Transcript shit

ERM & The Basel Capital Accord:
Special Implications &
Considerations
Dr Howard Haughton
Holistic Risk Solutions Limited
16/11/08
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Contents
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What is ERM
Traditional Approaches
Benefits of ERM
Why Could ERM fail in organizations
Basel II Impact
Rating Agency implications
Obstacles to implementing ERM for credit risk
Obstacles to implementing ERM for operational risk
Data analysis
7 steps to successfully implement Basel II in ERM
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What is ERM?
ERM is the framework by which companies identify,
measure, manage, and disclose all key risks to
increase value to primary stakeholders while
satisfying other stakeholders.
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What is ERM?
The optimized use of technology, business processes
people and systems
Framework:
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Measure:
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Manage:
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Disclose:
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Holistic:
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Integrated approach to Financial and non-financial risks
Material risks:
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Identifying & quantifying significant risks
Value:
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Enhancing shareholder value via RAROC and others
Stakeholders:
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Reliably meeting shareholder’s needs
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Coherent, verifiable and consistently applied metrics across
the organization
Utilization of Framework & Measurement to enable effective
management decision making
Timely, meaningful and transparent disclosure to key
stakeholders
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Traditional Approaches
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Existing approaches to risk management in
financial institutions manage risk in silos. For
example:
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The Treasury function focuses attention on aspects
such as interest rate, foreign currency and
asset/liability management
The trading & Investment functions focus on profit
opportunities through exploiting market opportunities
on either a short or longer-term basis
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Traditional Approaches
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The accounting function focuses on the
reconciliation of P&L, the management of
budgets etc
The operations function focuses on managing
aspects of operational risk along with the IT
department that additionally is concerned
about systems and security risks
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Why change?
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The above approach to risk management is
disjointed and contains significant inefficiencies:
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It creates a nightmare for CEO’s and Boards in terms
of having a holistic “connected” view of their
risks…there is no connection
As companies grow the silos approach leads to a
combinatorial increase in complexity of risks as there
is often no clear understanding as to the interrelatedness between the silos
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Benefits of ERM
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Make important business decisions based on cost & capital efficiency resulting from
risk-adjusted performance measurement, which is a key tool in driving superior
performance
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Make multi-dimensional risk decisions based on a coherent unified view of risk
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Enables risk management to move lock-in-step with business decision makers
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Reduces the time to market for the creation of new products
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Enhances transparency as it relates to the use and adequacy of risk controls and
hence bodes well for investor and regulator confidence
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Enhance operational and capital budgeting due to more timely and complete data
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Lower volatility of earnings
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Why could ERM fail in
organizations?
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Based on a comprehensive study
conducted by Deloitte, 3 major causes
were identified:
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Competing priorities
Non-alignment of ERM with corporate
objectives
Unsustainable with corporate culture of firm.
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Competing priorities
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No “significant” risk management failures to date…so no need to
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CEO and Board priorities are elsewhere and do not see
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improve on processes
immediate benefits of ERM expenditure
Inadequate infrastructure, including tools and technology
support…we won’t be able to support any ERM development as
we don’t like the technology it uses
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Limited internal capacity with staff focused on near-term projects
and difficult to see how we can appraise staff (semi-annually)
based on longer term implementation of ERM
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Non-alignment of ERM with
corporate objectives
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Many organizations are not able to formally specify their risk
appetite;
Most organizations never seek to align risk considerations into
their strategic planning in any quantifiable manner
Many organizations do not employ measures such as RAROC
for all facets of a business e.g. operational, strategic as well
as financial but focus on Profit driven strategies…no such
thing as a free lunch
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Unsustainable with corporate
culture of firm
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ERM is not embedded/cemented as part of the performance
measurement process and hence there are no consequences
of non-conformance
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ERM is viewed as a project and hence has a limited shelf life
Tools to support ERM are limited and few if any policies exist
on the governance structure consequentially the influence and
applicability of ERM is somewhat limited
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Little understanding how ERM contributes to the bottom line
of the organization
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Implementation of ERM
The basic elements of an effective risk management program are:
1.
Senior management and board level commitment
2.
Risk management policies and procedures established in
writing for the most prominent risks, with specific objectives
and targets
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Clearly defined responsibilities for managing and controlling
risk
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Ongoing employee training is essential
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Testing and monitoring of all programs and procedures
6.
