Advanced Measurement Approach in Practice and Theory

The advanced measurement approach is a game-changer for businesses and organizations. It's a data-driven method that helps you measure what matters most to your company.

By using advanced metrics, you can gain a deeper understanding of your customers' needs and behaviors. For example, you can use metrics like customer lifetime value (CLV) to determine the revenue potential of each customer.

Advanced measurement approaches also help you identify areas for improvement and optimize your processes. In practice, this means using techniques like regression analysis to analyze the relationship between different variables.

By applying these advanced measurement techniques, you can make data-driven decisions that drive real results.

In an advanced measurement approach, it's essential to consider the complexity of the system being measured. This is where the concept of "system thinking" comes in, which involves analyzing the relationships and interactions between different components.

The key is to identify the critical factors that affect the system's behavior, such as the number of variables involved, which can range from a few dozen to thousands. This requires a shift from traditional measurement methods to more sophisticated approaches.

One approach is to use a "bottom-up" method, where measurements are taken at the individual component level and then aggregated to understand the system as a whole. This can be more accurate than traditional "top-down" methods.

The complexity of the system also determines the type of data collection method used, such as simulation, experimentation, or observation. For example, in a complex system with many variables, simulation may be the most effective method.

The measurement approach should be tailored to the specific system being measured, taking into account factors such as the system's size, complexity, and dynamics.

The regulatory framework for the advanced measurement approach (AMA) is governed by the Basel Committee on Banking Supervision.

The AMA is based on the IRB approach, which was introduced in 2006 as part of the Basel II framework.

Regulators have given banks the option to use the AMA as an alternative to the standardized approach for calculating risk-weighted assets.

The AMA is designed to be more risk-sensitive and accurate than the standardized approach, but it also requires more data and complex models.

The AMA uses a combination of internal and external data to estimate the probability of default and the loss given default for a bank's exposures.

Banks must also meet the requirements of the Pillar 2 framework, which includes the need to hold enough capital to cover potential risks.

The Business Indicator (BI) is a crucial component of the Advanced Measurement Approach. It's made up of almost the same Profit and Loss (P&L) items as the Gross Income (GI), but with some adjustments to improve consistency.

One of the main differences is how the items are combined. The impact of other operating capital requirements is changed from negative to positive to serve as a proxy indicator for operational loss exposure.

The BI structure has been adjusted to address several issues raised during the consultation. For example, overcapitalization of firms with a high net interest margin has been addressed by adopting a linear normalization ratio.

Dividend income is now included in the Interest component of the BI. This change aims to address the variation of dividend income treatment and ensure consistency in determining the BI.

The Services component has been modified to address the asymmetric impact on different business models. Specifically, the formula has been changed to: "max(Fee Income; Fee Expense) + max(Other Operating Income; Other Operating Expense)".

Here's a breakdown of the changes to the BI structure:

These adjustments aim to improve the accuracy and consistency of the BI as a proxy indicator for operational loss exposure. By addressing these issues, the BI can provide a more accurate picture of a bank's operational risk.

The Internal Loss Multiplier is a crucial concept in the Advanced Measurement Approach.

A bank with exposure at the average of the industry has an Internal Loss Multiplier equal to 1. This is the starting point for calculating the multiplier.

The Internal Loss Multiplier increases at a decreasing rate with the Loss Component. This means that as the Loss Component increases, the multiplier will also increase, but at a slower rate.

The Internal Loss Multiplier is confined by approximately 0.541. This is the maximum value the multiplier can reach.

The committee has proposed that banks use their internal losses to calculate the Loss Component. This will provide a more accurate reflection of operational loss exposure.

A minimum of five years of good quality loss data is required to calculate the Loss Component, especially in the transition period.

The SMA works differently within a group of companies. SMA calculations use consolidated BI figures at the consolidated level to net out intragroup expenses and incomes.

At the subsidiary and sub-consolidated levels, SMA calculations are used differently. The bank uses only losses incurred in SMA calculations when BI figures reach the second bucket.

