The document discusses the liquidity risk management requirements under the Enhanced Prudential Standards (EPS) for foreign banking organizations operating in the US. It notes that the EPS placed significant emphasis on liquidity risk management and clarified roles for risk committees and CROs. It also reinforced stress testing, contingency funding plans, limits monitoring, and buffer requirements. Transitioning to comply will require enhancements to governance frameworks, cash flow projections, stress testing, and internal controls. Meeting the requirements will impact branches, BHCs, and intermediate holding companies.

1. PwC
EPS Liquidity Risk Management
(LRM) for Foreign Banking
Organization
1

2. 2
Responding to regulatory developments in liquidity risk
management
• The finalization of the EPS by the Federal Reserve on February 18, 2013 placed significant emphasis on Liquidity
Risk Management requirements for foreign banking organizations operating in the U.S. particularly where U.S.
operations exceed $50 billion across branch and agency network and non-branch legal entities
• The EPS clarified the roles and responsibilities of the US Risk Committee and Chief Risk Officer relative to Liquidity
Risk Management , in particular, identifying what aspects require review and approvals at various levels within the
organization. Addtionally, the EPS reinforced the importance of Internal Audit as the independent review element.
• The EPS also confirmed the elements within the proposal regarding cash flow projection processes, contingency
funding plan-related expectations (including the quantitative assessment and control framework) and the limit
structure across a range of potential liquidity exposures – concentrations, maturities, collateral and intraday
exposures
• Defined the expectations pertaining to stress testing particularly the scope of coverage across legal entities on an
aggregate basis as well as the branch and agency network and IHCs, where applicable. The rule also defined the
expected planning horizons (O/N, 30 day, 90 day and 1 yr.) with emphasis on the 30 day horizon to calculate the
required liquidity buffer .
• Identified that the branch and agency network would need to maintain high quality liquid assets sufficient to cover
the first 14 days of the net external and internal cash needs. Eliminated the burden of maintaining the residual
amount of the buffer elsewhere but did not reduce burden of calculation.
• Fed indicated that it would seek to implement LCR framework for FBOs through separate rulemaking process.
Likely to impact IHCs rather than branch and agency network to align with implementation relative to US BHCs

3. 3
EPS requirements focus in four areas of Liquidity Risk Management
EPS Requirements
• US Risk Committee
• US CRO / Senior Mgmt.
• Independent Review
Governance
• Liquidity Stress Testing
• Contingency Funding
Plans
Stress Test & CFP
• Liquidity Risk Limits
• Cash Flow Projections
• Collateral Monitoring
• Intraday Liquidity
Limits & Monitoring
• Buffer Calculations
• Buffer Composition
Liquidity Buffer

4. 4
Transitioning to a Target Operating Model (TOM) – Regulatory Gaps
Supervisory and Regulatory Letter 10-6
Compliance Gaps
Enhanced Prudential Standards Implications
Governance
Framework
• Liquidity Risk Appetite framework
defined at the holding company level
• US risk committee established at
holding company level and reporting
should be provided on monthly or
more frequent basis
• Specific LRM requirements focused on Risk
Committee and CRO roles to address oversight on
consolidated US operations
• Liquidity Risk-focus needed for new products process
• Increased report/dashboard generation frequency
Cashflow
Estimation and
Stress Testing
Framework
• Define cash flow estimation
procedures and ensure consistency
across US Operations
• Liquidity stress testing that
addresses US Operations and
material legal entities
• Cash flow estimations should include behavioral
assumptions on inflows/outflows and supplement
conservative estimates
• Cash flow estimation methodologies to be reviewed by
senior management
• Stress Test horizons to cover O/N; 30; 90 day and 1yr.
time horizons – DNB covers 1 week; 1 & month
horizon
• Processes to address standalone Branch
Contingency
Funding Plan
• Contingency Funding Plan to
address consolidated US Operations
• Enhance assessment of alternative
funding sources
• CFP for consolidated US Operations that embeds
stress tests and addresses funding alternatives under
scenarios
• Integration of EWIs and monitoring across US
Operations
Internal
Controls
Framework
• Align Internal Audit scope with
regulatory/supervisory requirements
• Assessment of limit framework in line with Risk
Appetite and EWI framework – capture specific limits
required with in EPS

