basel ii it advisory (en).pptx

8/11/2019 BASEL II IT ADVISORY (EN).pptx

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Basel II Technology Enablement

IT Advisory Services

February 9, 2009


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IT Advisory Services, FSO, FEAPage 2

Business Challenges

Strategic

Become Basel II compliant by 2010 at advanced level (reporting

RWA using IRB*) under enormous pressure from government

Improve risk management rigor in a fluctuating market

Become compliant for fund raising capability in foreign capital

market

Reduce minimal capital requirement and free up more earning asset

Tactical

Hard to map and translate Basel II required data to source system

data Insufficient, missing, inconsistent, and inaccurate historical data

Tedious data collection from different line of business for operating

risk reporting

Decentralized and non-standardized source systems and data

Substantial amount of report generation and validation effort

Prohibitive IT infrastructure costs to support Basel II reporting

Strong project management office and data management group,with rigorous change management

Prioritization of Basel II scoping by portfolios and source

systems, using Pareto analysis, and mixing basic standard and

advanced approaches

Deploying resources knowledgeable in both Basel II and

business intelligence for report definition

Historical data conversion by phases

Implementing multidimensional data model with efficient

reporting tools Design (or optimize) data extract, transform, load (ETL) process

Challenges Short-Term Quick Wins

Strong project management office and data management group,

with rigorous change management and IT governance

Implementing (or enhancing) credit workflow system, collateral

management system, treasury system, and customer

relationship management

Implementing enterprise data warehouse (EDW) with single

view of customer (SVC)

Long-Term Solution

* RWA: Risk Weigh Asset; IRB: Internal Rating Based


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Basel II Requirement

Pillar 1

Minimum

Capital

Requirement

Pillar 2

Supervisory

Review

Pillar 3

Market

Discipline

Credit Risk

Operational Risk

Market Risk

Internal Capital Adequacy

Appraisal Process (ICAAP)

Supervisory Review & Evaluation

Process (SREP)

Stage 1 Stage 2 Stage 3

Standardized Foundational IRB Advanced IRB

BasicAlternate Standardized

Approach (ASA)

Advanced Measurement

Approach (AMA)

Basic Standardized Internal Model

Loss Event Capturing Line of Business Alignment

Loan Categorization (RWA,

from LGD, EAD, PD*)

Credit Scorecard & S&P/TU**

Required

Historical Loss Recovery

Required

Treasury Portfolio

CategorizationValue at Risk (VaR) Required

Risk Management Reporting (Liquidity, Interest Rate, Concentration, etc.)

Economic Capital, Stress Test, Exposure Aggregation

Scope, Capital Structure, Capital Adequacy, Equity, Securitization,

Credit Risk, Market Risk, Operational Risk, Reconciliation,

Interest Rate Risk in Banking Book (IRRBB)Disclosure

3-Year Application of RatingSystem

5-Year of Historical Data forPD, LGD, EAD

5-Year of PD

Retail: 5-Year of LGD, EAD

Non-Retail: 7-Year of LGD, EAD

* LGD: Loss Given Default; EAD: Exposure At Default. ** S&P: Standard & Poors Rating; TU: Trade Union (Credit Bureau).


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Technology Challenge

Data Management

Collection of all needed information required by

Basel II for all transactions

Lack of change control during report development

Mapping of complex structures (client, collateral)

Collect enterprise wide operational risk data, such

as loss event value and incident reporting

RWA Calculation Process

Ability of daily valuation of the banks total balance Analysis of complex contractual relationships

Optimization of the collateral allocation

Splitting of derivative instruments

Storage of Results / Historical Time Series

Saving parameters of internal models for the

estimation of PDs, CCFs, LGDs

Disclosure Reporting

Flexible design in order to fulfill both external

disclosure requirements as well as internal

reporting requirements

Efficient report generation and nightly batch jobs

Data Management

Source Data Analysis

Data Quality Analysis & Cleansing

Data Gap Assessment

Data Collection Portal (e.g. Operational Risk)

Ongoing Data Governance

RWA Calculation Process

Risk Engine Implementation

ETL Design (e.g. Single View of Customer)

Package Selection

Storage of Result / Historical Time Series

Data Model Design

Metadata Management

Data Retention Policy

Data Access & Security

Disclosure Reporting Report Design

Report Development (e.g. Supervisory Report)

Report Logic Validation

Dashboard of Key Risk Indicators


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Credit Risk Technology Enablement

Project Management

Project Charter

Scope (Pillar I, II, or

III; Credit,

Operational, or

Market Risk)

Target Approach(Standardized,

Foundation, or

Advanced;

Qualitative or

Quantitative)

