ALM-Governance-Defizite & Fehlerhafte Zinsrisiko-Modellierung
Definition
ECB published supervisory findings (2025 speech) revealing that 'almost every time we take a closer look at behavioral models, we find significant deficiencies.' These gaps affect IRRBB and credit spread risk in banking book (CSRBB) calculations. Lack of integrated IT systems and robust data aggregation processes prevents senior management from making informed decisions. Deposits are not properly categorized by account type, depositor profile, or currency. During crisis periods (e.g., March 2023 events), instant payment channels and client mobility create unpredictable customer behavior that existing models cannot capture. Result: Interest rate risk mispricing, inadequate hedging decisions, and potential capital reserve violations.
Key Findings
- Financial Impact: Conservative estimate: 1–3% of net interest margin (NIM) lost annually due to IRRBB miscalculations = €10M–€50M for mid-sized German bank (assuming €500M average net interest income). Plus 60–120 hours/month in manual stress testing = €3,000–€6,000/month in analyst labor.
- Frequency: Continuous (daily ALM model recalibration required); quarterly (stress testing & SREP disclosures); annually (Pillar 2 ICAAP assessments)
- Root Cause: Deficient behavioral models for non-maturing deposits and prepayments; lack of granular deposit categorization; insufficient stress testing integration; poor governance data architecture; slow model adaptation to instant payment ecosystem
Why This Matters
This pain point represents a significant opportunity for B2B solutions targeting Banking.
Affected Stakeholders
ALM Portfolio Managers, Risk Officers, Treasury Analysts, Compliance/ICAAP Teams, CFO
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Financial Impact
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Current Workarounds
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Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
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