PwC

EPS Liquidity Risk Management (LRM) for Foreign Banking Organization

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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

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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

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Transitioning to a Target Operating Model (TOM) – Regulatory GapsSupervisory and Regulatory Letter 10-6

Compliance GapsEnhanced 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

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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. GovernanceImplement 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 PlansPerform 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

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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

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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 the bank.

• High Quality Liquid Asset (HQLA) trends, regulatory reports and metrics may be necessary depending on the banks’ size and operation.

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Liquidity Risk MIS Liquidity Reporting Package FrameworkLRM 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.

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Stress Testing: The Core Elements

Base scenario

Stress scenario

1 m

onth

2 m

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3-6

mon

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6-12

mon

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1-2

year

2-3

year

3-4

year

4-5

year

>5

year

1 m

onth

2 m

onth

3-6

mon

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6-12

mon

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1-2

year

2-3

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3-4

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4-5

year

>5

year

Base assumptions

Cashflow systemCashflow by position

Deposit characterization:rate sensitivebalance dynamics

Liquidity gap analysisCashflow generation

Liquidity gap analyses(by scenario)

Scenario 1Base

assumptions

Cashflow systemCashflow by position

Deposit characterization:rate sensitivebalance 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.

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Setting Target State for Liquidity Risk InfrastructureBeing 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

DataData 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

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A Sustainable Liquidity Risk InfrastructureLiquidity 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

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Liquidity Risk Management Data RequirementsLiquidity 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.

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

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Liquidity Risk MIS Reference ArchitectureThe 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 Remediatio

n

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]

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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.

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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.

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Liquidity Risk IT/MIS – Lessons LearnedLessons 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 andIT 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 approachsince books and records data maynot 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.