Country Specific

Approach

Regulatory Schedule

Budget

Team Organization

Data Governance


Diagnosis Design SustainIdentify Deliver

Application Inventory

Infrastructure

Assessment

Data Dictionary

Data Quality Report

Project Formation

Report Definition

Report Definition (with

Calculation Logic)

Interface Specification

for Risk Engines

Data Modelling

Operational Data

Store Schema

Logical Credit

Risk Data Mart

(CRDM) Schema

Basel Rating Model

Retail Rating

Modeling

Non-Retail Rating

Model

Business Definition

Product Mapping

Line of Business

Alignment

Portfolio Inventory

Data Management Group Formation

Meta Data Management (Process, Specification, Tool)

Transformation Logic

ETL Design

Specification

Report Design

Report Specification

Risk Engine

Implementation

Report

Development

Risk Engine

Engine Configuration

Package Selection

ETL

Development

Physical Data

Model

Implementation

Source Data Dictionary

ImplementationValidation

Data Cleansing

Vendor Short List

Selection Criteria

Vendor Rating

Report

Template

Report

Population

Coding Report

Population

Scheduling

BAS/TSRS

Vendor

FSRM


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Operational Risk Technology Enablement

Project Management

Project Charter


III; Credit,

Operational, or

Market Risk)


Foundation, or

Advanced;

Qualitative or

Quantitative)

Country Specific

Approach

Regulatory Schedule

Budget

Team Organization

Data Governance

Data Definition


Loss Event & Value

Capture Requirement

Loss Incident Record

Requirement

Project Formation

Report Definition

Report Definition Economic Capital

Model

Scenario Analysis

Requirement

Data Modelling

Incident Data

Model Schema

Portal Design

Intranet Portal

Design (for Data

Capture)

Business Definition

Line of Business

Mapping



Report Design


Risk Engine

Implementation

ReportDevelopment

Risk Engine

Economic Capital Model

Scenario Analysis

Package Selection

Physical Data

Model

Implementation


Im

plementationValidation

Vendor Short List

Selection Criteria

Vendor Rating

Report

Template

Report

Population

Coding

Report

PopulationScheduling

BAS/TSRS

Vendor

FSRM

User Interface

Development

Portal

Development


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Market Risk Technology Enablement

Project Management

Project Charter


III; Credit,

Operational, or

Market Risk)


Foundation, or

Advanced;

Qualitative or

Quantitative)

Country Specific

Approach

Regulatory Schedule

Budget

Team Organization

Data Governance

Data Definition


Market Data

Position Data

Time Series Data

Project Formation

Report Definition

VaR Report Individual Desk

Traffic Light System

Limit Utilization

Data Modelling

Data Model

Schema

Model Definition

VaR Model

Pricing



Report Design


Risk EngineImplementation

Report

Development

Risk Engine

Pricing Engine

VaR Model

Model Test

Stress Test

Package Selection

Physical Data Model

Implementation


Im

plementationValidation

Vendor Short List

Selection Criteria

Vendor Rating

Report

Template

ReportPopulation

Coding

Report

Population

Scheduling

BAS/TSRS

Vendor

FSRM


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Supervisory Report Technology Enablement

Project Management

Project Charter


III; Credit,

Operational, or

Market Risk)


Foundation, or

Advanced;

Qualitative or

Quantitative)

Country Specific

Approach

Regulatory Schedule

Budget

Team Organization

Data Governance



Credit Risk Data Mart

Risk Engine Result

Data Dictionary

Project Formation

Report Design

Dashboard

Design

Logic Definition

Business Definition

Key Risk Indicators

Meta Data Management (Report Definition)

Report

Development

Package Selection


ImplementationValidation

Vendor Short List

Selection Criteria

Vendor Rating

Report

Template

Report

PopulationCoding

Report

Population

Scheduling

Visualization

Tool Installed

Tool

Configuration User Training

BAS/TSRS

Vendor

FSRM


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Architecture Principle

Business Driver

Operational Data Management

Enable the organization to collect

the right data at the right level of

granularity

Effective Data Analysis

Manage data processing and

reporting

Compliance Demonstration

Demonstrate compliance to the

regulator

Technical Driver

Right mix of centralization and

decentralization

Extensibility and scalability to support

evolving requirement

Workflow process management

Source data analysis and collection process

Data transformation logic

Data quality assurance and data cleansing

Enforceable consistency, quality, validation,

and auditability of data


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Basel II Technology Architecture

Data Store

Extraction

Transformation

& Calculation

Reporting

Operational Systems

Data Sources

Data Marts

Credit Risk

Product Systems & Corporate Data

Core Banking Products Credit Collateral

Regulatory ReportingBasel II Reporting

(Credit, Op. Risk, Interest)

