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

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

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

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

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: james.okarimia@rmassociates.nl