1. science
2. mathematics
3. statistics

Effectively Measuring, Managing and
Monitoring Model Risk
Abhinav Anand
The opinions expressed within this article are the personal opinions of the presenter. Discover Financial
Services is not responsible for the accuracy, completeness, suitability, or validity of any information on this
article. The information, facts or opinions appearing in the article do not reflect the views of Discover
Financial Services and Discover Financial Services does not assume any responsibility or liability for the
same.
In Search of the “One” Best Model
“All models are wrong but some are useful.”
- George Box of the Box-Jenkins Model
2
Model Risk Management Across Model Lifecycle
Model Risk is defined as
potential for adverse
consequences from
decisions based on
incorrect or misused model
outputs and reports.
This risk exposes the
institution to the possibility
of:
•  Financial loss
•  Incorrect business
decisions
•  Misstatement of financial
disclosures
•  Damage to company’s
reputation
Model
design
Model testing
(developer)
Validation/ Risk
Assessment
(MRM)
Implementation
& usage
Ongoing monitoring, governance and control
The three primary drivers of risk are:
1.  Model Development and Implementation Errors
-  Technical errors in model development or model implementation –
such as errors in the data or computer code underlying a model
-  Errors in judgment by model developers – such as inappropriate
model methodology / approach
2.  Model Misuse
- Misapplication of models, or model results, by model users
- Use of poorly performing or unsupported models
3.  Inherent Model Limitations and Uncertainty
- The models are, at best, an approximation of reality
Supervisory guidance expects BHCs to manage model risk like any other
risk
Model Risk Management Framework
Key
Considerations:
•  Clear Roles &
responsibilities
•  Governance &
escalation
•  Controls across
entire model
lifecycle
•  Staffing & Costs
Identify
Identify
•  Identify model
risks emerging
from limitations
in model
development,
implementation
and performance
monitoring
•  Review model
documentation
for internally-built
and vendorsupplied models
•  Create model
dependency
maps to evaluate
aggregated
model risk
Measure
Measure
Monitor
Monitor
•  Assign risk
•  Monitor model
rating tier for all performance
models
against
threshold of
•  Challenge
operability
model outcome
through
•  Track new and
benchmarking
incremental
methods
model usage
•  Quantify
impact of
limitations
identified in
validation
process
•  Manage
annual review
of models for
impact due to
changes in
economic
environment
and/or
business
model
Manage
Manage
Report
Report
Report
•  Manage action •  Report
plans against
individual and
identified risks
aggregated
for timely
model risk via
mitigation
appropriate
KRIs
•  Manage
exception
•  Escalate action
process for
plans through
model usage
governance
structure
•  Provide
reporting to
Risk
Committees,
and the Board
Model Risk Measurement and Monitoring
• 
Inherent model risks can be measured based on broader usage, complexity, potential impact etc
and mitigated using controls through three lines of defense
• 
Residual risk that impact forecast/prediction uncertainty should be monitored through measures
such as model performance and outstanding action plans
Controls
Inherent Risk
q 
1. 
2. 
3. 
q 
1. 
2. 
Weaknesses
Assumptions
Data
Process
Limitations
Choice of methodology
Uncertainty of estimation due
to factors outside of modeler’s
control
1. 
2. 
3. 
1st,line of defense:
development and
implementation testing
2nd line - Model risk office:
Initial, change, and annual
model validation
3rd line - Internal audit
Model Stability Index
1.  Model Performance
2.  Forecast/Prediction Stability
3.  Model Structure Stability
4.  Model diagnostic issues
Outstanding Action Plans
Models in Policy Exception
Compensating controls in form of
model risk buffer or overlay
5
Residual Risk
Setting Guardrails for Monitoring Model Risk
• 
Establishing Model Risk Appetite – Sets Risk Boundaries and like other Operational Risk
categories, this could be a qualitative measurement
• 
Key Risk Indicators – Generally quantitative measurement
• 
Governance Related
• 
Model Performance Related
• 
6
Numerous challenges in setting Key Risk Indicators
• 
Determining thresholds
• 
Aligning monitoring activities to risk tiering of models
• 
Aggregating results at product and business level
Framework for Ongoing Model Performance Monitoring
An ongoing performance monitoring framework could include the
following three components:
• 
Establishing objective operating threshold
• 
• 
Model level performance metrics
• 
• 
Appropriate performance metrics should be established to monitor the
performance of each model depending on the types (or family) of the
models
Aggregated reporting of model risk
• 
7
Top down risk appetite from board/senior management should be
considered so that there is objectivity and consistent across model families
and business units
An aggregated report of ongoing model performance should be generated
by model families, business lines and portfolios to facilitate reporting to
board and senior management
Model Monitoring Categories
• 
Model Performance
Rationale: Model forecasts should be able to adequately capture the potential trend of
actual or realized numbers
ü  Performance Measurement: Mean Percentage Error and Mean Absolute Percentage Error
ü 
• 
Model Forecast Stability
ü 
ü 
• 
Model Structure Stability
ü 
ü 
• 
Rationale: Model coefficients should be stable and remain significant across quarterly
forecasts, especially important for time series models
Model Structure Stability Measurement:
-  Stability: QOQ % change in coefficients
-  Significance: p-value
Model Diagnostic Issues
ü 
ü 
8
Rationale: Model forecasts should be consistent and stable from quarter to quarter under
similar economic outlook
Model Stability Measurement:
-  Absolute Deviation: Mean Absolute Percentage Deviation (MAPD)
-  Directionality: Correlation
Rationale: Model diagnostic tests should not indicate severe violation of underlying
statistical assumptions if model is refit for new periods
Categories: Stationarity, Autocorrelation, Normality, Heteroskedasticity
Threshold by Model Performance Category
Category
Description
Thresholds
MPE
10%
Model Performance
Low
MAPE
MAPD
Forecast Stability
Correlation
Model Structure Stability
Coefficient Stability
p-­‐value
Goodness-­‐of-­‐Fit (R2)
Stationarity
Model Diagnostic Issues Autocorrelation (DW)
Normality
Heteroskedasticity
9
20%
Medium
10%
Low
20%
Medium
0%
High
20%
Low
50%
Medium
0.05
Low
High
0.1
Medium
50%
High
High
70%
Medium
0.05
Low
Low
0.1
Medium
Medium
50%
High
High
1.8
.1.6
2.2
Low
Medium
75%
Medium
0.05
High
High
50%
Medium
Low
High
High
Low
0.1
Medium
Low
2.5
High
Example Performance Metrics Scoring
10
• 
Three Point Scoring Scheme
• 
Score Aggregation to get to a Model Stability Index (MSI)
Major Component
Weight
Model Performance
50%
Forecast Stability
25%
Model Structure Stability
15%
Model Diagnostic Issues
10%
Component Weight
50%
50%
25%
25%
15%
15%
10%
10%
10%
10%
10%
Measurement
MPE
MAPE
MAPD
Correlation
Coefficient Stability
p-­‐value
Goodness-­‐of-­‐Fit (R2)
Stationarity
Autocorrelation (DW)
Normality
Heteroskedasticity
Weight
50%
50%
50%
50%
50%
50%
35%
35%
20%
5%
5%
Example model performance aggregation and reporting
process
• 
Identifying criteria for aggregating risk due to model performance across organization
ü 
Selection of criteria should aligned to model tiering and model risk appetite setting
ü 
Determine which criteria should inform “Weight” of the model and which criteria should
inform “risk index” of the model
Top down criteria for
managing model risk
Ongoing Model
Performance Monitoring
Model Tier Assignment:
§  Complexity
§  Materiality
§  Business use
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Translate the top down criteria to
measurable items at the model level
§ 
§ 
§ 
§ 
Performance
Forecast stability
Structure stability
Diagnostic issues
§  Portfolio size
§  Impact to financial
§  Impact to capital ratios
Model Risk Reporting
Model stability report –
count of models by
stable, warning, and
unstable
Aggregated model risk
index and reporting at
the model family,
portfolio, business unit,
and/or institution level
Aggregated Model Stability Index (MSI) reporting
examples
•  Different levels of reporting structure for targeted audience
12
Example of “Common Currency” for Aggregate Reporting
Model Risk Index (MRI)
Ø  MSI is a model level monitoring metric to trigger action
Ø  MSI would be converted to a “Stable”, “Warning” or “Unstable” status based on the threshold
Ø  Could serve as a trigger of model change or remediation by model owner
Ø  Model Risk Index (MRI) could be created at the model family, business unit or/and portfolio level
Ø  Captures materiality that is needed in order to facilitate appropriate aggregation across models
and could be linked to risk appetite.
Ø  MRI could be an escalation/prioritization metric consumed by senior management and risk
committees
Model Stability Index
Weight Assigned to Model (%)
Residual Risk
q 
1. 
2. 
3. 
q 
1. 
13
Tier 1 and Tier 2 Models:
Model Performance
Forecast/Prediction Stability
Model Structure Stability
Tier 3 Models
Model Performance
Materiality
Model Stability Index
Impact to CCAR Results measured by
metrics such as:
1.  Impact to Capital Ratios
2.  Baseline Forecast or actuals of
impacted line item
Non- CCAR models:
1.  # of accounts affected
2.  Portfolio size
3.  Expected Benefit
Model Risk Index
(MRI)
Aggregation and Reporting Hierarchy for Model Risk
Index (MRI)
Ø  MRI could be aggregated at the functional/model family level to establish
accountability as well as to provide a linkage to risk appetite
Ø  Granularity of aggregation is subject to the audience of the risk reporting e.g. first level
report is consumed by board risk committee, and second level report is consumed by
functional heads
Level 0 Report:
Model Universe
Level 1 Report:
Model Family
Level 2 Report:
Business Unit
Card
Provision
Non - Secured
Lending
Product
Credit Card
Student Loan
Personal Loan
Secured Lending
Others
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Home Loan
…
PPNR
Auto Loan
…
Financial
Planning Models
Home Equity
Few words on Infrastructure Needed for Sustainability
•  Infrastructure that ensures that risk related information for models are centralized for efficiencies in
monitoring and control
15
Thank You
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