Customer

Internal Reporting

Market Risk Operational Risk

Historic Data Store

Extraction, Transformation, Validation, Loading

BII Operational

Risk Engine

Rating

Engine

Basel II Credit

Risk Engine

Market

Risk Engine

Internal Credit

Risk Engine

Loss Data Store

Result Data Result Data Result Data Result Data

PD

Engine

LGD

Engine

CCF

Engine

Centralized Data Store


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Major Risk Management Solution

Data Mapping &

Standardization

Counterparty Risk Types

Maturities

Ratings

Collateral

Valuations

Data Store

(Warehouse)

Risk History Loss History

Rating History

Transactions

Positions

Calculation (Risk Engine)

PD, EAD, LAD, M

Capital Allocation

P1: RWAs

P2: Stress Testing

Reporting

Basel II P3

Value At Risk

Local Regulatory

Large Exposures

Economic Capital

Performance

Measurement

Financials

Data Collection

Transaction

Counter-Party

Ratings

Market Data

Default Data

Collateral

Credit Risk

Mitigation

Oracle SAP

SAS

Sungard

Moodys KMV

Standard & Poors

Cognos

Informatica

Metamatrix

Fitch

SAP SAS

Oracle

Sungard

Fitch / Algo

Metamatrix

Moodys KMV

Almonde

Cognos

FTI

IBM

Informatica

Fermat FinArch

SAP

SAS

Oracle

Sungard

Cognos

FTI

IBM

Informatica

Metamatrix

Trillium

Fermat Algorithmics

FinArch

Kamakura

SAP

SAS

Oracle

Whitelight

FTI

Almonde

Reuters

Misys

Reuters

Reveleus

SAS Fitch / Algo

FinArch

Sungard

Oracle

Kamakura

Metamatrix

Reuters

Almonde

BusinessObjects

Cognos

FRS

FunctionalCompon

ent

KeyVendor


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Solution Selection

Risk Weighed Asset (RWA) Asset Liability Management (ALM)


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Credit Risk Technology


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Legend

Credit Data Requirement

Borrower

Basel II Derived Data

Supporting Raw Data

Rating (with History)

Quantitative Borrower Data

Qualitative Borrower Data

General Information

Grade

Exposure Type

Transaction

Instrument

Collateral

Securities, Guarantees

Netting

EAD

LGD

M

Borrower

Legal Data

Data Pool

Interest & Exchange Rate

Rating Statistics

Counterparty Clustering

External Rating

Model Specific Market Data

Grade

PD Map

Portfolio & Control

Limit

By Business Line

By Location

By Customer

By Industry

By Country

Portfolio Structure

Internal & External

Organizational Data

Aggregation & Report

Aggregated Risk Figure

By Borrower

By Exposure Class

By Business Unit

Granularity Adjusted Risk

Risk Weighted Asset

Report Data

Optional & Control Data

Important Reference Link


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Gap Analysis Approach

Gap Analysis& Action Planning

Data AssessmentPortfolio InputProduct Mapping

Systems/Products Inventory

Inventories bank systems that contain

products subject to Basel II

For each system, inventory banks

products and map them to Basel II

categories

Portfolio Characteristics

Inventories banks portfolios

Automatically links Basel II data

requirements to portfolios and systems.

Enable structured assessment of data

availability in targeted segments

Data Assessment

Performs gap analysis to compare

available client data against Basel II

credit data requirements

Maintains remediation plans


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Treasury

Portfolio Monitoring & Reporting

Portfolio Management


DisbursementUnderwritingCredit Application

Credit Management

Payment

Tracking

&

Follow

Up

Concentration

& Ratings

Monitoring

Reporting

Collateral

Tracking

Revaluation

Portfolio

Visualization

Reporting &

Drill-Down

NPL

Tracking &

Reporting

Customer,

Account,

Product

Trending

Analysis

Portfolio

Quantification

Marginal

Pricing

Risk-

Based

Pricing

Quantitative

Limit

Management

& Treasury

Integration

Credit

Derivative &

Credit

Securitization

Basel II

Compliant

Financial

& Ratings

Advanced

Financial &

Comparative

Analysis

PD

Modeling

Back Office

Settlement

Accounting

Profit & Loss

Revaluation

Middle Office

VaR

Stress Testing

Front Office

Pricing

Hedging

Limit

Limit

Rate &

Curve

Analytic

Library

Commercial Lending

Back Office

Settlement

Accounting

Front Office

Pricing

Sanction

Limit

Limit

Rate &

Curve

Retail Lending

Collection

Arrear

Loss & Recovery

Front Office

Credit Approval

Document

Management

Account

Interest Repayment

Schedule

Group Reference

Data

Group

Transaction Data

Group Limit Data

Market Time

Series Data


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Data Collection

Exposure

Characteristic time to maturity

Currency of exposure

Product type

Rating

Allocation of collateral

Exposure amount (basic credit line)

Seniority

Characteristic revocability

Characteristic delinquency status

Expected Loss

Risk segment

Characteristic regional financing

Allocation of guarantees

Risk weight category

Specific provision

Characteristic interruption of payments

Characteristic netting category

Collateral

Type of collateral

Value of the collateral (C)

Currency of the collateral

Characteristic recognized

Date at which collateral must be pledged

Date of last valuation

Characteristic re-margining

Guarantee

Time to maturity

Value of the guarantee (G)

Seniority


Materiality thresholds

Credit Derivative

Borrower

Rating

Sovereign of incorporation

Industry

Guarantor


Rating

Rating

Credit risk measurement

Rating

Date of ratings

Term

Type of Security

Issues assessment

Seniority

Comparable rating

Characteristic main index

Characteristic traded on a recognized exchange

Characteristic UCITS recognized

Market inputs

PD, LGD, CCF

Currency rates

Security prices

Interest rate curve

Volatility of foreign currencies, securities, interest rates

Process

Valuation approach

Data use

Process

Market Inputs

Borrower

Guarantor

Collateral

Issuer

Exposure

Guarantee

Rating

Credit Derivative

Type of Security

Collateral Provider


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Time Series Data

Source

Financial Data Store

Manual Tracking

External Data Provider

Internal Systems

Business Partner Data

Partner ID

Industry Type

Customer Group

Rating

Rating

Date

Default

Default Event

Date

Risk Drivers

Rating System/Method

Balance Sheet

Loss Data

Provision

Recovery

Contract Data

Facility Type

Outstanding

Migration Matrix

Default Ratet

PDt

LGDt

CCFt


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Credit Risk Data Mart (CRDM) Model

Report Definition

Logical Data Model

Physical Data Model


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Data Transformation Logic

Determining Risk Weight

Determining the Basel II risk class of

the borrower Determining master rating category

Determining the master rating

applicable to the approach

Determining the risk weight (r)

Determining Exposure at Default (EAD)

Determining credit conversion factor

(CCF)

Determining market value /assessment base /add on-risk driver

Determining exposure at default (EAD)

Balance sheet positions

Book value (after provisions)

Amount after on-balance-sheet-

netting

Off-balance sheet positions

Open bank line * CCF

Amount after (Repo-Lending-)Netting

Derivatives MTM + regulatory add-on factor

Amount after netting

Determining Risk Weighed Assets (RWA)

Determining eligibility of collaterals by

means of quantitative and qualitative criteria Determining currency mismatch

Determining maturity mismatch

Determining haircut factors

Determining the adjusted risk weight (r)

Determining probability of default (PD, IRB)

Determining loss given default (LGD, IRB)

Determining the adjusted value of the

collateral (CA)

Determining the adjusted value of theguarantee (GA)

Optimized distribution of collateral and

guarantees

Determining regulatory capital requirement

The challenge here is in particular the

distribution of assessed collateral on

individual transactions

Stress Testing

Scenarios on market data

Interest rate curve, exchange rates,

security prices

Scenarios on determining rating categories

Ability of determining a systematic inferior

rating

Pre-Processing Functions

Determining Exposure at Default (EAD)

Determining Risk Weight

Determining Risk Weighed Assets (RWA)

Stress Testing

Pre-Processing Functions

Selecting relevant data

Currency conversion

Offsetting of accounts (complex structure)

Distributing basic credit line

Determining undrawn amount (complex

structure)

Determining the approach for the calculation

process

Determining original maturity

Determining time to maturity

Determining period of market value


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Rating Model Development

Independence of rating approvers from sales and marketing

Use of ratings in risk management

Independent review of ratings consistency/accuracy

Independent quantification validation

Board and Senior Management oversight

Reference data sets for parameter estimation

Application of statistical techniques to the

reference data set to establish PD, LGD, andEAD

Linkage of portfolio data to reference data

based on common characteristics

Application of loss parameters to actual

portfolio data

Validation and annual update of quantification

estimates

Data Maintenance

Maintain essential data for obligors and facilities throughout the life and disposition of

the credit exposure, including inputs to ratings assignments

Maintained in controlled environment

Support ratings monitoring, parameter quantification, capital calculations, reporting

