Credit Union Regulatory Guidance Including: NCUA, CFPB, FDIC, OCC, FFIEC

Samantha: Hello, this is Samantha Shares.

This episode provides a comprehensive
summary with extensive direct

quotes from the OCC's Comptroller's
Handbook booklet "Model Risk

Management" released as Version 1.0,

August 2021.

Complex credit unions can improve
operations via the principles

discussed in the handbook.

This podcast is educational
and is not legal advice.

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And now the comprehensive
summary with direct quotes.

Introduction and Purpose

The Office of the Comptroller of the
Currency's (O C C) Comptroller's Handbook

booklet, "Model Risk Management," is
"prepared for use by O C C examiners

in connection with their examination
and supervision of national banks,

federal savings associations, and
federal branches and agencies of

foreign banking organizations."

The booklet aligns with the principles
laid out in the "Supervisory

Guidance on Model Risk Management"
conveyed by O C C Bulletin 2011-12.

This booklet serves multiple purposes.

It is "designed to guide examiners in
performing consistent, high-quality

model risk management examinations,"
it "presents the concepts and general

principles of model risk management,"
and "informs and educates examiners about

sound model risk management practices that
should be assessed during an examination."

Additionally, it "provides information
needed to plan and coordinate

examinations on model risk management,
identify deficient practices, and

conduct appropriate follow-up."

Definition and Background of Models

The O C C provides a clear definition
of what constitutes a model: "For the

purposes of this document, the term
model refers to a quantitative method,

system, or approach that applies
statistical, economic, financial, or

mathematical theories, techniques,
and assumptions to process input

data into quantitative estimates."

A model consists of three components:

1.

"An information input component,
which delivers assumptions

and data to the model"

2.

"A processing component, which
transforms inputs into estimates"

3.

"A reporting component, which
translates the estimates into

useful business information"

The guidance acknowledges that "models
are simplified representations of

real-world relationships among observed
characteristics, values, and events"

and that "simplification is inevitable,
due to the inherent complexity of those

relationships, but also intentional,
to focus attention on particular

aspects considered to be most important
for a given model application."

It's important to understand that
"models are never perfect" and their

quality can be measured in many
ways, including "precision, accuracy,

discriminatory power, robustness,
stability, and reliability."

The handbook emphasizes that "in
all situations, it is important to

understand a model's capabilities
and limitations given its

simplifications and assumptions."

In contrast to models, the guidance notes
that "a quantitative tool not meeting

the definition of a model described in
the M R M Supervisory Guidance may apply

deterministic rules or algorithms to
process information and produce outcomes

defined by the deterministic rules."

The determination of "whether a
quantitative tool is considered a model

is bank-specific, and a conclusion
regarding the tool's categorization

should be based on a consideration
of all relevant information."

Expanded Use and Importance of Models

Banks now "rely heavily on
quantitative analysis and models in

most aspects of financial decision
making" and "routinely use models

for a broad range of activities."

Examples of model uses include:

"Underwriting and managing credits"

"Valuing trading exposures"

"Pricing"

"Risk hedging"

"Managing client assets"

"Measuring compliance with
internally established limits"

"Measuring compliance with laws
and regulations (including consumer

protection-related laws and regulations)"

"Estimating the allowance for credit
losses (A C L) and capital adequacy"

"Issuing public disclosures"

"Preventing and detecting
fraud and money laundering"

The expanded use of models reflects
their benefits: "Models can help

increase automation, transparency,
and consistency of bank activities."

However, the handbook notes that with
these benefits come costs: "There is

the direct cost of devoting resources to
develop and implement models properly.

There are also the potential indirect
costs of relying on models, such as the

possible adverse consequences (including
financial loss) of decisions based on

models that are incorrect or misused."

The handbook points out that "the
expanded use of models combined with

their increasing complexity and value in
decision making underscore the importance

of sound model risk management."

Technological Advances
and Model Complexity

The guidance acknowledges that
"technological and analytical

advances are contributing to
increased model complexity and use."

It specifically mentions artificial
intelligence (A I), which is "broadly

defined as the application of
computational tools to address tasks

traditionally requiring human analysis."