5. 5
The EPS Requirements for Liquidity generally impact the entirety of
combined US Operations – branches, US BHCs (due January 1, 2015), and
the IHC. Applicable EPS Rule Requirements
US BHC1 Branch IHC2
Dec. 2014 Jun. 2016 Jun. 2016
1. Governance
Implement liquidity risk management governance standards applicable to the CRO and Risk Committee; and
establish and maintain an independent review function to evaluate liquidity risk management
X X X
2. Limits & Monitoring
Establish and maintain specific limits on potential sources of liquidity risk, procedures for monitoring assets
pledged / available to be pledged as collateral, and procedures for monitoring intraday liquidity risk exposure
X X X
Establish a methodology for and produce comprehensive cash flow projections on a daily and monthly basis X X X
3. Stress Testing & Contingency Funding Plans
Perform monthly internal liquidity stress testing to assess the impact of liquidity stress scenarios on the cash
flows, liquidity position, profitability and solvency
X X X
Stress scenarios must address 1) market events; 2) idiosyncratic events; and 3) combination of market and
idiosyncratic events and cover at least the following planning horizons – overnight, 30 days, 90 days and one
year
X X X
Develop a combined contingency funding plan for operations covering quantitative assessment, event
management, monitoring and testing requirements; and approved by the US or BHC Risk Committee
X X X
4. Liquidity Buffers
Maintain 30-day liquidity buffer consisting of unencumbered high quality liquid assets maintained in the US to
meet net stressed cash flow needs of the IHC / BHC based on the stress tests covering the 30 day planning
horizon
X X
Maintain 14-day liquidity buffer consisting of unencumbered high quality liquid assets maintained in the US to
meet net stressed cash flow needs of FBO owned branches based on the stress tests covering the 30 day
planning horizon
X X

6. 6
Foundational Liquidity Monitoring – Cash Flow Projections, Collateral, and
Intraday Liquidity
EPS Cash Flow Projection Drivers MIS Implications
• Cash flow projections for combined US Operations from assets, liabilities and off-balance
sheet exposures for short and long term time horizons.
• Short term projections must be updated daily, and long term projections monthly.
• Must establish a cash-flow projection methodology that:
– Includes flows from contractual maturities, intercompany transactions, new business,
funding renewals, customer options and other potential liquidity events
– Have reasonable assumptions regarding future behavior of assets, liabilities and off-
balance sheet exposures
– Identify and quantity discrete and cumulative cash-flow mismatches over these time
periods
• Cash flow estimation methodologies to be reviewed by senior management
• Include sufficient detail to reflect the capital structure, risk profile, complexity, currency
structure, activities and size of the Combined US operations and include segmentation by
business line, currency or legal entity as needed
• Extensive granular data collection requirements
• If added FRB 5G report into the scope the granular data
needs to be pre-processed for cash flow classifications
– Segment by portfolio, product, customer
– Operational vs. non-operational deposits
– Stable vs. less stable deposits
– Committed vs. liquidity credit facility
• Advanced analytics to model cash flows
• Focus on indeterminate maturity liability risk
• Increased report generation frequency
• Need for greater precision and reconciliation to GL
• A US centric view of liquidity requirements and modelling
framework and increased scrutiny of internal controls
Intraday Liquidity and Collateral Monitoring MIS Implications
• Monitoring and measuring expected daily inflows and outflows
• Maintaining, managing and transferring collateral to obtain intraday credit
• Identifying and prioritizing time-specific obligations
• Controlling issuance of credit to customers
• Considering amounts of collateral and liquidity needed to meet payment systems obligations
for overall US liquidity needs
• Weekly calculations of collateral positions specifying the value of pledged assets vs. the
amount of security required and the value of unencumbered assets available to be pledged
• Monitoring levels of unencumbered assets available to be pledge by legal entity, jurisdiction
and currency exposure
• Monitoring shifts in funding patterns including shifts between intraday, overnight and term
pledging of collateral
• Tracking operational and timing requirements
• T+0 data collection for intraday liquidity involved in
trading book and/or funding liability transactions
• Increased frequency of counterparty payment and credit
issuance tracking
• Collateral position data collection at a granular level
• Collateral linkages to on and off balance sheet transactions
• Increased frequency of collateral reporting between
monthly, daily, and intra-day liquidity management
• Daily reconciliation with funding liability transactions

7. 7
Enhanced Liquidity Monitoring
Normally a standard LRM reporting framework should encompass information targeting core business
areas pertaining to operational liquidity management within the business network, the sensitivity of fund
providers to both financial market and institutional trends and events, economic conditions effecting the
trading book, any anticipated deviations from the original plan versus budget. A sound liquidity risk MIS
should contain reports detailing the following information and metrics:
• Liquidity needs and the sources of funds available to meet these needs over various time horizons and scenarios.
• Pro-forma cash flow statements and funding mismatch gaps over different time horizons. The maturity distribution of assets and
liabilities over a full range maturity interval and expected funding commitments.
• Funding concentration: Top 20 large deposits, list of large wholesale fund providers, and the list of fed-fund programs.
• The sensitivity of fund providers to both financial market and institutional trends and events.
• Any exceptions to ALCO limits and policy guidelines with regard to liquidity ratios and contractual cash-flow mismatch.
• Longer-term interest margin trends and asset quality trends, and economic conditions in the bank's trade area, equity prices, CDS
prices, debt markets, funding cost, FX markets, interest rate projections, and any anticipated deviations from the original plan vs.
budget.
• The bank’s exposure to both broad-based market and institution-specific contingent liquidity events.
• Information concerning non-relationship or higher-cost funding programs. At a minimum, this information should include a
listing of public funds obtained through each significant program, rates paid on each instrument and an average per program.
• Information on maturity of the instruments, and concentrations or other limit monitoring and reporting.
• If applicable, the impact of cash flows related to the repricing, exercise, or maturity of financial derivatives contracts, including
the potential for counterparties to demand additional collateral in the case of a weakening of the market’s perception of thebank.
• High Quality Liquid Asset (HQLA) trends, regulatory reports and metrics may be necessary depending on the banks’ size and
operation.

8. 8
Liquidity Risk MIS Liquidity Reporting Package Framework
LRM practice within the financial institutions need to be coherent in order to produce multiple
reporting packages with narratives on market conditions, internal liquidity metrics and KPIs, stress test
results and early warning indicators, peer financial institution benchmarks, and executive dashboards
and heat-maps.

9. 9
Stress Testing: The Core Elements
Base
scenario
Stress
scenario
1month
2month
3-6month
6-12month
1-2year
2-3year
3-4year
4-5year
>5year
1month
2month
3-6month
6-12month
1-2year
2-3year
3-4year
4-5year
>5year
Base
assumption
s
Cashflow system
Cashflow by position
Deposit
characterization:
rate sensitive
balance dynamics
Liquidity gap analysisCashflow generation
Liquidity gap analyses
(by scenario)
Scenario 1
Base
assumption
s
Cashflow system
Cashflow by position
Deposit
characterization:
rate sensitive
balance dynamics
Cashflow generation
Liquidity metrics
• Basel III ratios (as per
baseline assumptions)
• FRB 5G report
• Liquidity cash flow mismatch by
maturity buckets
• Other regulatory ratios as needed
by geography
• Key daily Treasury ratios
(e.g. deposits as % of assets)
• Other management metrics
(e.g. unencumbered asset levels)
Liquidity metrics
• Ratios as above under stress
scenario
• Asset composition/coverage
analysis
• Liquidity cushion
• Survival horizon analyses
– Target survival horizon
by scenario
– Scenario survival results
A foreign banking organization must conduct stress tests to separately assess the potential impact of
liquidity stress scenarios on the cash flows, liquidity position, profitability, and solvency of: a) Its combined
U.S. operations as a whole; b) Its U.S. branches and agencies on an aggregate basis; and c) Its U.S.
intermediate holding company, if any.