Two-dimensional rating system

Universal rating scale

Product/industry specific methods Consistent rank ordering of risk

Meaningful differentiation by grade

Consistency in ratings assignment

Ratings assignment transparency

Calibration of obligor grades to actual

long run default frequencies and

LGDs to realized loss rates

Economic

Capital

Credit Limits

and Reporting

Regulatory

Capital

Control & Oversight

Rating Assignment Quantification

Data Maintenance


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Operational Risk Technology


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Legend

Operational Risk Management Framework

Strategic Decision

Business Initiative

Risk Measurement

Local Standards / Basel II

SOX

Basel II Specific

Business Value

Operational Risk FoundationVision, Guiding Principles, Risk Strategy, Risk Appetite, Organisation Structure, Risk Glossary

Risk Identification & Assessment

Regulatory and Economic Capital Model

Risk Reporting

Risk Monitoring

Risk & Control

Assessments (RCA) Loss DataKey Risk Indicators (KRIs)


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Loss Data

Identifying and agreeing on the true short term and long-term objectives

Capturing loss events Sourcing

Frequency

Completeness

Accuracy

Value vs. Effort

Workflow of Process and developing the front-end

Multiple data sources Making the capture process less painful (data trees)

Life cycle of a loss event

Non static nature of data

Developing the data map

Categorization along multiple dimensions

Many data elements

Mapping and enrichment of existing data sources Inherent challenges in the environment

Inconsistent use of general ledger accounts that do not map well to Basel

Inherent conflict of interest, incentives, and motivation

Deployment, training and integration into business processes

Linkage to other risk and business processes


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Loss Data Infrastructure

Operational Risk Incident Database

Data Capture

General Ledger

Operational System

Business Internal Loss

External Loss

Identification of Potential SOX Issue

Alignment with SOX Action Plan

Economic and regulatory capital models

Back-Testing of KRIs Risk & Control Assessment Validation

Root Cause and Scenario Analysis

Loss Distribution

When

Occurrence Date

Discovery Date

Resolution Date (Target)

Resolution Date (Actual)

Recovery Date

Write-Off Date

Where

Location of Occurrence

Responsible Entity

Loss Booking Cost Center

Why

Loss Description & Cause

Control Failure

Remediation Work

Discovery

Description

Market/Credit

Risk Flag

How Much

Gross Loss Amount

(Estimated)

Gross Loss Amount (Actual)

Recovery Amount

Net Loss

Which

Loss/Incident Type

Account Write Off

Information Source

Level 1 Risk Category

Level 2 Risk Category

Recovery Type

Who

Action Owner


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Market Risk Technology


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Market Risk Management

Commodity Risk

Foreign Exchange Risk

Interest Rate Risk

Counterparty Risk


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Market Risk Management Framework

Assumption

Volatilities

Correlations

Higher Moments

Pricing Engine

Market Data

Position Data

VaR Model Analytic

Simulation

Model Testing

Back Testing

Stress Testing

Reporting

Daily & Monthly VaR

Individual Desks Breach & Traffic Light System

Limit Utilization

IT System (Data Flow, Performance)

Control (Change, Security)

Process (Monitoring, Reporting, Risk Assessment)


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Counterparty Risk Framework

Internal Rating System

Trading Book

Collateral System

LGD System

(Type / Facility)

Default Event

Risk Engine

Counterparty Credit Limit System

Rating

Netting Agreement

Counterparty Hierarchy

Economic Sector

Geographical Area

Limits

Guarantees

CSA

Collatera Calls

Scenario Engine

Market Data

Static Data

Economic Capital

Exposures (Daily Batch)

Static Data

Static Data

Intra-Day Transaction

MarketTransaction

(DailyBat

ch)

Collateral

Transaction PV

PD

LGD

RiskFactors


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Supervisory Reporting Tool


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KRI Reporting & Dashboard


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Implementation Approach


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Basel II Themes

Program Management

Pillar I - Minimum Capital Requirement

Methodology &

Quantification

Data &

Infrastructure

Control &

Oversight

Ratings Process / Credit

Policies & Procedures

Ratings System Review / Internal Audit / Validation

Board & Senior Management Approval and Oversight

Pillar III - RWA

Calculator& Reporting

Regulatory

Reporting

Requirements

- Qualitative and

quantitative

disclosures

- Legal entity

aggregation

RWA Calculator

- Input requirements

- Calculation engine

Reconciliation

Organizational

Roles &

Responsibilities

- Calculation

components- Review and

signoff

Economic Capital

Stress Testing

Concentration

Analysis

Board and Senior

Management

Oversight

Pillar II -

SupervisoryReview

Unique ratings assignment

- Counterparty/obligor

exposure

identification

Quantification/Validation

- PD/LGD statistical analysis

- Reporting

Ratings Module- Data capture

- Workflow functionality

Reference Data Maintenance

- Risk rating criteria

- Default incidents & recoveries

Collateral &

Documentation

- Risk mitigant

recognition

Transaction

System Extracts

PFE Calculator

- Analytics

- Reporting

Counterparty

Referential Data

Collateral &

Documentation- Enforceability

recognition

Reconciliation

Data Management Standards

Market / Time

Series Data

Retail Capital Methodology

- Definition of default

- Segmentation Framework

Regulatory

Dialogue

Quantitative Impact

Studies (QIS)

Ratings Framework Methodology

Obligor

Ratings & PD

Quantification

Facility

Ratings &

LGD

Quantification

Wholesale(A-IRB Ratings System)

Retail(Pool Segmentation)

Securitization Repo-styleTransactions

Equities

PFE Model

- Simulation

- Backtesting

- Valuation

models

Approach- Grandfathering

- Materiality

Use Test

VaR Calculator

- Analytics

- Reporting

VaR Model

- Simulation

- Stress tests

- Valuation

models

Valuation / Time

Series Data

Exposure

Classification

A-IRB inputs for

K-IRB

- PD & LGD

- EAD

- EL

External Ratingor Inferred Rating

Risk Transference

MeasuresL - Enhancement

T - Thickness

N - Effective

Number

Ratings BasedApproach (RBA)

- External Rating

- Inferred Rating

IAA for ABCP

Exposures

Supervisory

Formula

Qualified Legal

Opinion to confirm- Risk Transference

- Enforceability of

Assets in SyntheticSecuritizations

Independent Model Validation

Reference Data Maintenance

- Segmentation risk factors- Default incidents & recoveries

PD , LGD, & EAD

Quantification

for each Pool

Quantification/Validation

- PD/LGD statistical analysis

- Reporting

Segmentation Process

- Data capture

Retail Capital Process /

Credit Policies & Procedures

Exposure at Default


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Basel II Streams


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Overall Project Approach

Detailed Data Gap Analysis

Data Quality Assessment

Data Gap Closure

Flow data into relevant systems for credit management reporting

and capital calculations

Capture or migration of loss, default data

Credit System Interface Enhancement

Sourcing of risk factors data

Feed to servicing and downstream systems including credit riskdata mart

Retirement of End User Computing (EUC) platforms

Data Management Framework Development

Policies

Processes

Tools

Risk Scorecard Platform Implementation

RWA Calculator Implementation Buy vs. Build Decision

System Selection or In-house Development

End-to-End Testing

Reporting and Analytic

Platform Implementation

Credit Risk Management

Concentration Analysis

Limit Reporting

Economic Capital

Framework

System Selection or In-

house Development

Credit Risk Data Mart

Operation

Regulatory Reporting

Data Mart

Implementation

Pillar 1 Pillar 2 Pillar 3


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Data Governance

G


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Data Governance Value Proposition

Common Current State

Lack of trusted data sources

Duplication of data across systems, no

central repository

Critical data populated incorrectly or

does not exist

Inconsistent data structures,

definitions, and usage Limited standardization of business

rules

Loss of data across systems

Difficulty reconciling data between

business functions and systems

IT and business resources manually

cleaning data Lack of processes for keeping data

clean

Data quality issues

Desired Future State

Trusted single source of truth

Critical data synchronized and reconciled

across the organization

Integrated view of master reference data

Planned redundancy of data

IT and business analyzing data - not

cleansing it Quantitative metrics to measure data

quality

Increased accuracy, consistency, and

confidence in underlying data

Data treated and managed as a valued

enterprise asset

Data is transformed into information andultimately becomes insight

D t G F k


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Data Governance Framework

Data governance is about developing data standards & policies that will be monitored

and enforced by the organization through agreed upon processes & procedures. The goal of data governance is to institute appropriate guidance catered for the

enterprise leading to quality information and usage.

Organization Model: Structure for making data-related decisions which

defines roles/responsibilities, stewardship/ownership, and handles

communication of data governance

Standards & Policies: Defines data definition/taxonomies, enterprise data

models, and data usage patterns Processes & Procedures: Defines monitoring methods, handles change

management, defines rules of engagement between initiatives and

governance body, and provides guidance on data usage

Enablement: Describes enabling data governance through technology

Organizational

Model

Standards &

Policies

Processes &

Procedures

Enablement

Provide

QualityDat

a

D t G A h


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Data Governance Approach

Review existing

governance

structure, data

standards, policies,

processes,

procedures, data

administration (e.g.

metadata)

Review data quality

pain points

Identify

stakeholders and

form working group

Achieve buy-in on

governance

framework

Current state

assessment of each

component of the

data governance

framework

Data quality

challenges and

improvement needs

Current GovernanceProgram

(Establish Baseline)

Define new governance

structure including

roles, responsibilities,

activities and time

commitments

Define data standards &

policies

Define data processes

& procedures

Define data quality

scorecard

Conduct group sessions

to review desired

processes

Design administration

and support capabilities

New data governance

structure

Data standards/policies

and processes/

procedures

Data administration

and support strategy

Data quality scorecard

Defining Future State

Governance Program

Review new

governance program

with key

stakeholder/working

groups

Develop transition plan

Consider training needs

on new processes and

procedures for the

governance team

Develop communication

plan to include

messages around the

new governance

structure and impact to

supported initiatives

(e.g. DW)

Plan communication

method & target

audience

Transition Plan

Communication Plan

Planning &Managing

Expectation

Identify data

quality initiatives

aligned to new

data

governance

program

Prioritize

initiatives

according to

returned

benefits and

data quality

scorecard

Initiate data

quality initiatives

Data quality

execution plan

Enabling Quality

Information & Usage

Assess

Current State

Define

Future State

Plan &

Communicate

Execute & Manage

Quality Data

Activities

Profiling & ControlsCleansing &

Enrichment

Ongoing

Sustainability

Work Products

Data quality report and prioritized

list of data cleansing initiatives

Data Dictionary listing critical data

fields to be cleansed with

corresponding standard

definitions, domain values,

usage, sources of truth, and

transformation rules

Repeatable data

cleansing/exception handling

controls & processes

Profile underlying data sources to

identify data quality issues,

determine root causes, and

prioritize cleansing efforts

Validate data cleansing scope,

approach, and activities

Create Data Dictionary to enforce

consistent/standard terms,

definitions, domain values,

usage, sources of truth, and

transformation rules

Identify critical data fields

Document repeatable data

cleansing/exception handling

controls & processes

Cleansing document listing

cleansing activities and

results

Controls and processes in

place to manage data

quality on an ongoing basis

Cleansed dataset

Obtain list of all known

changes to underlying data

that occurred during interim

period since last data

cleansing

Reconcile/Consolidate all

changes to underlying data

and create dataset to be

cleansed

Apply standards, controls,

and processes identified

during Profiling to create

cleansed dataset

Review/update data

cleansing results and

address identified

exceptions

Profiling and

cleansing work

products for a

specified dataset

that can be

leveraged for all

enterprise

datasets

Business case for

data governance

Sustain ongoing

data quality by

implementing data

governance

Leverage data

standards,

controls, and

processes defined

for profiled &

cleansed dataset

Increase scope as

necessary to

cover all aspects

of business data


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Team Experience

B l II E t


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Project Management Office

Basel II Engagement

PAS

Report Definition

FSRM

Deliverable:


Data Requirement

Specification

Deliverable:

Project Plan

Project Status Report

...


TSRS, BAS

Deliverable:

Process-Data

Dictionary

Data Gap Analysis

Report

Data Quality Analysis

TSRS

Deliverable:

Domain Value Report (Level 1)

Referential Integrity Report (Level 2)

Logic Integrity Report (Level 3)

...

Data Modelling

BAS

Deliverable:

Operational Data

Store Specification

Dimensional Data

Store Specification

Data Governance

TSRS

Deliverable:

Metadata Management Process

Data Management Group Initiation

Data Transformation

FSRM, BAS, TSRS

Deliverable:

Data Mapping

Specification

High-Level ETL

Design Specification

Infrastructure Design

TSRS, BAS

Deliverable:

Infrastructure

Specification

(Architecture, Sizing)

Report Generation

TSRS, BAS

Deliverable:

KRI Dashboard

Risk Convergence

Reports

T CV


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Business Consulting

Implemented regional imaging, workflow, credit analysis, enterprise application integration, and

decision support systems for financial services

Optimized the global performance management and operational risk process for a majorinvestment bank across Asia Pacific

Designed and implemented corporate wide business intelligence solutions from data modeling,data quality, ETL development, metadata, and infrastructure architecting, rendering enterprise

performance management (EPM), credit risk management (CRM), financial regulatory reporting,

and customer relationship management (CRM) analytics

Architected and implemented a 3-tier core banking transformation project for a global bank;

platform hosted centrally in Singapore, supporting business in Asia, Europe, and South America;

lead the subsequent change management effort

Provided strategic IT consulting on e-business and mobile computing initiatives, for financial

services, supply chains, as well as the public sector

Financial Services IT Operation Defined and executed regional centralization strategy to enhance productivity, achieve platform

synergy, and leverage low-cost country outsourcing strategy

Set up regional Project Management Office and define IT governance for technology investment

Advise on regional IT due diligence, post-merger and acquisition integration, and forge joint

venture and minority equity partnership in Greater China and across Asia through setting up

shared service centers

Paul SinAssociate Director, IT Advisory Services, Business Advisory Services

Direct Tel: +852 2846 8737Direct Fax: +852 3753 8667

Mobile: +852 9625 2427

E-mail: [email protected]

Paul is the Associate Director of IT Advisory

Service, Business Advisory Services, Far East

Asia, Ernst & Young

Paul has over 12 years of informationtechnology experience, served two major

business consulting firms (PwC/IBM), as well as

the financial arm of a global conglomerate as a

regional IT leader (GE Capital).