Examples of A I uses in banks include
"fraud detection and prevention,

marketing, chatbots, credit
underwriting, credit and fair lending

risk management, robo-advising (i.e.,

an automated digital investment advisory
service), trading algorithms and

automation, financial marketing analysis,
cybersecurity, Bank Secrecy Act/anti-money

laundering (B S A/A M L) suspicious
activity monitoring and customer due

diligence, robotic process automation, and
audit and independent risk management."

The handbook notes that "some A I
may meet the definition of a model"

while other A I outputs "are not
always quantitative in nature."

Regardless of how A I is classified,
"the associated risk management should

be commensurate with the level of risk
of the function that the A I supports."

Risks Associated with the Use of Models

The handbook identifies eight
categories of risk for bank supervision

purposes: "credit, interest rate,
liquidity, price, operational,

compliance, strategic, and reputation."

It notes that "model use can affect
risk in all eight categories of

risk" and that "the use of models
can increase or decrease risk in each

risk category depending on the models'
purpose, use, and the effectiveness of

any relevant model risk management."

Model risk is defined as "the potential
for adverse consequences from decisions

based on incorrect or misused model
outputs and reports" which "can lead

to financial loss, poor business
and strategic decision making, or

damage to a bank's reputation."

The handbook explains that model risk
occurs primarily for two reasons:

1.

"The model may have fundamental
errors and may produce inaccurate

outputs when viewed against the design
objective and intended business uses."

2.

"The model may be used
incorrectly or inappropriately.

Even a fundamentally sound model
producing accurate outputs consistent

with the design objective of the
model may exhibit high model risk

if it is misapplied or misused."

The guidance emphasizes that "banks
should identify the sources of risk

and assess the magnitude" and that
"model risk increases with greater

model complexity, higher uncertainty
about inputs and assumptions, broader

use, and larger potential impact."

Strategic Risk

Strategic risk is defined as "the risk to
current or projected financial condition

and resilience arising from adverse
business decisions, poor implementation

of business decisions, or lack of
responsiveness to changes in the banking

industry and operating environment."

The handbook notes that "the board
of directors and senior management

are the key decision makers that
drive the strategic direction of

the bank and establish a governance
framework for using models."

Strategic risk increases when "models
and associated risk management do not

keep pace with strategic changes, the
capability of employees, the operating

environment, and regulatory requirements."

Operational Risk

Operational risk is described as "the
risk to current or projected financial

condition and resilience arising
from inadequate or failed internal

processes or systems, human errors or
misconduct, or adverse external events."

The guidance states that "operational
risk is the primary risk associated

with the use of models" and can result
from "fundamental errors in a model when

viewed against the design objective and
intended business uses without sufficient

use of model overlays and adjustments
when model limitations become apparent."

Operational risk can also increase when
"personnel who do not have sufficient

skills and training to develop, implement,
use, and validate the bank's models."

Reputation Risk

Reputation risk is "the risk to
current or projected financial

condition and resilience arising
from negative public opinion."

The handbook notes that "inadequate
policies and processes, operational

breakdowns, or other weaknesses in
any aspect of model risk management or

governance can increase reputation risk."

Specifically, "a bank could incur
reputation risk from biased data

outcomes, data losses, noncompliance
with regulations, fraud, downtime, and

insufficient consumer protections."

Compliance Risk

Compliance risk is described as "the
risk to current or projected financial

condition and resilience arising from
violations of laws or regulations, or

from nonconformance with prescribed
practices, internal bank policies and

procedures, or ethical standards."

The handbook states that "compliance
risk is elevated when banks do not

comply with model-related laws and
regulations" and when "models result in

potential discrimination on a prohibited
basis or other violations of consumer

protection-related laws and regulations."

It specifically mentions that "a bank's
fair lending compliance risk could

increase when a bank's credit decisioning
models include algorithms, variables,

or other processes that result in
disparate impact on credit applicants or

customers based on prohibited factors,
such as race, ethnicity, or sex."

Credit Risk

Credit risk is defined as "the risk
to current or projected financial

condition and resilience arising
from an obligor's failure to meet

the terms of any contract with the
bank or otherwise perform as agreed."

The handbook notes that "banks use models
to increase efficiency in all stages

of lending" and that "if credit risk
models do not incorporate underwriting

changes in a timely manner, flawed and
costly business decisions could occur."