The subsidiary will only calculate SMA capital by applying 100% of the BI component if the bank belonging to bucket 2 or higher meets the standards and is from the subsidiary level.

Data collection and treatment are crucial aspects of the Advanced Measurement Approach (AMA). A bank's internal loss event data must be based on a 10-year observational period, with the exception that good data are unavailable for more than five years.

To ensure accurate data collection, banks must have documented procedures for the identification, collection, and treatment of internal loss data. This includes mapping historical internal loss data into relevant supervisory categories, as defined in Annex 9 of the Basel II accord.

Banks must also collect information about the reference dates of operational risk events, including the date of occurrence, date of discovery, and date of accounting. Additionally, they must collect information on recoveries of gross loss amounts and descriptive information about the drivers or causes of the loss event.

A bank's internal loss data must be comprehensive, capturing all material activities and exposures from all appropriate subsystems and geographic locations. The de-minimis gross loss threshold for internal loss data collection must not be higher than €10,000, except when a bank first moves to the SMA, in which case a threshold of €20,000 is acceptable.

Here is a summary of the key requirements for data collection and treatment:

The de-minimis gross loss threshold for internal loss data collection must not be higher than €10,000.A bank's internal loss data must capture all material activities and exposures from all appropriate subsystems and geographic locations.Banks must collect information on recoveries of gross loss amounts and descriptive information about the drivers or causes of the loss event.li>The de-minimis gross loss threshold for internal loss data collection can be €20,000 when a bank first moves to the SMA.

By following these guidelines, banks can ensure that their data collection and treatment processes meet the requirements of the AMA and provide accurate and reliable information for regulatory purposes.

Proximus, a Belgian telecommunications company, uses the Advanced Measurement Approach (AMA) for operational risk measurement and management. This approach is also used by the DB Group for calculating regulatory capital requirements for Operational Risk on a global level.

The Advanced Measurement Approach has been adopted by several banks, including the Bank and ANZ New Zealand, for determining regulatory capital requirements for operational risk. ANZ New Zealand uses this approach in accordance with BS2B.

The Advanced Measurement Approach is not limited to banks, as it is also used by the DB Group across all divisions and legal entities. This approach has been approved by regulatory bodies, such as APRA, which released the final standard for the calculation of operational risk, APS 115, in 2019.

Proximus relies on the Advanced Measurement Approach (AMA) methodology for its operational risk measurement and management.

The Advanced Measurement Approach is used for calculation of economic capital and is also used by DB Group for risk management purposes on a global level.

ANZ New Zealand uses the Advanced Measurement Approach for determining its regulatory capital requirement for operational risk.

The Bank adopted Operational Loss Data Reporting Format from the Loss Data Methodology Document for collection of Loss Data, which will enable the Bank to eventually ease the transition to Advanced Measurement Approach for Capital Calculation.

OSFI approved the Bank to use the Advanced Measurement Approach (AMA) and to calculate the majority of the retail portfolio credit RWA in the U.S. Retail segment using the Advanced Internal Ratings Based (AIRB) approach.

Here are some examples of companies using the Advanced Measurement Approach:

APRA released the final standard for the calculation of operational risk, APS 115, which replaces the current Advanced Measurement Approach with the Standardised Measurement Approach, excluding the loss component.

The Business Indicator (BI) structure is a crucial tool for evaluating bank performance. It's modified to address overcapitalization issues in high-fee banks by accounting for only 10% of fees that exceed 50% of the unadjusted BI.

The BI structure for high-fee banks takes into account the absolute value of net fee income as a floor to avoid unintended capital reductions. This is done to prevent overcapitalization.

The BI structure modification for high-fee banks involves netting and adding the absolute value of financial and operating lease income and expenses into the interest rate component. This is a key consideration in evaluating bank performance.

The business income is included in the interest component of the BI. This is an important aspect of the BI structure modification for high-fee banks.

Here are the key modifications to the BI structure for high-fee banks: