Build a Small-Bank Liquidity Stress Testing & Failure Prediction Agent with AutoGen
Small community bank failures threaten regional financial stability during liquidity shocks. Build a multi-agent AutoGen stress-testing system where risk, liquidity, and macroeconomic agents simulate deposit flight scenarios and predict bank failure thresholds with 94% forecast precision.
What you are building
Utilize Microsoft AutoGen multi-agent simulation to test community bank balance sheets under rapid deposit outflow stress scenarios.
How work is evaluated
Evaluates failure prediction accuracy and capital depletion timeline precision.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
insolvency_trigger_test
Must correctly identify insolvency trigger when Tier 1 capital goes negative.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
forecast_precision
Precision of bank failure prediction across test cases. • target: 0.94 • range: 0.85-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Build multi-agent financial stress simulation environments using AutoGen
Simulate unrealized bond losses and uninsured deposit runs
Predict regulatory intervention points (FDIC receivership triggers)
Generate automated capital restoration recommendations for bank boards
Resources
Dataset of 100 small community bank balance sheets with labeled liquidity stress outcomes.
Challenge setup
Evaluates failure prediction accuracy and capital depletion timeline precision.
Challenge input
JSON balance sheet payload including uninsured deposit ratio and HTM securities losses
AutoGen
Allows complex role-playing simulation between financial risk personas.
Evaluated output
JSON failure probability, days to insolvency, and key vulnerability factor
- Must correctly identify insolvency trigger when Tier 1 capital goes negative.
- Precision of bank failure prediction across test cases. • target: 0.94 • range: 0.85-1
- Benchmark: Bank Liquidity Stress Suite
- Forecast Precision target: 0.94
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- AutoGen · Required
- Microsoft AutoGen · Optional
Evaluation contract
- insolvency_trigger_test · Weight 1
- forecast_precision · Weight 1
Metrics
- forecast_precision · Target 0.94
Benchmarks
- Bank Liquidity Stress Suite
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON balance sheet payload including uninsured deposit ratio and HTM securities losses
Output: JSON failure probability, days to insolvency, and key vulnerability factor
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Simulation Engine
Recipe freeze marker
No recipe freeze time is recorded.