In this class, Olga Torres, CTO at Pirani, will show you how to automate and strengthen this process. Will guide you through the essentials of calculating capital requirements and how automation through risk management software can make this process faster, more accurate, and far easier to manage.
Is the amount of financial resources a bank or financial institution must hold to absorb losses that come from failures in people, processes, systems, or external events.
Key reasons:
1. Avoids insolvency during high-impact events.
2. Improves risk culture and operational discipline.
3. Supports long-term strategic decisions.
4. Strengthens regulatory compliance.
5. Enhances market credibility.
Key pillars:
● Pillar 1 – Minimum Capital Requirements
● Pillar 2 – Supervisory Review Process
● Pillar 3 – Market Discipline / Transparency
|
Approach |
Full Name |
How It Works (Simple Explanation) |
|
BIA |
Basic Indicator Approach |
Uses gross income as a proxy for operational risk. Capital = 15% × 3-year average gross income. |
|
TSA |
Standardised Approach |
Applies different percentage factors to income across business lines. |
|
AMA |
Advanced Measurement Approach |
Uses internal models based on historical losses, scenarios, and internal controls. |
|
SMA |
Standardised Measurement Approach |
Combines the Business Indicator Component (BIC) with the Internal Loss Multiplier (ILM). Capital = BIC × ILM. |
|
Approach |
Data Required |
Pros |
Cons |
|
BIA |
Only financial statements (gross income). |
Simple, fast, very low effort. |
Not risk-sensitive; income does not reflect operational risk. |
|
TSA |
Financial statements + business line allocation. |
More risk-sensitive than BIA; structured. |
Still income-based; weak link to real losses. |
|
AMA |
Large internal loss database, scenario analysis, internal models. |
Highly risk-sensitive and tailored. |
Very complex and costly |
|
SMA |
Financial statements + 10 years of operational loss data. |
Balanced, transparent, globally adopted; rewards strong data quality. |
Requires disciplined loss reporting and clean data. |
Data Quality Requirements:
Process Requirements:
Technology Requirements: