• Black Ice Partners is a global risk management consulting and technology firm with over 20 years experience in the financial services industry, and with clients ranging from large global financial institutions, to small domestic banks.

Experience

• We have a comprehensive understanding of best risk management practices, and continually update our services to cover constantly evolving regulations and demands.

Knowledge

• We are a practical and experienced team of industry veterans who have been part of at least ten Basel implementations around the world, and our partners are industry recognized experts.

Implementation

• Black Ice Risk Data Aggregation Solution (RDAS)Solution

Client Work Description

Malaysian Bank A ICAAP Gap Analysis

Malaysian Bank B Enterprise Risk Management Risk Data Mart

Canadian Banks (2) Road Map for Basel AIRB Compliance and Gap Analysis Report

South Korean Bank Implementation of Basel II AIRB Compliance

Singaporean / Taiwanese

BankImplementation of Basel II AIRB Compliance

Canadian Bank C Road Map for Basel AIRB Compliance and Gap Analysis Report

Canadian Bank D ERM Risk Data Mart

Singaporean Bank Road Map for Basel AIRB Compliance and Gap Analysis Report and ICAAP

Nth American Bank Road Map for Basel AIRB Compliance and Gap Analysis Repo

Data Warehouse Provider Enterprise Risk Management Risk Data Mart

Global BankIndependent Audit of ICAAP Implementation on behalf of Board and Senior

Management, Basel III and Dodd Frank Gap Analysis and readiness

Malaysian & Indonesian& Thai

Regulators

Training to the directors and management of various banks on Basel III and ICAAP, Risk

Governance, Ent Risk Mgmt, Techniques in Risk Management

Malaysian Investment Bank Training for Bank risk team on ICAAP, Risk Appetite, RAROC, Basel III

Client Work Description

Taiwanese Bank ICAAP Gap Analysis

Australian Bank Enterprise Risk Management Risk Data Mart

Thailand Bank Road Map for Basel AIRB Compliance and Gap Analysis Report

Canadian Bank Implementation of Basel II AIRB Compliance

Hong Kong Bank Implementation of Basel II AIRB Compliance

Black Ice RDAS

Wholesale Credit

Retail Credit

MarketRisk

Operational Risk

A Physical/Logical Data Model framework developed on IBM PureData that enables the organization of data efficiently and effectively in a way that makes sense.

The Black Ice Risk Data Aggregation Solution (RDAS) addresses all levels of Basel and Dodd Frank compliance with all relevant analytic engines and comprehensive reporting.

The Black Ice RDAS compromises of four Logical Data Models that organizes data and feeds analytic engines:

BRC Wholesale Credit Data Model

BRC Retail Credit Data Model

BRC Market Data Model

BRC Operational Risk Model

Allows a financial institution to meet the following regulatory requirements:

Risk Data Aggregation & Reporting (2016)

Global Legal Entity Identifier

Basel II/III

Capital and Risk Weighted Asset calculations

Basel Committee on Banking Supervision (BCBS) – Basel II and III

Guidance on international standards on capital adequacy, and principles for effective banking supervision

BCBS – Risk Data Aggregation & Risk Reporting

A set of principles to strengthen banks’ risk data aggregation capabilities and risk reporting practices. National supervisors expect G-SIBs to implement these principles by 2016.

Financial Stability Board – Global Legal Entity Identifiers

The Global Legal Entity Identifier is designed to accurately identify financial transactions.

Country Specific Regulator Guidance

Implementation Notes on Data Maintenance, that prescribe Senior Management Oversight, Data Collection and Data Processing guidelines.

Governance & Infrastructure

Risk Data Aggregation Capabilities

Risk Reporting

?

How does an institution effectively

operationalize regulatory

requirements?

The majority of institutions will

require an investment in

technology solutions to meet

requirements

Data Aggregation

GovernanceData Arch and IT

Infrastructure

Accuracy and Integrity

Timeliness

Completeness

Adaptability

Accuracy

Frequency Clarity

Comprehensive

BITS

Ris

k D

ata

Ag

greg

ati

on

So

luti

on

Executive Sponsorship

Data Governance

Data Quality and Integrity

Data Architecture

Data Analytics & Business Intelligence

Level 1 Level 2 Level 3 Level 4

Infancy Developing Mature Leading

Localized Initiatives driven

by individual IT teams

Limited involvement of

senior business and

management in information

integration

Collaboration of business

and IT mangers with senior

management sponsorship

Top management actively

engaged in enhancing the

enterprise

Lack of data ownership; No

defined responsibilities for

caretaking of data

Assigned data caretaking

for selected data sets

Business driven data

governance; Augmented by

IT support and

infrastructure

Functional areas own data

assets and benefit from

senior business executive

support

Data is not trusted, not

consolidated & errors are

corrected manually

Data consolidation is

underway, basic data

quality requirements have

been defined

Data accuracy and

completeness is trusted

within silos; Quality tools

and & processes in place

Data accuracy and

completeness is trusted

enterprise-wide; Quality is

actively monitored &

improved

No enterprise reference

data model in use

Defined data model but not

widely used

Single and widely used

data model but lacking

formalized governance of

the model

Standardized data model

located in a central

repository, centrally managed

and governance model well

known across the enterprise

No organized BI plan or

strategy; Lack of alignment

to business objectivesMulti-year BI strategy and

budget

BI Strategy linked to

functional strategy; benefits

tracked & realized

BI strategy integrated with

the Enterprise information

needs and strategy

Industry AverageBITS Implementation

UndefinedData Ownership at the Enterprise

Level

Data Quality

Inconsistent or Inaccurate Reporting

Inadequate Structure or

Framework for Data

Complex and Comprehensive

Regulatory Requirements

End-to-end data element

identification

Single View of Client and

Relationship to Exposures

Data Aggregation

Do you understand the impact of IT projects across the entire organization, or only with systems with direct relationships (i.e., one-step removed)?

Do you know who owns your data, is there a central group that will drive changes, or does each business unit determine their own priorities?

Do you know how accurate your data is, are you confident that all reports reflect the same information?

Do you know your data strategy, is there an enterprise or a business-level strategy?

How comprehensive is your data framework and data policies to support your approach and to ensure regulatory requirements and senior management expectations?

Has your institution identified Mandatory Risk Data from origination to reporting/calculation?

Has your institution identified controls to ensure accuracy for Mandatory Risk Data?

What validation/monitoring do you perform on data quality?

Actual Observations at financial institutions

• ALCo reports being generated using incorrect data. The data dictionary was incomplete, and the business thought the data was “real-time/current” and was the same value as the book of record.

• Retail risk reports being generated by two different groups for different purposes, but the values for the same period did not match. Neither group could determine which was the correct value.

G-SIBs need to act now to meet the deadline, but those that embrace this opportunity to deliver strategic change will gain competitive advantage.

- Deloitte EMEA Centre for Regulatory Strategy

Overall, we see further evidence in these changes of the shift from risk as a compliance function to risk as a support function for improved performance across the business. And, as we look ahead, the baseline is that G-SIBs have got to get moving and start investing in the systems that will keep them on track towards the 2016 deadline.

- IBM Integrated Risk Platform

Inadequate data aggregation, insufficient risk reporting and ineffective IT systems were seen as a significant contributor to the financial crisis

- Thompson Reuters

The financial crisis revealed that many banks, including global systemically important banks (G-SIBs), were unable to aggregate risk exposures and identify concentrations fully, quickly and accurately. This meant that banks' ability to take risk decisions in a timely fashion was seriously impaired with wide-ranging consequences for the banks themselves and for the stability of the financial system as a whole.

- The Asian Banker

Risk data and reports should provide management with the ability to monitor and track risks relative to the bank’s risk tolerance/appetite.

- BCBS

Common data governance and management issues are found across the industry with data aggregation as a critical foundation for resolution

- Deloitte & Touche LLP

IBM PureData System

Global Legal Identity

Identifier

BCBSRisk Data

Aggregation and Risk Reporting

BCBS Capital

Calculations

Board and Senior

Management Reporting

The solution provides critical advantages to the client in the areas of:

Platform agnostic, enterprise-wide risk infrastructure covering Market, Operational, Credit Risk (across retail & Wholesale asset classes)

Cost effective solution available as measured in Total Cost to Acquire and Cost to Maintain

Rapid time to deploy (typically between 3 to 8 months to implement and achieve full compliance)

Compliant with regulator requirements for end-to-end data lineage

Supports disparate data and reporting requirements across

- Management reporting;

- Board of Directors reporting;

- Regulatory reports; and

- Regulatory audit processes.

