James Okarimia - Basel II Pillar1 Analytics : Covering Credit, Market,and Operational Risks

1. JAMES OKARIMIA
BASEL II ANALYTICS – PILLAR 1 : Covering – Credit, Market,
Operational Risks.

2. 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. 3
Basel II - based on the concept of 3 Pillars

4. 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. 5
Basel II - the 3 Pillars

6. 6
PILLAR 1 PILLAR 3PILLAR 2
Increased
Supervisory
Power
Increased
Disclosure
Requirements
Minimum
Capital
Requirement
Market
Discipline
Requirements
Supervisory
Review
Process
Rules
To Calculate
Required Capital
New Regulatory Structure Based on Three Pillars
Capital
Adequacy
Basel II – the Three Pillars

7. 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 & Corporate
Concentration Risk
ALM Stress testing
Financial Projection
Models
Upgrade Internal
Rating Systems
Prudential Limit
Global Limit
Country Limit
Private sector Limit
Portfolio and Asset
Concentration Risk
Interest Rate
Risk 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 Discipline
Usage of Metadata ICAAP, Economic Capital,
RAROC*
Rules Based Engine
Capital Adequacy Reporting
Quantitative DisclosuresRisk Assessment Reports
Flexible Reporting
Qualitative Disclosures
Equity Position
Risk
Currency
Risk
Liquidity
Risk
VaR
Calibration
of
PD’s / LGD’s
Definition
of
RWA
*Risk-Adjusted Return on Capital (RAROC)

8. 8
Basel II Risk Analytic Coverage
Basic
Indicator
Standardised
Advanced
Foundation
Standardised
IRB
Operational
Credit
Pillar 2
Regulatory Review
Pillar 3
Market Discipline
Pillar 1
Capital Requirements
Market
Advanced

9. 9
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. 10
Changed Capital Requirement
Minimum Regulatory
Capital
Capital
(Credit & Market) Risk adjusted assets
=  8%
Minimum Regulatory
Capital
Capital
Credit
risk
Operational
risk
Market
risk
=
+ +
 8%
Basel II
Basel I

11. 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. 12
1. What is the probability of a
counterparty going into default?
2. How much will that customer
owe the bank in the case of
default? (Expected Exposure)
3. How much of that exposure
is 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. 13
Expected Loss
(EL) =
Probability of
Default
(PD)
Severity of Loss
(LGD)
Exposure at
Defaultxx
Standardise = External x Regulatory x Regulatory
Rating 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

14. Credit Risk – Functional Architecture
1
4
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

15. Credit Risk – Functional Application
1
5

16. Market Risk
16
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

17. Market Risk – Functional Application
17

18. 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

19. 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.

20. Liquidity Risk Framework
20
Liquidity Risk
Governance &
Oversight
Measurement
Management
R
eporting
Systems &
Controls
O
ff-
Balance
SheetItem
s
Asset-
Liability
M
ism
atch
Regulatory
Reporting
ContingencyFunding Plan
Diversification of
Sources of Funds
Liquid Asset
Buffer
Emergency Day – to - Day
Models
Metrics
Early
W
arning
Indicators
Probabilistic
Behavioural
Scenario

21. 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. 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. 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. 24
Supervisory Review of ICAAP

25. 25
How the Regulators Uses the ICAAP

26. Integrated Risk & Finance Analytics View
Covering RAROC, VaR, RWA, Operational, Market & Credit Reporting
Data 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
RAROC
Capital
ELELExpensesvenue CROR

Re
Credit
Data
Integration&enrichment
Risk & FINANCE DW (Economic Data)
-Loans & Borrowings
-Economic Capital
-Revenue
-Expense
-Budgets
-Risk Capital charges
Risk & FINANCE DW
-Risk Capital- Prudential Limits (RWA)
-RWA for exposures
-Investment Portfolio
-Expected Loss EL
META DATA & BUSINESS RULES
SUPPORT
-Common meta data
-Business rules definitions & support
Integration&enrichment
RWA (Regulatory) Engines
Analytics Engines
EOD Calculators
-VaR
-Stress Testing
-Back Testing
-Prudential Limits
-Operational Risk (via
Dashboard)
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
(Regulatory
Reporting)
Data Mart
(Economic
Reporting)
Data Mart
(Operational Risk
Dashboard)
Reporting
Architecture
Reporting
Engine
Reporting
Engine
ServicesAPIs
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 risk
Data
Market risk
Data
ODS’s
Data
Business
Actors
Traders
Debt
Managers
Operations
Accounting
Management
Regulators
Compliance
Pre deal RWA
Intraday / pre
deal
analytics

27. 27
Regulatory compliance such as Dodd‐Frank, Basel II & Basel III require Big Data demands placed on financial firms
to 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
Scenario
Dodd‐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

28. 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

29. 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 Office
Cross 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

30. 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

31. 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..

32. CONTACT
JAMES OKARIMIA
Managing Partner RM Associates
Janssoniuslaan 30
3528 AJ Utrecht
t: +31 (0) 36 532 2399
m: +31 (0) 6 2319 2655
e: james.okarimia@rmassociates.nl