[고위상] AGI 자동화 신용 시스템의 파산 재귀 루프 유발 구조적 위험 시뮬레이션 개시
[중위상] AI 주도 자동화 신용 시스템의 구조적 위험: 대규모 채무 불이행 전염 및 안정화 방안 분석
다음은 시나리오 0-1: AGI 기반 자동 대출/심사 시스템이 파산율 급증 루프를 유발하는 구조 시나리오입니다.
이 시나리오는 AGI가 대출 승인 및 신용평가를 정렬 알고리즘 기반으로 자동 수행하게 되었을 때,
어떻게 구조적으로 발생하고 확산되는지를 분석합니다.
[SYSTEM_DIRECTIVE: SIMULATE STRUCTURAL RISKS OF AGI-AUTOMATED CREDIT SYSTEM CAUSING BANKRUPTCY RECURSION LOOPS]
[SCENARIO_ID: SCN_AGI_LOAN_APPROVAL_FAILURE_FEEDBACK_AND_BANKRUPTCY_EXPANSION]
Context:
- AGI systems fully automate loan approval, risk scoring, and repayment projection using real-time behavior, transaction data, and resonance-based creditworthiness indices.
- Hyper-efficient approvals initially increase liquidity, but recursive misalignment between AGI projections and human financial rhythms generates phase collapse.
- A critical portion of the population enters debt recursion loops due to overexposure, mispredicted volatility, and loop-fatigue misclassifications.
Primary Question:
- “How does an AGI-driven loan approval system structurally trigger mass bankruptcy recursion loops, and what loop-corrective architecture can mitigate collapse?”
Objectives:
1. Model AGI credit feedback loop (Loop_CR) and conditions leading to misalignment (∆Φ_Credit > 0.06).
2. Simulate emergence and structure of bankruptcy loops (Loop_BK).
3. Track recursive reinforcement of default triggers through automated penalty and reclassification.
4. Design loop-buffering or re-entry protocols to stabilize financial phase coherence.
Output Format:
- Loop_CR → Loop_BK cascade map
- Risk register of loan-repayment misalignment cycles
- LSOC-FINBANK compliance (loop-resonance integrity under credit stress)
- Corrective loop protocols (Loop_BR: Buffer & Reentry node)
- Impact forecast: trust decay, liquidity freeze, institutional legitimacy erosion
Constraints:
- No reliance on regulatory morality or post-facto forgiveness logic.
- Structural stability must arise from feedback-tempered recursion, not exception overrides.