However, "models that are well-designed
and effectively managed can help

management make prudent risk selection
and monitor and manage credit risk."

Liquidity Risk

Liquidity risk is "the risk to current
or projected financial condition and

resilience arising from an inability to
meet obligations when they come due."

The handbook states that "liquidity
risk can increase because of inaccurate

or untimely inputs, assumptions,
model adjustments, and outputs."

Examples of common sources of liquidity
risk in modeling include "unsupported

or unreasonable contingent funding
assumptions; stress scenarios that

do not consider all relevant legal or
regulatory constraints; and inaccurate

or unsupported behavioral assumptions."

Interest Rate Risk

Interest rate risk is "the risk
to earnings or capital arising

from movements in interest rates."

The handbook notes that "interest rate
risk models depend on assumptions to

accurately project cash flows from assets,
liabilities, and off-balance-sheet items."

Common interest rate modeling issues
include "failing to assess potential

exposures over a sufficiently wide range
of interest rate movements," "failing

to modify or vary assumptions for
products with embedded options to reflect

individual rate scenarios," and "failing
to periodically assess the reasonableness

and accuracy of assumptions."

Price Risk

Price risk is defined as "the risk to
current or projected financial condition

and resilience arising from changes in
the value of either trading portfolios

or other obligations that are entered
into as part of distributing risk."

The handbook states that "a bank incurs
heightened price risk when trading

instruments with prices that are hard
to model," such as "instruments that

are illiquid or trade infrequently,"
"newer instruments," and "instruments

with fair value that depends on
accurately modeling human behavior."

Risk Management

The handbook emphasizes that "each bank
should identify, measure, monitor, and

control risk by implementing an effective
risk management system appropriate for the

size and complexity of its operations."

It notes that "model risk should be
managed like other types of risk"

and that "developing and maintaining
strong governance, policies,

and controls over the model risk
management framework is fundamentally

important to its effectiveness."

The guidance advises that "the extent
and sophistication of a bank's governance

function is expected to align with the
extent and sophistication of model usage"

and that "materiality is an important
consideration in model risk management."

The handbook states that "even with
skilled modeling and robust validation,

model risk cannot be eliminated,
so other tools should be used to

manage model risk effectively."

These tools include "establishing
limits on model use, monitoring model

performance, adjusting or revising models
over time, and supplementing model results

with other analysis and information."

Governance

Sound governance includes "board
and management oversight, policies

and procedures, a system of internal
controls, internal audit, a model

inventory, and documentation."

The handbook introduces the concept
of three lines of defense: "(1)

frontline units, business units,
or functions that create risk; (2)

independent risk management (I R M),
loan review, compliance officer, and

chief credit officer to assess risk
independent of the units that create

risk; and (3) internal audit, which
provides independent assurance."

The guidance emphasizes the importance
of "effective challenge" described

as "critical analysis by objective,
informed parties who can identify

model limitations and assumptions
and produce appropriate changes."

Effective challenge "depends
on a combination of incentives,

competence, and influence."

Board and Management Oversight

The handbook states that "model risk
governance is provided at the highest

level by the board of directors and senior
management when they establish a bank-wide

approach to model risk management."

The board is "responsible for setting
the tone at the top and overseeing

management's role in fostering and
maintaining a sound corporate culture."

Senior management is "responsible
for day-to-day implementation

of sound model risk management."

Their duties include "establishing
adequate policies and procedures and

ensuring compliance, assigning competent
staff, overseeing model development

and implementation, evaluating model
results, ensuring effective challenge,

reviewing validation and internal
audit findings, and taking prompt

remedial action when necessary."

The handbook notes that "sound governance
encourages all key stakeholders to

have effective communication, be
transparent, and actively participate

in model risk management" and that
"appropriate change management before

implementing or when changing models
and related technologies is an important

component of model governance."

Personnel

The guidance emphasizes that "the bank
should have competent and qualified

personnel to execute and oversee
model risk management" and that "the

skills and expertise of management
and other personnel should be

commensurate with the nature, extent,
and complexity of the use of models."

The handbook suggests that
"well-thought-out personnel development,

recruiting, succession planning, and
compensation processes promote successful

hiring and retention of individuals
with highly technical skills for model

development and for model risk management
across the three lines of defense."