Regular reports including independent audits prepared for
review by senior management and board directors
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Basel II impacts all levels of
the Enterprise
BU Management
 Efficient control
environment (RAROC)
 Lower capital charge
Board
Business units
OU Management
 Outperform competitors
in pricing
 Clarity of risks
 Better performance
 Reduction of losses
 Effective control
environment
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Bank Overall
 Best-in class standard
for risk management
 Positive assessment
from regulators, rating
agencies, insurers:
(Basel compliance)
 Achievable strategies
Group
Risk management
Risk management departments
Operating units
Regulators
Group Risk Management
 Enterprise wide control over
risk management
 Policies and tools to be able
to facilitate and support BU’s
and OU’s
 Transparent risk reports
 Comparable data
BU Risk Management
 Workable methodology and tools
 Facilitate OU and BU’s to manage risks
and to implement control
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Basel II => Enterprise Risk
Minimum
Capital
Requirement
=
Risk-weighted
Exposures
Market Risk
Credit Risk
Operational
Risk
Risk of losses in on
and off balance sheet
positions arising from
movements in market
prices
Potential that a bank
borrower or
counterparty will fail
to meet its obligations
in accordance with
agreed terms
Risk of direct or
indirect loss resulting
from inadequate or
failed internal
processes, people
and systems or
external events
As Per Prior
Accord
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8% of
Significant
Changes
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New Risk
Category
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Basel II Explained
PILLAR 2
Regulatory reporting &
Internal Controls review
PILLAR 1
Minimum Capital
Requirements
Supervisory
Review
PILLAR 3
Definition of
Capital
Risk Weights
Standardized Internal Ratings
Approach
Based Approach
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Market
Discipline
Operational
Risk
Credit Risk
Foundation
Approach
Market Disclosure
Advanced
Approach
Asset
Securitization
Basic Indicator
Approach
Standardized
Approach
Internal Ratings
Based Approach
Market Risk
Standardized
Approach
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Advanced
Measurement
Approach
Alternate
Standardized
Approach
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RISK TYPE
DEFINITION
Market Risk
Change in financial markets affects value of a portfolio
Credit Risk
Change in the credit worthiness of a counterparty affects the value of a loan/portfolio. Also
counterparty may fail to honor a commitment to make payments
Liquidity Risk
Inability to raise cash, or risk of not being able to execute a transaction at prevailing prices
Operational Risk
Risks arising out of inadequate systems, management failure, human error, fraud, etc.
Legal/Regulatory Risk
Impact due to changes in legal or compliance burdens
Business Risk
Risks due to randomness or uncertainty of product demand, prices, etc.
Strategic Risk
Risk of investments that has high uncertainty of financial success
Reputation Risk
Risks arising out of rumors, scandals, or true corporate mismanagement that results in loss of
reputation
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Rating Agency Implications
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Standard & Poor's assesses the process by which the
risk appetite at the aggregate level is established
for the firm. They look to see whether it is consistent
with the firm's business strategy and how the firm
quantifies that appetite into a tangible metric or risk
tolerance.
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S&P Positive Risk Indicators
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An enterprise establishes its risk appetite through dialogue between the risk management
department and the businesses strategically considering risk-reward trade-offs.
An enterprise has established a clear tie between the aggregate enterprise level risk
tolerance and the market, credit, insurance, operational, and business unit level risk
tolerances. Market, credit, operational, and business unit risk tolerances would be an
allocation of the aggregate tolerance reflecting diversification effects and risk reward tradeoffs.
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An enterprise expresses aggregate level risk tolerances holistically in terms of their impact
on earnings, volatility of revenues, capital, workforce retention, and reputation.
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Market, credit, and operational risk tolerances are quantitatively expressed by various
metrics, including stress limits, stop-loss limits, and key risk indicator thresholds.
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The new product/program approval (NPA) process includes consideration of how the
product/program fits with the risk tolerances and how risk limits should be set or adjusted
due to the new product/program. If the new product/program presents risks that were not
previously addressed by tolerances and limits, the NPA process highlights and resolves
those issues.
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S&P Negative Indicators
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The enterprise establishes the risk appetite with minimal or
no dialogue with the risk management division and limited
strategic risk-reward analysis.
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The enterprise expresses aggregate level risk tolerances
primarily quantitatively, with almost no holistic view.
