Deutsche Bank's AMA Model

39 Pages·2008·0.3 MB·English

by  Michael Kalkbrener

Deutsche Bank’s AMA Model
Challenges for Operational Risk Measurement and Management
Federal Reserve Bank of Boston, May 14, 2008
Michael Kalkbrener
LRC, Risk Analytics & Instruments
Risk Analytics & Instruments
Agenda
1. AMA at DB
2. Main components of an LDA model
3. Appendix: model validation
For more information:
F. Aue and M. Kalkbrener (2006).
LDA at work: Deutsche Bank’s approach to quantifying operational risk.
J. Operational Risk, 1(4), 49-93.
Deutsche Bank’s AMA Model · page 2
Risk Analytics & Instruments
AMA Model Development at Deutsche Bank
Timeline
1999 Systematic collection of loss data
2000 Economic capital with LDA
- Top-down model: loss distribution at Group level, capital allocation with risk
indicators
- Internal and external loss data
- Qualitative adjustment with Incentive Scheme
2001 AMA project
2002 Development of AMA model
2003 Implementation of prototype
2004 EC test calculations with AMA model
2005 Official EC calculation with AMA model (starting Q2 05)
2006 Implementation of production engine
AMA application submitted in September
2007 Regulatory approval
Deutsche Bank’s AMA Model · page 3
Risk Analytics & Instruments
Risk Capital Allocation
AMA at DB:
(before QA) (via model)
Calculation flow Group-Level
Correlation /
Group-Level Aggregate distribution
Diversification
RC before QA
Net Losses
Business Divisions Business Divisions
Insurance
s
e
p
Gross Losses
y X
T
t
n
e
v
E Qualitative
Adjustment
(BE&ICF)
Frequency/ Severity
Dependency
Business Divisions
Scenario
ORX analysis
Cons. HILFE Staging dbIRS
Data External area Internal RC after QA
Data Data
Deutsche Bank’s AMA Model · page 4
Risk Analytics & Instruments
DB’s Business Line / Event Type Matrix
Business Lines
Basel Level 1 Internal Event Types
BL1 BL2 BL3 BL4 BL5 BL6 Group
Internal Fraud
Fraud
External Fraud
Damage to physical assets
Infrastructure
Business disruption …
Clients, Products, Clients, Products,
Business Practices Business Practices
Execution, delivery, Execution, delivery,
process management process management
Employment practices, Employment practices,
workplace safety workplace safety
(cid:132) Design criteria (cid:132) Treatment of losses that cannot be
– comparable loss profile assigned to a single cell
– same insurance type – Group losses
– same management responsibilities – Split losses
– availability of data
– relative importance of cells
Deutsche Bank’s AMA Model · page 5
Risk Analytics & Instruments
Agenda
1. AMA at DB
2. Main components of an LDA model
3. Appendix: model validation
Deutsche Bank’s AMA Model · page 6
Risk Analytics & Instruments
RC before QA
Data
Group-Level Correlation /
Aggregate distribution
Diversification
Net Losses
Business Division
e
p Insurance
y
X
T
t Gross Losses
n
e
v
E
Frequency Severity
Scenario
ORX analysis
Cons. HILFE Staging dbIRS
Data External area Internal
Data Data
Deutsche Bank’s AMA Model · page 7
Risk Analytics & Instruments
Data for Modelling Loss Distributions
Data sources
(cid:132) Internal loss data
(cid:132) Consortium data
(cid:132) Commercial loss database
(cid:132) Scenarios
Internal loss data is the most important data source
(cid:132) Each firm’s operational losses are a reflection of its underlying operational risk exposure
(cid:132) Internal losses are used for
– modelling frequencies (exclusively)
– modelling severities
– estimating correlations
Motivation for using external data and scenarios
(cid:132) Additional information on severity profile, in particular on risk of unexpected losses (tails
of severity distributions)
Deutsche Bank’s AMA Model · page 8
Risk Analytics & Instruments
Creating a Relevant Loss Data Set
Exclude non-
fin. inst. and Exclude Loan Exclude DB
Map all insurance data Fraud data losses
OR losses
Public
to BLET
Matrix
Consortium
BL review and
approval of
external points
DB Access To be used for
Database scenario analysis
RC
Engine
Scenarios are added as individual data points to relevant external losses
Deutsche Bank’s AMA Model · page 9
Risk Analytics & Instruments
Scenario Analysis Process and Methodology
OpVar
data
Existing RLD process for OpVar events is integral part of scenario analysis
Each relevant OpVar data point is considered a scenario
Relevant
OpVar
data
OpVar data is enriched to close gaps through following processes:
Divisional Expert driven process
Control function driven process (e.g. BCM, Outsourcing)
Regions for region specific events
Enriched
scenario
data
Same treatment of scenarios and OpVar data points in RC calculation
RC
Deutsche Bank’s AMA Model · page 10