Ph.D. Candidate in Organizational Psychology

(2009), Walden University, Minnesota, US

MBA, Chinese University of Hong Kong

B.Sc. (Hons) Computer Science Information

Systems, University of Hong Kong

Project Management Professional Certification

Six Sigma Blackbelt Certification

Language: English, Chinese (Cantonese,

Mandarin), Some Japanese, Some Thai

Background Experience

Qualification & Skills

Team CVs


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IT Ad i S i Off i


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IT Advisory Services OfferingIT Strategic Planning & Requirement Analysis (ITSP)

The methodology for IT Strategic Planning and Requirements Analysiscovers an extensive scope of

internal and external analyses, developing the optimal business case and implementationframework (Identify, Diagnose and Design). It does not cover actual implementation of the

proposed operating model (Deliver, Sustain), but can include Strategy Testing (Validate).

All IT Strategic Planning and Requirements Analysis projects adhere to the overall monitoring level

of Quality and Risk Management (Q&RM), as well as Project Management.

Change Managementand Benefit Managementapproaches will vary depending on the nature of the

particular IT Strategic Planning projects.

Relevant Phases


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IT Advisory Services Offering


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IT Advisory Services OfferingInformation Management Framework

Data Model

Conceptual Logical Physical

Data Management

Metadata Management Reference Data ManagementData Protection

Operational Data Stores Data Warehouse / Data MartSystem of Records

Master Data Management

Data Movement (ETL/EAI/EII)

Data Governance

Standards & Policies Processes & ProceduresOrganizational Model Enablement

Data Quality

Profiling / Analysis Cleansing Controls

Data Usage

Management Reporting Analytics (OLAP) Data Mining

Scorecard / DashboardsQuantitative Analysis Alerts / Notifications

Architecture

Physical / TechnicalLogicalConceptual

StandardsServicesDesign Patterns

Enrichment / Enhancement



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IT Advisory Services OfferingProcess Analytics (PA)

Deep business insights help organizations improve their ability to make timely and

accurate business decisions and meet business objectives.

Determine

what could

happen

Prediction

Statistical, econometric, scenario-

based

Analysis and Monitoring

Root cause, modeling and validation,

multidimensional analysis

Measurement and Reporting

Forensic evidence, queries, profiling,

data/text mining, benchmarking

Determinewhy it

happened

Determine

what

happened

Business BenefitsAnalytic Capabilities

Process Analytics

Information Management

Data

InformationManagement

QualityData/Information

Analysis

BusinessInsights

CompetitiveAdvantage /Shareholder

Value

` `

Reliability

of data

Information Management

Data governance, data quality,

data conversion, data warehousing

To make smart and accurate decisions, organizations need to transform data into information and actionable insights.

Objective

Data that meets the

needs of the business

Sustainable data quality

Make better business

decisions

Improve key processes

Create competitive

advantage

Enhance revenue

Improve shareholdervalue

Improve risk

management

Reduce risk of fraud

Just-in-time control

monitoring

Reduce working

capital



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Organization

IT Advisory Services OfferingInformation Management Advisory Service (IMAS)

Policies & Procedures Standards

GovernanceFramework

Business requirements

Definition, format. and rules

Data design

Classification

Data collection

Meta data management

Test data management

Data change management

Master data management

System performance measurement

Data security Extract, transform & load

Data quality assessment

Storage

Business continuity planning

Backup and retrieval

Data protection

Monitoring

Consumption (analysis, reporting

and manipulation)

Master data management

Data resilience

Tracking and notification

Media management

Disposal

Governance

Data Quality

Data

Conversion

DataWarehouse

Metadata

Management

Data

Reconciliation

Accessibility

Auditability

Availability

Compliance

Consistency

Integrity

Define & Design

Dispose

Increased

Market Share

DependableCompetitiveIntelligence

Liquidity

& Solvency

HeightenedRegulatory &

ReportingCompliance

Improved

Stakeholder

Confidence

Data Management ActivitiesData Life CycleInformation

Management

Data ObjectivesBusiness

Objectives

basel ii it advisory (en).pptx

Documents