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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 Op...
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 ...
5
Basel II - the 3 Pillars
6
PILLAR 1 PILLAR 3PILLAR 2
Increased
Supervisory
Power
Increased
Disclosure
Requirements
Minimum
Capital
Requirement
Mark...
Basel II 3 Pillar Analytics
Pillar I : Capital Adequacy Calculations
Credit Risk Market Risk Operational Risk
Calibration
...
8
Basel II Risk Analytic Coverage
Basic
Indicator
Standardised
Advanced
Foundation
Standardised
IRB
Operational
Credit
Pil...
9
Approaches Risk Components Mitigation
Basel I  Counterparty Nature
(Sov, Corp, OECD country etc)
 Supervisory values
...
10
Changed Capital Requirement
Minimum Regulatory
Capital
Capital
(Credit & Market) Risk adjusted assets
=  8%
Minimum Re...
11
Credit Risk
• Basel II places emphasis on improving the management and
measurement of credit risk
• The measurement of ...
12
1. What is the probability of a
counterparty going into default?
2. How much will that customer
owe the bank in the cas...
13
Expected Loss
(EL) =
Probability of
Default
(PD)
Severity of Loss
(LGD)
Exposure at
Defaultxx
Standardise = External x ...
Credit Risk – Functional Architecture
1
4
Evolution of regulatory framework has added to the complexity of models requirin...
Credit Risk – Functional Application
1
5
Market Risk
16
Experience Snapshot
 Process Consulting for
VaR Calculation for
leading bank in Singapore
 Portfolio Asse...
Market Risk – Functional Application
17
18
Operational Risk
• Capital requirement for Operational Risk (OR) introduced
• Banks’ OR models not as developed as for ...
Operational Risk Application
19
Experience Snapshot
 Operational Risk Data
Capture for Large
European Bank
 Capital Char...
Liquidity Risk Framework
20
Liquidity Risk
Governance &
Oversight
Measurement
Management
R
eporting
Systems &
Controls
O
f...
21
Internal Capital Adequacy Assessment Process
 Emphasis is on ‘P’ – Process
 Confusingly, ICAAP now also refers to the...
22
Overview of Pillar 2 and ICAAP
CAPITAL: Relationship between Pillar 1, Pillar 2 and the ICAAP
 minimum capital
require...
23
ICAAP should covers …
Other Risk Types
Interest rate risks in the banking book
–Maturity transformation
Pension risk
...
24
Supervisory Review of ICAAP
25
How the Regulators Uses the ICAAP
Integrated Risk & Finance Analytics View
Covering RAROC, VaR, RWA, Operational, Market & Credit Reporting
Data Warehouse: ...
27
Regulatory compliance such as Dodd‐Frank, Basel II & Basel III require Big Data demands placed on financial firms
to tr...
Enterprise Risk Analytics | RFC (Risk, Fraud & Compliance)
 Executive Dashboards around BASEL II / 
III with Dodd Frank a...
Business IT Radar for Data Analytics 
29
Low 
Business 
Model 
Fitment
Medium Business 
Model Fitment
High 
Business 
Mode...
Integrated & Unified Trade Data Analytics
30
Big Data Usage
• Processing
• Analytics
• Integration and Aggregation
• Stora...
Integrated & Unified Trade Data Analytics | Envisaged Benefits
31
 Improved Post Trade support and “Where’s my Trade” tra...
CONTACT
JAMES OKARIMIA
Managing Partner RM Associates
Janssoniuslaan 30
3528 AJ Utrecht
t: +31 (0) 36 532 2399
m: +31 (0) ...
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JAMES OKARIMIA BASEL II - PILLAR 1 ANALYTICS - Covering Credit,Market,and Operational Risks

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JAMES OKARIMIA BASEL II - PILLAR 1 ANALYTICS - Covering Credit,Market,and Operational Risks

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JAMES OKARIMIA BASEL II - PILLAR 1 ANALYTICS - Covering Credit,Market,and Operational Risks

  1. 1. JAMES OKARIMIA BASEL II ANALYTICS – PILLAR 1 : Covering – Credit, Market,  Operational Risks.
  2. 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. 3 Basel II - based on the concept of 3 Pillars
  4. 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. 5 Basel II - the 3 Pillars
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. Credit Risk – Functional Application 1 5
  16. 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. 17. Market Risk – Functional Application 17
  18. 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. 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. 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. 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. 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. 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. 24 Supervisory Review of ICAAP
  25. 25. 25 How the Regulators Uses the ICAAP
  26. 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. 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. 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. 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. 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. 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. 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

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