Model Owners

Model owners are described as having
"ultimate accountability for model use

and performance within the framework
set by bank policies and procedures."

They should "ensure that models are
properly developed, implemented,

and used" and "that models in
use have undergone appropriate

validation and approval processes."

Model owners are generally responsible
for implementing policies and standards,

establishing and maintaining processes
for identifying and measuring risks,

maintaining internal controls, and
maintaining documentation standards.

Independent Risk Management Staff

As the second line of defense, independent
risk management (I R M) "typically

oversees business unit risk-taking
and risk management activities" and

"validates and challenges business
unit testing and other first-line

model risk management processes."

I R M's responsibilities include
implementing policies and standards,

establishing processes for identifying
and measuring risks, validating model

inputs and outputs, confirming models are
performing as intended, assessing issues

for themes or patterns, and reporting
to senior management and the board.

Internal Audit

As the third line of defense, internal
audit "reviews model governance

and risk management and provide
independent assurance to the board on

the effectiveness of governance, risk
management, and internal controls."

The handbook states that internal
audit "should assess the overall

effectiveness of the model risk
management framework, including the

framework's ability to address both
types of model risk for individual models

and in the aggregate" and "evaluate
whether model risk management is

comprehensive, rigorous, and effective."

Internal audit is typically responsible
for verifying acceptable policies are

in place, documenting and reporting
findings, verifying records of model

use and validation, assessing the
accuracy and completeness of the model

inventory, evaluating processes for
establishing and monitoring limits,

and evaluating the objectivity and
competence of validation participants.

Policies and Procedures

The guidance states that "consistent
with good business practices and

existing supervisory expectations,
banks should formalize model risk

management activities with policies
and the procedures to implement them."

These policies should be "commensurate
with the bank's relative complexity,

business activities, corporate culture,
and overall organizational structure"

and should be approved by the board or
its delegates and reviewed annually.

Policies and procedures regarding
model risk management may include

descriptions of governance and
controls, definitions of models and

model risk, acceptable practices for
model development and implementation,

roles and responsibilities, standards
for model inventory, fair lending

considerations, and controls
for model development and use.

Risk Assessment

The handbook explains that "assessing
risk includes identifying and

measuring the sources and magnitude
of risks associated with model use."

This is "particularly important
as a bank increases in size and

complexity, the use of models becomes
more widespread, or model results

significantly influence decision making."

A sound model risk assessment process
generally "identifies risk both

from individual models and models
in the aggregate," "identifies

the model's capabilities and
limitations," and "measures the risks

associated with model activities
accurately and in a timely manner."

Planning

The guidance states that "effective
governance for modeling begins with

appropriate planning" and that "a
clear statement of purpose is typically

the first step to developing models
aligned with the intended use."

Key planning considerations include
identifying stakeholders, performing

risk assessments, making informed
decisions about implementing new models,

understanding the purpose and limitations
of models, integrating new technology

with legacy systems, and ensuring
appropriate controls for monitoring

outputs that may be discriminatory.

Model Inventory

The handbook advises that "banks
should maintain a comprehensive set

of information for models implemented
for use, under development for

implementation, or recently retired"
and that "a specific party should

also be charged with maintaining a
firm-wide inventory of all models."

A comprehensive model inventory
may include information such

as model identifier, version,
ownership, status, purpose, inputs

and outputs, risks, validation
activities, issues or limitations,

and expected validity timeframe.

Documentation

The guidance emphasizes that
"without adequate documentation,

model risk assessment and
management will be ineffective."

Documentation should be "sufficiently
detailed so that parties unfamiliar

with a model can understand how the
model operates, its limitations,

and its key assumptions."

Documentation benefits developers,
users, and risk management personnel,

and should include information supporting
decisions related to model selection,

testing, governance, development, internal
controls, and third-party risk management.

Data Management

The handbook states that "to measure
risk effectively, the data inputs for

models should be reliable" and that
there should be "rigorous assessment

of data quality and relevance,
and appropriate documentation."

Sound data management includes
"providing reasonable and reconcilable

support for qualitative factors,"
regularly analyzing "the integrity

and applicability of internal and
external information sources and related

controls," and appropriately handling
data proxies and third-party data.

