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JAMES OKARIMIA
BASEL II ANALYTICS – PILLAR 1 : Covering – Credit, Market, Operational Risks.
Credit Risk
• Application scorecard• Risk based Pricing • Credit Line Assignment• Behavior Scorecards• Credit Limit Optimization• Retention /Foreclosure Analysis• Loss Forecasting• Counterparty Risk Scorecards• PD• EAD• LGD
Basel II/III Risk Analytics
2
Market Risk
• Market data • Position Data Analysis
(Trading/Portfolio)• Sensitivity Modeling for
Exposures• Liquidity Risk• Interest Rate Risk• Money Market Fund Sector
Analysis • ALM
Governance
• Basel III / CRD IV / CRR• Solvency 2 framework • SOX • Rating Agency Impact analysis• Data, IT, Infrastructure• Model Management• Portfolio Tracking • Portfolio Stress Testing • Risk Reserves
Compliance Collections and Fraud
• Risk & accounting compliance rules • Credit watch & history reports• Daily compliance dashboards• VaR & Risk Adjusted Rates • Issuer/Lender Concentration • Model Governance
• Collections scorecard• Recovery scores• Delinquency Bucket Analytics• Fraud Detection• Fraud Control & Monitoring • Phishing
3
Basel II - based on the concept of 3 Pillars
4
The Basel II Framework Pillar 1
– Minimum Capital Requirement Calculation Credit Risk Market Risk (little changes vs. Basel I) Operational Risk
– Regulatory Reporting Pillar 2
– Internal Capital Adequacy Assessment Process Capital requirement vs. capital estimates Risk Management
– Pillar 1 Risks– Credit risk concentration– Interest Rate Risk in the banking book– Other Risks : Liquidity, Reputation, Strategic, …
– Supervisory Review Process Audit (External, Internal) Regulatory Supervision
Pillar 3– Firms will have to publish their risk profile and risk data
Supplementary Pillar III reporting, Annexes of Balance Sheet, …
5
Basel II - the 3 Pillars
6
PILLAR 1 PILLAR 3PILLAR 2
IncreasedSupervisory
Power
IncreasedDisclosure
Requirements
Minimum Capital
Requirement
MarketDiscipline
Requirements
SupervisoryReviewProcess
RulesTo Calculate
Required Capital
New Regulatory Structure Based on Three Pillars
Capital Adequacy
Basel II – the Three Pillars
Basel II 3 Pillar Analytics
Pillar I : Capital Adequacy Calculations
Credit Risk Market Risk Operational Risk
Calibration of
EAD
Calculation of
Risk Weights Based on PD & LGD
Limits and Collateral
System
-Counterparty Risk -Country & CorporateConcentration Risk
ALM Stress testing Financial Projection
ModelsUpgrade InternalRating Systems
Prudential LimitGlobal Limit
Country LimitPrivate sector Limit
Portfolio and AssetConcentration Risk
Interest RateRisk and Basis Risk
Support for all 3 approaches
Basic Indicator Approach: Capital Calculated as a percentage of Gross Income
Standardised Approach: Line of Business Based Exposure Indicators
Advanced Measurement Approach: Capital computation as per Opsrisk Loss Data Approach
Approach:Internal Ratings Based Approach (Advanced): IRBA
Standardised Measurement
Methods
Pillar II : Supervisory Oversight Pillar III : Market DisciplineUsage of Metadata ICAAP, Economic Capital,
RAROC*
Rules Based Engine
Capital Adequacy Reporting
Quantitative DisclosuresRisk Assessment Reports
Flexible Reporting
Qualitative Disclosures
Equity PositionRisk
CurrencyRisk
LiquidityRiskVaR
Calibration of
PD’s / LGD’s
Definition of
RWA
*Risk-Adjusted Return on Capital (RAROC)
8
Basel II Risk Analytic Coverage
BasicIndicator
Standardised
Advanced
Foundation
Standardised
IRB
Operational
Credit
Pillar 2Regulatory Review
Pillar 3Market Discipline
Pillar 1Capital Requirements
Market
Advanced
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Approaches Risk Components Mitigation
Basel I Counterparty Nature (Sov, Corp, OECD country etc)
Supervisory values
Limited set of eligible risk mitigants
Substitution of RW
Basel II
Standardised
External Ratings
Supervisory values
Limited set of eligible risk mitigants
Basel II
IRB Foundation
PD : by the bank
LGD, EAD : fixed
More eligible mitigants
Apply on PD, LGD, EAD
Basel II
IRB Advanced
PD, LGD EAD : by the bank
Even more eligible mitigants
Apply on PD, LGD, EAD
Basel Credit Risk Approaches Overview
10
Changed Capital Requirement
Minimum RegulatoryCapital
Capital
(Credit & Market) Risk adjusted assets
= 8%
Minimum RegulatoryCapital
Capital
Creditrisk
Operationalrisk
Marketrisk
= + +
8%
Basel II
Basel I
11
Credit Risk
• Basel II places emphasis on improving the management and measurement of credit risk
• The measurement of credit risk implies assessing the borrower’s creditworthiness.