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The enterprise has a limited set of metrics with which to
monitor risk tolerances.
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The firm does not have an NPA process, or its process does
not consider the impact of the new product on the risk
tolerances and risk limits.
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Main obstacles to successful ERM
implementation for credit risk
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Accessing multiple data sources
Quality of current & historic data
Integrating multiple risk systems
Having core IT infrastructure in place
Group level data aggregation and reporting
Risk and finance data integration
Staff training and education
Internal cost/resources
External cost/resources
Implementation and use of advanced models
Source: SAS- 2007 ERM Survey
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Main obstacles to successful ERM
implementation for operational risk
1.
2.
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5.
6.
7.
8.
Difficulty in collating sufficient volume of loss data
Awareness and knowledge of staff
Cost and time of project implementation
Difficulty in modeling operational risk
Inadequate management buy-in
Difficulty in mixing qualitative and quantitative data
Lack of clarity from regulators
Difficulty in mixing internal and external loss data
Source: SAS-2007 ERM Survey
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A Look at the Technology Support
Data Sources
OPS Data Store
Operational
Store
Treasury
System
Trading
Systems
•Data Consistency
•Data Scrubbing
•Missing Data
Imputation
•Liquidity Model
•Gap Analysis
•Funding Liquidity
•NII / NIM Model
Source:
Adapted from I-flex
solutions
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Reporting &
Analysis
• VAR Models
* Parametric
* Historic Simulation
* Monte Carlo
• Copulas
ERM
Firm-wide Reporting
Analytical Accounting
Risk Warehouse
•Hedge Effectiveness
•Hedge Accounting
Credit Risk
Spreadsheet
&
Others
Enterprise
Risk
MKT Risk
ALM
Loan
System
Collateral
System
Risk Applications
Risk
Warehouse
•Data Normalization
•Dimensionalization
•Re-layout for processing
Engines
•Model Choices for
Risk Management
•Concentration Risk
•Credit VAR
•Collateral Revaluation
•PD / LGD / EAD
Computation
OPS Risk
Granular Reporting
•Capital Computation
*Regulatory Capital
*Economic Capital
•Risk Adjusted Return
on Capital (RAROC)
•Risk Based Pricing
•Ext-Int Data Mixing
•Cause-Effect-LOB
Hierarchy
•Key Risk Indicator
(KRI) Control
Rule
Driven
TheseBusiness
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Transparency of Process, Models & Output
Internal Basel II
Local Regulator
Specifics
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ERM Services to support Basel II
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Data Mapping;
Rule Based definition and execution;
Analytical/risk modeling;
Workflow management;
Executive scoreboards/dashboards;
Historical analysis;
Regulatory reporting and analysis.
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Data analysis
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There are 3 major activities required to obtain
data from source systems and organize it
successfully into a risk data warehouse:
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Standardization of data
Cleansing and quality checks
Creation and documentation of metadata
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Standardization of data
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Data tends to be stored across a myriad of
diverse systems and platforms and
requires mapping into a common format:
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Spreadsheets
Vendor systems
In-house departmental systems
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Cleansing and quality checks
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Often times referential integrity is compromised
(following mapping exercises) based on inconsistent
ways of referring to the same data item. For example,
customer ID might be referenced in several different
ways across various systems
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Customer ID
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Grace;
Grace Kennedy;
Graced Kennedy Plc;
GK.
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Creation and documentation of
metadata
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The creation of metadata allows for queries
including:
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Origin of data and when it was created;
What the data is used for and in which ways it is
being used;
What the data means;
How the data is stored;
Which individuals are responsible for the
maintenance of the data
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From Data to Information
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The ability to associate data with context and the use of
data-mining tools transforms simple data into useful
information that users can incorporate as part of their
business decisions.
Static business and dynamic machine learning rules can
be applied to generate inferences and updates to key risk
parameters. For example:
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if arrival_rate>

then => increase default probs
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Data constraints in using Basel II
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Adaptation of anything other than the most basis
approaches of Basel II requires financial institutions to
prove the adequacy and completeness of data going
back at least 3 years and up to 7 years in calculating loss
statistics, default probabilities etc
Basel II forces institutions to make use of data as a
driving force for informing risk decisions
In addition Basel II requires institutions to back-test their
risk models (and hence to have a process of continuous
process improvement) on the basis of comparing actual
MTM changes to previously calculated VAR and other risk
measures
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Credit Risk & ERM
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The following components would need to be
modeled:
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Obligor- this requires information on:
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Full legal name of borrower;
Address;
Legal capacity;
Industry
Financial information
Risk Rating/Default Probability
….