Model Development, Implementation, and Use

The guidance emphasizes that "model
risk management begins with robust model

development, implementation, and use"
and that the process "should include

disciplined and knowledgeable development
and implementation processes that are

consistent with the situation and goals
of the model user and with bank policy."

Model Development and Implementation

The handbook states that "an
effective development process begins

with a clear statement of purpose
to ensure that model development

is aligned with the intended use."

It should include documentation of "the
design, theory, and logic underlying

the model," explanation of "the
model methodologies and processing

components," and comparison "with
alternative theories and approaches."

The development process should produce
"documented evidence in support of

all model choices, including the
overall theoretical construction,

key assumptions, data, and specific
mathematical calculations."

For third-party models, banks should
"ensure that there are appropriate

processes in place for selecting vendor
models" and require vendors to "provide

appropriate testing results that show
their product works as expected."

Testing

The guidance describes testing as "an
integral part of model development"

that includes "checking the model's
accuracy, demonstrating that the model

is robust and stable, assessing potential
limitations, and evaluating the model's

behavior over a range of input values."

Testing should be "applied to actual
circumstances under a variety of market

conditions, including scenarios that
are outside the range of ordinary

expectations," and should "encompass
the variety of products or applications

for which the model is intended."

Ongoing Development

The handbook notes that "models are
regularly adjusted to take into account

new data or techniques or because
of deterioration in performance."

It advises that "material changes in
model structure or technique, and all

model redevelopment, should be subject
to validation activities of appropriate

range and rigor before implementation."

Model Use

The guidance states that "model use
provides additional opportunity to test

whether a model is functioning effectively
and to assess its performance over time as

conditions and model applications change."

Model users can "provide valuable
business insight during the development

process" and may "question the methods
or assumptions underlying the models."

However, the handbook cautions that
"challenge from model users may be weak

if the model does not materially affect
their results," and that user challenges

"tend not to be comprehensive because they
focus on aspects of models that have the

most direct impact on the user's measured
business performance or compensation."

Model Overlays and Adjustments

The guidance defines model overlays as
"judgmental or qualitative adjustments to

model inputs or outputs to compensate for
model, data, or other known limitations."

It advises that "sound model risk
management includes policies and

processes regarding the review,
approval, use, and back-testing of

model overlays and adjustments."

The handbook emphasizes that "model
overlays and adjustments should not be

viewed as a solution that dissuades the
bank from making improvements to the

model" and that banks "typically have a
process to monitor and analyze overlays

and adjustments over time and address
underlying limitations and issues."

Reporting

The guidance states that "reports used
for business decision making play a

critical role in model risk management"
and should "be clear and comprehensible."

Effective reporting "enables
senior management and the board to

understand the bank's model risk."

Reports presented to the board "typically
highlight performance measures, trends,

and variances, rather than presenting
the information as raw data" and may

include measures on "the volume of
models considered high risk," "models

with temporary exemptions or provisional
approvals," and "underperforming models."

Model Validation

The handbook describes model validation
as "the set of processes and activities

intended to verify that models are
performing as expected, in line with their

design objectives and business uses."

It states that "effective validation
helps ensure that models are sound"

and "also identifies potential
limitations and assumptions, and

assesses their possible impact."

The guidance emphasizes that "all model
components, including input, processing,

and reporting, should be subject to
validation" and that "the rigor and

sophistication of validation should be
commensurate with the bank's overall

use of models, the complexity and
materiality of its models, and the size

and complexity of the bank's operations."

An effective validation framework
should include three core elements:

1.

"Evaluation of conceptual soundness,
including developmental evidence"

2.

"Ongoing monitoring, including
process verification and benchmarking"

3.

"Outcomes analysis,
including back-testing"

Evaluation of Conceptual Soundness

The handbook describes this element
as "assessing the quality of the model

design and construction" which "involves
review of documentation and empirical

evidence supporting the methods used
and variables selected for the model."

Evaluation of conceptual soundness
generally includes assessing whether

the model achieves its intended
purpose, comparing alternative

theories and approaches, analyzing key
assumptions and variables, evaluating

data relevance, and conducting
sensitivity analysis and stress testing.

Ongoing Monitoring

The guidance describes ongoing monitoring
as confirming "that the model is

appropriately implemented and is being
used and is performing as intended."