12
1. What is the probability of a counterparty going into default?
2. How much will that customer owe the bank in the case ofdefault? (Expected Exposure)
3. How much of that exposureis the bank going to lose?
“Probability of Default”
“Loan Equivalency”(Exposure at Default)
“Severity”(Loss Given Default)
PD
LGD
EaD
=
=
=
X
X
Size of Expected Loss “Expected Loss“ EL=
=
Components of Credit Risk
13
Expected Loss(EL) =
Probability of Default(PD)
Severity of Loss(LGD)
Exposure atDefaultxx
Standardise = External x Regulatory x RegulatoryRating Imposed Imposed
IRB = Proprietary x Regulatory x Regulatory
Foundation Rating Imposed Imposed
IRB = Proprietary x Proprietary x Proprietary
Advanced Rating Severity Exposure
Credit Risk Components
Credit Risk – Functional Architecture
14
Evolution of regulatory framework has added to the complexity of models requiring banks to further their: Risk Control and reporting process Data Management Processing capabilities of the systems
Credit Risk – Functional Application
15
Market Risk
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Experience Snapshot Process Consulting for
VaR Calculation for leading bank in Singapore
Portfolio Assessment Modeling for large hedge fund
Performance Evaluation System for One of the top US Investment Consultants
Strategic Exposure Limits Management for One of the Largest Investment Banks
Client Information Management System (CIMS) for Leading US West Coast Bank
Market Risk – Functional Application
17
18
Operational Risk
• Capital requirement for Operational Risk (OR) introduced• Banks’ OR models not as developed as for Credit Risk• Operational Risk (OR) will add to banks’ regulatory capital
requirements• Increased cost for OR might offset any capital savings on Credit Risk• Operational risk is not restricted to banks, it’s present in all
organisations including yours
Operational Risk Application
19
Experience Snapshot Operational Risk Data
Capture for Large European Bank
Capital Charge Calculation & Reporting for Large Canadian Bank
Operational Risk Management System for Reputed Bank in Scotland
Operational Risk Data Capture & Reporting for Premier Provider of Asset Servicing, Fund Admin & Investment Mgmt.
Liquidity Risk Framework
20
Liquidity Risk
Governance & Oversight
Measurement
Management
Reporting
Systems & Controls
Off-Bala
nce
Sheet
Items
Asset-
Liabil
ity
Mismatc
h
Regulatory Reporting
Contingency Funding Plan
Diversification of
Sources of Funds
Liquid Asset Buffer
Emergency Day – to - Day
Models
Metrics
Early W
arning
Indica
tors
Probabilistic
Behavioural
Scenario
21
Internal Capital Adequacy Assessment Process Emphasis is on ‘P’ – Process
Confusingly, ICAAP now also refers to the calculated capital figure
A process by which a firm assesses its risks and mitigation for its business and sets appropriate levels of risk capital
An ICAAP is specific to each firm– Minimum standards apply– Fit for its purpose– Appropriate to the risks assumed
There is no prescriptive definition of an ICAAP.– senior management ownership and responsibility for own process – FSA will review through ARROW assessments.
22
Overview of Pillar 2 and ICAAP
CAPITAL: Relationship between Pillar 1, Pillar 2 and the ICAAP
minimum capital requirement;
calculated using prescribed parameters (advanced or standardised).
Pillar 1 Pillar 2 ICAAP
the firm's own assessment of its capital needs;
need not be calculated by reference to regulatory capital (firms which use economic capital models will express their capital using a variety of measures e.g. tier 1, shareholders funds).
supervisory assessment of the amount of regulatory capital necessary to cover: Pillar 1 risks (including any
uncertainties in their calculation); and
risks not included in Pillar 1. calculated on a forward-
looking basis through, at least, an economic downturn.
