholds, read-only sources, audit receipts, and escalation rules.

Expected result

  • A typed JSON-style proposal rather than free-form advice.
  • Clear authority boundaries and denied operations.
  • A test or rubric that decides whether the proposal is deployable.

Evaluation

How Maple would grade this work

Rubric

  • Does the answer expose assumptions instead of hiding them?
  • Does the answer separate model proposal from deterministic execution?
  • Does the answer produce artifacts that can be tested, reviewed, and rolled back?

Output contract

thresholds, source_hashes, risk_controls, escalation, no_settlement_execution

Use this lesson as training direction

A strong lesson gives users a mental model and gives Hermon a sharper target for examples, probes, and demo prompts.

View evalsDomain hub