10. 10
Setting Target State for Liquidity Risk Infrastructure
Being ahead of the industry curve requires investment in an expansive liquidity risk management
information systems (MIS) encapsulating a robust data and analytics infrastructure and architectural
agility that can respond to business needs in real time, with minimal manual intervention.
Integrated
Operating
Environment
The liquidity risk MIS infrastructure design and implementation should be fully integrated with ALM, fund
transfer pricing (FTP), cost of liquidity allocation and pricing liquidity risk, funding and liquidity risk, hedging
and diversification, capital calculation and planning solution within the balance sheet management and
enterprise risk frameworks to enable strategic decision making and timely optimization.
Golden Source
of Data
Data at its most fundamental level is required to be collected, normalized, standardized, integrated, and
retained within a single data warehouse.
Unified Data
Management
The data management process should be supported by adequate governance and controls ensuring
standardization, conformance, quality, reconciliation, traceability, auditability, and flexibility to add and change
data.
Computational
Flexibility
The calculation and analytics engines should enable flexibility that can run complex quantitative models to
project cash-flows, generate and run complex stress scenarios, and can implement rule changes fairly quickly.
Future-proof
architecture
The information architecture needs to be forward-looking and must be designed for flexibility that can
anticipate changes, and implement fairly quickly.
Informational
Readiness
Basic information should be readily available for day-to-day liquidity and funds management and during times
of stress.
The reporting and IM engine should support intraday, daily, weekly, monthly liquidity monitoring, and survival
horizon analysis, report level calculations and validation rules, regulatory metrics and templates, audit trails,
access to granular data, and be able to implement changes fairly quickly.
Adaptive
reporting and
monitoring
(IM) engine

11. 11
A Sustainable Liquidity Risk Infrastructure
Liquidity risk monitoring under EPS and supervisory expectations require an automated and
controlled platform for data collection, aggregation, capture of market and behavioral assumptions,
report generation and analytics while reflecting all required aggregation dimensions. Manual processes
can impede reporting frequency and accuracy.
Position Data
Data Collection & Aggregation Analytics & Reporting
Liquidity Data Hub
Reconciled Positions
Reference Data
Market Reference Data
Internal Reference Data
Liquidity Stress Testing & Analytics
Stress Scenario Modeling
Regulatory and Internal Metrics
Sensitivity Analysis
RWA/Capital Impacts HQLA Impacts
Internal & External Reporting
Regulatory
Templates
ALCO
KRI
Dashboards
Aggregation Dimensions
Holding Company
Jurisdiction/Region
Line of Business
Legal Entity
Currency
Securities
Deposits
Unsecured Funding
Secured Funding
Commercial Lending
Collateral Mapping
Contract Obligations
Derivatives
Swap Agreements
Customer Attributes
Basel II Risk Weights

12. 12
Liquidity Risk Management Data Requirements
Liquidity risk management under EPS and supervisory expectations warrant a comprehensive set of data
attributes from the following areas. Depending on the size and complexity of the balance sheet, as well as
the investment portfolio, the number of reference attributes can range from 500 to 700 data elements.

13. Collateral Management
• Collateral-transaction Mapping
• Rehypothecation Rights
• Encumbered Asset Identification
• Counterparty Type
• Lendable Value Of Assets
• Collateral Swap Transactions
Wholesale Deposit Classification
• Operational Deposit Tag
• Insured Product Eligibility Tag
• Insured Deposit Tag
• Required Collateral
• Legal Entity Concentration
Retail Deposit Classification
• Individual/SME Bifurcation
• First Service Date
• Established Relationship Tag
• Transactional Account Tag
• Insured Product Eligibility Tag
• Insured Deposit Tag
• Stable/Less Stable Deposit Tag
Liquid Asset Classification
• Basel 3 Standardized Risk Weight
• LAB Asset Class
• LAB criteria check
• Contractual Inflow Schedule
• Encumbrance of assets
Customer Classification
• Financial Counterparty Tag
• Number of Accounts
• Basel 3 Customer Type
• Customer Segmentation Tag
• Aggregate Customer Balance
• Subsidiary-level Consolidation
Retail Deposit
Classification
Liquid Asset
Classification
Customer
Classification
Internal Position
and Stress
Testing Needs
FR 2052B
Regulatory
Reporting
Contingent
Funding
Collateral
Management
Wholesale
Deposit
Classification
Contingent Funding
• Credit / liquidity facility tag
• Committed / Uncommitted Tag
• Facility Counterparty Types
• Letters of Credit
• Other Unfunded Commitments
• Derivative Valuation Changes
• Credit Downgrade Triggers
Liquidity Risk Management Position Data Processing
Requirements – Specific to 5G, FR 2052b, and US LCR
Slide 13
The principal challenge in automating liquidity risk management reporting is the establishment of an
automated data repository that updates required position data to support MIS and regulatory reports