23
ICAAP should covers …
Other Risk Types
Interest rate risks in the banking book–Maturity transformation
Pension risk–Liabilities
Business risk–Market volume volatility–Competition
Liquidity risks–Funding & Refinancing
Strategic risks–Political / Legal / Social
Risk types in pillar 1
Counterparty risks–Credit risk–Settlement risk–Country risk–Equity risk
Operational risks–Risks caused by persons, processes, technology and external impacts
Market risks in the trading book–Interest rate risks–Special risks–Currency risks–Credit spreads
24
Supervisory Review of ICAAP
25
How the Regulators Uses the ICAAP
Integrated Risk & Finance Analytics View
Covering RAROC, VaR, RWA, Operational, Market & Credit ReportingData Warehouse: The Bank wide data warehouse stores the raw and processed data from the calculation engines. It holds transaction level data and enables views of the data by multiple dimensions e.g. counterparty, general ledger account, functional organization, product etc. data is extracted from the business units specific systems as frequently as is required to provide timely and meaningful bank wide views of risk
RAROCCapital
ELELExpensesvenue CROR Re
CreditData
Integration & enrichment
Risk & FINANCE DW (Economic Data)-Loans & Borrowings-Economic Capital-Revenue-Expense-Budgets-Risk Capital chargesRisk & FINANCE DW-Risk Capital- Prudential Limits (RWA)-RWA for exposures-Investment Portfolio-Expected Loss ELMETA DATA & BUSINESS RULESSUPPORT-Common meta data-Business rules definitions & support
Integration & enrichment
RWA (Regulatory) Engines
Analytics EnginesEOD Calculators-VaR-Stress Testing-Back Testing-Prudential Limits-Operational Risk (viaDashboard)
INTRADAY Calculators-VaR-Trade position -Real Time Limits-Desk Level Analytics-Operational Availability(via dashboard)
Accounting Engine-P&L-RAROC-Other Accounting Measures GL
Data Mart(RegulatoryReporting)
Data Mart(EconomicReporting)
Data Mart(Operational Risk
Dashboard)
Reporting Architecture
ReportingEngine
ReportingEngine
Services APIs
RISK DIMENSIONS:
-Market Risk
-Credit Risk
-Operational Risk
-Prudential & Operational Limits
-Risk Capital Charges & Measures
1
2
3
1. Example RAPM equation for illustration
2. This represents a shared architecture for both EOD & intraday pre deal analytics 3. RISK DASHBOARD for operational quantitative & graphical risk evaluations
Financial Data
Client riskData
Market riskData
ODS’sData
Business
Actors
Traders
Debt Managers
Operations
Accounting
Management
Regulators
Compliance
Pre deal RWA
Intraday / pre
deal
analytics
27
Regulatory compliance such as Dodd‐Frank, Basel II & Basel III require Big Data demands placed on financial firmsto track the source of data, how it has changed over time, and who has changed
Critical success factors Integration of risk data from different source & building large iCAAP risk warehouse Creation of counterparty risk environment to support AIRB implementation of Basel II Integration of business definition, metadata and data Governance across business lines into iCAAP warehouse to facilitate
reporting Creation of Risk Analytic reporting to support Basel II – Capital & Economic calculation and Fed reporting Configuration and support of 3rd party Risk calculation engines and RWA calculation for Basel II reporting
Critical success factors Integration of risk data from different source & building large iCAAP risk warehouse Creation of counterparty risk environment to support AIRB implementation of Basel II Integration of business definition, metadata and data Governance across business lines into iCAAP warehouse to facilitate
reporting Creation of Risk Analytic reporting to support Basel II – Capital & Economic calculation and Fed reporting Configuration and support of 3rd party Risk calculation engines and RWA calculation for Basel II reporting
Envisaged Benefits Reduced close to 55% of the risk based capital allocation
Compliant within a year due to the implementation of large iCAAP data warehouse
Meet Fed Basel II reporting needs
Creation of counterparty risk data mart to facilitate internal risk ranks
ScenarioDodd‐Frank regulation – Large volumes of OTC derivatives need to be cleared at CCPs (Central Counter Party) require the clearing and risk management systems to be able to handle the volumes
Basel II & III regulations – Key requirements for voluminous credit data storage and management include maintaining a cradle‐to‐grave history of obligors increasing data storage needs. Analytical reporting as per Basel III for calculating NSFR (Net Stable Funding Ratio) & LCR (Liquidity Coverage Ratio) also need large volumes of data processing
Big Data Usage Integration and Aggregation
Storage Management
Processing
Analytics
Regulatory Compliance Driving Big Data Analytics
Enterprise Risk Analytics | RFC (Risk, Fraud & Compliance)
Executive Dashboards around BASEL II / III with Dodd Frank and Adaptive Revenue Assurance
Machine‐learning modules for fraud detection, to strengthen entry to the real‐time analytics market
Predictive analytics and new features to cover areas in risk and governance prediction