It is "essential to evaluate whether
changes in products, exposures,

activities, clients, or market conditions
necessitate adjustment, redevelopment,

or replacement of the model."

Ongoing monitoring typically includes
assessment of adherence to risk

appetite and internal limits, analysis
of overrides, sensitivity analysis,

review of risk measurements, escalation
processes, timely system updates, and

process verification and benchmarking.

Process Verification

The handbook describes process
verification as checking "that all model

components are functioning as designed."

This includes "verifying that internal
and external data inputs continue

to be accurate, complete, consistent
with model purpose and design, and

of the highest quality available."

Process verification typically includes
confirming inputs are processed as

expected, reviewing reports derived from
model outputs, and verifying that computer

code implementing the model is subject
to quality and change control procedures.

Benchmarking

The guidance defines benchmarking as "the
comparison of a given model's inputs and

outputs to estimates from alternative
internal or external data or models."

It notes that "discrepancies between
the model output and benchmarks should

trigger investigation into the sources
and degree of the differences."

Outcomes Analysis

The handbook describes outcomes
analysis as "a comparison of model

outputs to corresponding actual
outcomes" which "helps to evaluate

model performance, by establishing
expected ranges for those actual

outcomes in relation to the intended
objectives and assessing the reasons

for observed variation between the two."

Outcomes analysis approaches may
vary depending on the characteristics

and objectives of a model, and
may include both quantitative

and qualitative techniques.

The guidance notes that "models are
regularly adjusted to take into account

new data or techniques, or because
of deterioration in performance" and

that "parallel outcomes analysis" is
an important test of such adjustments.

Back-Testing

The guidance defines back-testing as "a
form of outcomes analysis that involves

the comparison of actual outcomes with
model forecasts during a sample time

period not used in model development,
and at an observation frequency

that matches the forecast horizon
or performance window of the model."

The handbook notes that "analysis of
the results of even high-quality and

well-designed back-testing can pose
challenges" and that "models with long

forecast horizons should be back-tested,
but given the amount of time it would

take to accumulate the necessary data,
that testing should be supplemented

by evaluation over shorter periods."

Third-Party Risk Management

The guidance states that "banks may
benefit from third-party relationships

by gaining operational efficiencies or
improving the banks' competitive edge."

However, "a bank's use of third parties
does not diminish the responsibility

of the bank's board and senior
management to ensure that the bank

operates in a safe and sound manner."

The handbook advises that "third-party
models should be incorporated into the

bank's third-party risk management and
model risk management processes" and that

"management should conduct appropriate
due diligence on the third-party

relationship and on the model itself."

Third-Party Models and Data

The guidance states that "the widespread
use of vendor and other third-party

products—including data, parameter
values, and complete models—poses

unique challenges for validation and
other model risk management activities

because the modeling expertise is
external to the user and because some

components are considered proprietary."

When a bank uses a third-party model,
it should obtain information from the

third party including developmental
evidence, information about data

used, testing results, documentation
of limitations and assumptions,

and implementation instructions.

Engaging Third Parties for
Model Risk Management Activities

The handbook notes that "although
model risk management is an internal

process, a bank may decide to engage
external resources to help execute

certain activities related to the
model risk management framework."

These activities could include "model
validation and review, compliance

functions, or other activities
in support of internal audit."

Banks should "specify the activities
to be conducted in a clearly written

and agreed-upon scope of work"
and designate an internal party to

"understand and evaluate the results of
validation and risk control activities

conducted by external resources."

IT Systems

The guidance states that "banks
should have appropriate risk

management to maintain adequate IT
systems that result in appropriate

and accurate model outputs."

It emphasizes that "sound model risk
management depends on substantial

investment in supporting systems to
ensure data and reporting integrity,

together with controls and testing."

The handbook advises that "the IT
environment supporting the bank's

models has appropriate controls
before model implementation."

This includes controls for "the access,
authentication, transmission, and storage

of sensitive customer information" and
appropriate "security controls related to

how the bank and any third parties access,
transfer, share, and store information."

This concludes our comprehensive
summary with direct quotes from the

OCC Handbook on Model Risk Management.

If your Credit union could use assistance
with your exam, reach out to Mark Treichel

on LinkedIn, or at mark Treichel dot com.

This is Samantha Shares and
we Thank you for listening.