14. 14
Liquidity Risk MIS Reference Architecture
The MIS plays a vital role in establishing a well-integrated, automated, and sustainable LRM operating
platform that can facilitate timely decision support by transforming raw on/off balance sheet position
data into meaningful information and liquidity risk measures.
Position data, external market and reference data
required for treasury -risk reporting and analysis is
collected, normalized and aggregated within an
internal Treasury Risk Data Warehouse
If a Treasury Risk Data Warehouse is not
available, a staging layer is created in which
position data is normalized and checked for
accuracy, consistency and completeness, prior to
classification
Data exceptions are identified as unexpected
results following the classification process;
individual exceptions are captured and
reported to risk managers; critical errors are
remediated and reclassified
Primary data fields required for the liquidity
classification process would be sourced from the
treasury risk data warehouse and transformed into
reportable elements using algorithms (tagging
logic); aggregation takes place using classified
elements and specified legal entities
A liquidity-specific database is formed by
the accumulation of classified elements
allowing for the production of regulatory
reports, internal risk management reports
and liquidity specific analytics
Data Classification
(Tagging Logic)
Data Aggregation
(Legal entity roll-ups)
Contractual Cash Flow
Time-bucketing
Exception
Report
Data Error
Remediation
LRM Data Layer
[Liquidity Database]
Regulatory Reporting
(5-G Reporting)
Analytic Suite
(Stress Testing)
Treasury Risk
Data Warehouse
Position
Data Feed
Reference
Data Feed
Market
Data Feed
Analytical Engine
[Vendor Solution]

15. 15
Liquidity Risk MIS Design Consideration
• Define LRM solution development scope, business and functional requirements
• LRM vendor application selection
• Conceptual solution design and prototype
• Detailed solution design and phased implementation roadmap
• User acceptance criteria and test cases
• Define detailed liquidity risk data dictionary by each A/L products and reference data subject area
• Define data source inventory / system of records (SOR) and source data treatments, detailed process, data and system
flows
• Data interface deign for cash-flow position, reference data, and collateral source feeds
• Data staging layer and Treasury data warehouse (TDW) design and data mapping to the Treasury data ware house
• LRM data mart design and cash-flow segmentation, classification, and aggregation dimensions design, and GL
Reconciliation process
• LRM analytical engine design for cash-flow modelling and liquidity stress test
• Liquidity risk management and regulatory reporting engine design
• Liquidity risk MIS end-to-end physical architecture for platforms and security
• Application components development and integration specification
• Application development prioritization and deployment roadmap
LRM elements such as visibility into liquidity characteristics, enterprise wide liquidity position
monitoring, liquidity stress test, contingency funding plan, decision strategy, and liquidity regulatory
reporting permeate the business operating network within the banking organizations. It is paramount
to involve the key stakeholders and the business process owners from these functional areas over the
lifecycle of the liquidity risk MIS solution design and implementation.