Smarter fraud detection capabilities reduce losses and improved recoveries
Flexible systems and processes to accommodate changing regulatory requirement
Proactive risk management across LoBs and product lifecycles with stress testing and scenario analysis
RFC Data and Analytics Platform
Holistic Risk Assessment, Fraud detection and Compliance application that ensures adherence to constantly changing regulatory requirement
BI Apps
App Features Benefits
28
Business IT Radar for Data Analytics
29
Low Business Model Fitment
Medium Business Model Fitment
High Business Model Fitment
Emerging
Adolescent
Early Mainstream
Operational Efficiency
Impact on business
Save Cost
Defend Business
Grow Business
Service Category
Front Office
Back OfficeCross Selling
Bank Exposure
Fraud Detection
Propensity Models
Payments Intelligence
MPP Data Appliances
Columnar Databases
High Performance Compute Clusters
NoSQL Platforms
Context Driven Offers
Customer Risk Profile
Customer Retention
Integrated & Unified Trade Data Analytics
30
Big Data Usage• Processing
• Analytics
• Integration and Aggregation
• Storage Management
• Reporting / Dashboarding
• Monitoring
Consolidate the trades & positions across all asset classes and geographies for a birds eye view trade performance,risk, trade analytics, and optimization of trading costs
The need for multiple desks trading similar products to leverage same/aligned process flows enabling the firm to increase trade efficiency and accuracy, decrease the time required to market new products, and adapt more quickly to changing market conditions
Analyze Volume and size of trades: By desk, LoB, product, execution
venue, clearing venue and geography. Trade Volumes help to obtain “per trade” metrics (cost,
revenue, profits, resources, technology)
Calculate / Unify risk: Get a single view into market and credit risk. Calculate credit risk across products / LE for a counterparty
Identify distribution of execution costs by LoB, products and desks.
An analysis can result in rationalization of technology,
people and processes to optimize execution cost structures
Unified Trade Data
Analytics / MIS
Client Service
Risk & Profitability Calculations
Cost Control & Operations
Mgmt
Scenario Scenario
Scenario Scenario
Integrated & Unified Trade Data Analytics | Envisaged Benefits
31
Improved Post Trade support and “Where’s my Trade” transparency Seamless presentation of Client data and dashboards Better communication with clients with better management of trade confirmations Support for sales trader, execution management and international order handling across asset classes Better information resulting in faster product roll outs; better pricing Cross product margining can be used to provide material benefits to clients
Enhanced, top‐down view of internal trading volumes, the financials of each trade, the counterparties involved and execution metrics MIS information like
• Trends in revenue, profitability and costs • Trade distributions by execution venues, clearing venues, LoB, Desk and Product
Correlations between revenue, cost and profitability. Negative correlations indicate that investments are not producing sufficient returns Identify profitability by client/s: Identification of profitable clients would result in optimization of investments in Sales (people, processes and tools) to retain valuable customers and go after more profitable segments Correctly apportion ‘real’ costs and identify most profitable business units
Improve the speed and execution accuracy of hedging activities with one consolidated view of positions across asset classes. This can help the firm’s hedging activities focus internally and not often priced by and remain on the books of– the originating desk. Consequently there will be less deals facing the street and more internally. This will also lead to broker fee savings and wire transfer/administrative fee savings Affirmations tracking; improved trade tracking and enhanced STP Distribution of funding and collateral costs. An analysis would result in better transfer pricing mechanisms and tools for banks Bank can optimize which brokers it uses (internal / external) based what they charge Improve costs & overheads associated with Reconciliation
Improvements in Regulatory Reporting processes Accurately calculate PnL, build centralized PnL calculators Improvement in cross product netting can free up a lot of capital Improved ability to simulate Risk Stress scenarios and identify risk
concentrations. Stress is a hot topic in regulatory space Pre‐trade risk checks become possible with unified trade
information Distribution of Credit, Market and Operational risks
Client Service: Unified Trade data can facilitate.. Analytics & MIS: Unified Trade data can be used for..
Risk & Profitability: Unified Trade data can be used for..
Cost Containment: Unified Trade data can facilitate..
CONTACT
JAMES OKARIMIA Managing Partner RM AssociatesJanssoniuslaan 303528 AJ Utrechtt: +31 (0) 36 532 2399m: +31 (0) 6 2319 2655e: [email protected]