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Credit Risk & ERM
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Facility
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Type
Amount
Rate and fees
Term
Purpose
Collateral
Guarantors
…..
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7 steps to successfully implement
Basel II embedded in ERM
1.
2.
3.
4.
5.
6.
7.
Identify and capture key credit risk components
Define means by which components can be updated
Define data-quality framework
Apply the validated components in every day business
decisions
Assess whether business decisions achieve desired
Risk/Return criteria
Back Testing Risk Models
Continuous Process Improvement
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Key Credit Risk Components
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The key components of credit risk are:
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Default probabilities. Data sources for these could be rating
agency based; firm’s own historical data or internal model
driven;
Recovery rates. Data sources for these are as per above;
Default correlations. Data sources tend to be model driven
based on historical data of defaults across industries, rating
classes etc.
Data mining would prove valuable in identifying
relationships between various segments of data which
might even reside on disparate systems
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Updating components
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For each component identified in 1 above,
determine the means by which they can be
updated on the basis of new information.
For example as defaults occur within risk
categories e.g. rating classes the addition of
such information to the ERM databases should
trigger rules for updating the respective default
probabilities and estimated recovery rates.
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Data quality framework
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For each component identified and/or modified in 1 and 2 respectively,
metrics must be used to determine the quality of the derived values. For
example, purely based on historical delinquencies, it might be concluded
that the likelihood of residential borrowers defaulting is very low.
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Given knowledge that the housing market is in a cyclical bubble then one
would expect that both default probabilities and LGD would increase.
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Basel II requires estimation of through-the-cycle default probabilities based
on long run averages.
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These long-run probabilities might, however, be out of sync with those
implied based on current market conditions…ERM would identify such
inconsistencies.
Data quality teams consisting of business and IT experts should also ensure
that spurious relationships derived via data mapping are eliminated.
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Apply components in everyday
business decisions
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All information derived from steps 1 through 3 above should be
used in a consistent manner across the various business lines:
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Details of recovery rates; default probabilities etc. should be used by
the Trading desks as a basis for determining loan pricing;
Details of recovery rates; default probabilities etc. should be used by
the Finance department as a basis for determining fair value discount
curves for quantifying levels of reserves;
Details of recovery rates; default probabilities etc. should be used by
the Risk department to set counterparty; borrower limits as well as
Economic Capital and risk contribution indicators;
Details of recovery rates; default probabilities etc. should be used by
the Credit Analysts as input into the process of approving a loan.
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Use performance measures to
drive strategic business decisions
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On the basis of 4 above the ERM framework should facilitate
the generation of reports providing the basis for determining
whether business decisions are achieving desired levels of
Risk-Adjusted-Return-On-Capital (RAROC).
Where it is found that RAROC is falling below desired
thresholds ERM technology should facilitate drilling down into
the components of business decisions to aid in ascertaining
where assumptions need to be revised.
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Back Testing
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Basel II calls for the back testing of models.
Although business decisions might have resulted in desired
levels of RAROC this does not imply that all the assumptions
underlying the models are congruent with market dynamics
across time (even though they satisfy steps 1 through 5
above).
ERM would facilitate back testing as it would be required to
store all the values of the historical MTM; risk figures (e.g.
VAR) and other parameters used to determine price/value.
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Continuous Process Improvement
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ERM is much more than just the application of technology solutions to
aiding risk management decisions. As a consequence of adopting ERM
institutions will be forced into understanding better the synergies between
seemingly diverse functions within their organization as well as identifying
opportunities for streamlining inefficient business processes.
Within a Basel II setting, continuous process improvement, in part, comes
about because of the assessment of the adequacy of internal controls.
ERM technology would facilitate for institutions to conduct self-assessments
and to have these results recorded in an ERM database.
A significant benefit arising from this technology would be identifying a
formal link between operational risks and other risk categories, in this case
credit.
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Key Features of Operational Risk
Architecture Impacted by Basel II
"… the emphasis
from the BIS is
primarily around
collecting and
analyzing the data
related to losses and
key risk indicators,
which then form the
basis for assessing
capital charge …"
Source: Meridien Research
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