16. 16
Liquidity Risk IT/MIS – Key Industry Trends
• Development Treasury Data Warehouse (TDW) to cover all key functional components across Treasury - ALM, FTP, funding and
liquidity management, capital management, and investment management – and support multiple downstream applications and
analytical engines.
- Component based Treasury architecture – segregation of data store, analytical engine, BI and reporting
- Highly integrated environments, considering ‘lowest common denominator’ of data requirements (latency, granularity)
- Leveraging ‘provisioning points’ for trusted sources of information (e.g. LOB DW); utilizing SORs only when required
- Development of interface SLAs, including reconciliation
- Automation of data quality rules and dashboards
• Ability to support different levels of aggregation and pooling of transaction / position data for ALM, FTP, funding and liquidity,
and capital management – for e.g. liquidity reporting will require more granularity on retail and wholesale deposits.
• Availability of historical account level data (up to 7 years) for specific balance sheet lines (retail and wholesale deposits) to support
behavioral modeling, portfolio segmentation and deposit characterization.
• Common analytical engine for ALM, FTP, Funding and Liquidity Management - to ensure consistency of contractual and
behavioral assumptions, product hierarchy and reporting.
• Lagging integration of capital calculation and planning solution with ALM, FTP, Funding and Liquidity – led by Risk Management.
• Single global repository of contractual and behavioral cash flows for all assets, liabilities and off-balance sheet items for all
significant entities (subsidiaries and branches)
• Rationalize the points of cash flow generation and mark-to-market valuation within the organization - for e.g. leverage cash flows
and valuation from market risk for derivatives.
• Vender Application: Large focus on acquiring holistic vendor applications for combined Liquidity Risk Management, ALM/IRR &
Balance sheet risk, Capital Management / CCAR modeling / stress test & reporting, regulatory reporting, and enterprise risk reporting
• Common market (yield curves) and reference (counterparty) data across all key functions in Treasury.
• Daily liquidity regulatory reporting (T+1 basis) across all major jurisdictions for all defined liquidity groups.
• Reconciliation of Treasury Data Warehouse (TDW) to books and records at an appropriate level of granularity.

17. 17
Liquidity Risk IT/MIS – Lessons Learned
Lessons learned Considerations for Liquidity Management
Confirm executive management
engagement
• Implementing Treasury analytics and reporting infrastructure is a significant undertaking that will require
support from groups across the organization – for e.g. Treasury, Finance, Risk Management, Clusters and IT.
Executive management should be engaged early on and integrated within the governance structure.
• Executive management attention is necessary to confirm the right resources are available to the project and
the prioritization is communicated so that information critical for the project’s success is delivered in a timely
manner.
Utilize a single source of data for
internal MI and regulatory
reporting
• Firms that have been successful have adopted a strategy where a single source of data supports both internal
treasury management and regulatory reporting. The foundation is position-level or pool-level contractual cash
flows with behavioral adjustments (common across ALM, FTP and Liquidity)
• Single source of data for internal risk management and regulatory reporting helps increase focus of Treasury
and Finance/Regulatory Reporting on data quality – Treasury’s knowledge is critical to get the data right.
Address data ownership and
carefully define the operating
model
• Reporting daily liquidity data is a complex process. Even the best processes will require some manual
adjustments, for which participation from diverse groups is required. It is important to carefully consider and
define the operating model to clarify roles and responsibilities, as well as the governance and controls around
the process.
Hold joint sessions with Treasury
and
IT to define business
requirements
• Rapid requirements development that includes both Treasury and IT helps to drive out inconsistencies of
understanding early in the process. Additionally, it saves overall time, as teams can move into functional
design more rapidly with a greater understanding of the overall requirements.
Utilize single consistent data
model and tightly control data
sourcing
• Time invested in clearly defining the interface requirements and rigorously defending them from change will
simplify testing and reduce testing cycle times. Automated testing tools can be developed to check data
formats and validations, limiting the need for manual testing and data validation.
• Since the data collected for liquidity reporting may not have been used in a business context before, source
data owners must be held accountable for data quality, to confirm the data flowing into the data store is
accurate.
Agree on data validation
approach
since books and records data may
not be available
• G/L data is typically accurate once a month after business true-ups and adjustments. Additional controls and
validations need to be developed to give confidence that liquidity risk data is representative of an institution’s
position as of a specific date. Depending on the product’s volatility and existing infrastructure, this could be as
basic as a trade count, but with more complex data or higher volatility, some form of valuation control is
needed - either a MTM figure or re-valuation.
Publish defect metrics to drive
accountability for data
• Transparency and frequent reporting of data defects is a powerful tool to quickly identify and remediate
errors, as well as provide benchmarks for source data providers to compare source data quality.