Provides a foundation for future risk requirements (e.g., by BCBS or by the regulator) through the enterprise risk data foundation schema, resulting in a reduced effort to assess and meet new requirements

Delivers the capability for a single identifier across the institution

Other solutions such as RDAS exist, but are expensive and often are in-house bespoke solutions built by financial institutions themselves that focus on Integrated Enterprise Wide Risk and Capital Data.

RDAS is what a Global Financial Institution usually builds for itself given the resources and knowledge they have in-house but at a significantly higher cost.

Improved Decision Making

Improved speed at which information is available

Improved ability to manage risks

Enhanced management of

information across the institution

Improved quality of strategic planning

Reduced probability of losses resulting

from weak risk management

Self Assessment

(Consulting Firm and/or Financial Institution)

Define Strategy(Consulting Firm and/or

Financial Institution)

Implement Common

Data Model(Black Ice Technologies)

Data Models by Asset Class (4):

Provides the capability for an institution to be BCBS data and GLEI compliant

Includes comprehensive library of regulatory and Board & Management reports out of the BOX

Analytics (yes/no):

Provides the capability to leverage stored procedures inside the RDAS, or leverage existing analytic engines currently in use at the institution

ONE

TWO

Data

Model

s

Analyti

c

Engine

s

Report

s

RWA

Economic Capital

Stress Testing

RAROC

Liquidity Risk

RWA

Economic Capital

Stress Testing

RWA

Economic Capital

Stress Testing

RWA

Economic Capital

Stress Testing

Risk Rating Models

Risk Rating Models

eVaR

Management + Regulatory

Reports

Management + Regulatory

Reports

Management + Regulatory

Reports

Management + Regulatory

Reports

Wholesale Retail Market Operational

IB

M

Pu

reD

ata

Sto

red

Pro

ced

ures

Im

ple

men

tati

on

Op

tio

ns:

Bla

ck I

ce /

3rd

Party

/ N

on

e

In

clu

des C

ore

Rep

ort

Tem

pla

tes

RAROC

Corporate and Commercial Banking Systems

• Risk Rating Systems

• Credit Approval Systems

• Credit Servicing Systems

• Collections and Workout Systems

• Trading Systems

• Trading Exposure Systems

Retail Banking Systems• Small Business

Credit

• Credit Card Products

• Mortgages

• Retail Portfolio Management

• Analytics and Decision Support

Trading Room Credit Risks• Facility

Apportionment

• Ratings Systems

• Exposure Measurement

• Collateral Management and Valuation

• Securities Finance

Special Products• Securitization • Non-Traded

Equities

Finance Systems• Detailed GL

Postings• Financial

Hierarchies

Source Systems

Concentration Risk

Analysis

Risk Adjusted Pricing

& RAPM

Regulatory Capital

Calculation

RAROC & Economic

Capital

Stress Testing and

Back TestingIn

Data

base A

naly

tic E

ng

ines

OR

Exte

rn

al A

pp

licati

on

Data

Mart

Source

Systems feed

into

Physical/Logic

al Data Model

Regulatory Board Management

Reporting

Financial

Data

• Physical

/Logical Data

Model

• Basel Asset

Classes

• Global Legal

Identity

Identifier

SQL / DataStage

Metadata

Repository

InternalAudit

BLACK ICERDAS

Credit Risk Retail/Wholesale

Operational Risk (AMA)

Market Risk (B2.5)

Basel II Basel II.5 Basel III

GL Data

FinancialReconciliation

Solution By

The BlackIce RDAS is already mapped to the following downstream Risk Applications:

SAS

Moody’s Analayitcs

ALGO Risk Watch

Sungard Adaptiv,

Sungard Panaroma,

Sungard Front Arena

Sungard B2CM

Sungard BancWare

Moodys KMV

Several G/L

The BlackIce RDAS is already mapped to these upstream aggregated data warehouse models:

FSLDM

BDW

Razor

Murex

Calypso

Xtrader

Misys

Sophus

Client Country Status Contract Period

Siam Commercial Bank Thailand Final contract negotiations ~$400k + Q2

Bank of China China Workshop / Proof of Concept ~$1.0M – $2.0M + Q3

China Guangfa Bank China Workshop / Proof of Concept ~$1.0M – $1.5M + Q3

Bank of Bejing China Engagement Started ~$1.0M + Q4

Chengdu Bank China Engagement Started TBC Q4

SBV Vietnam RFP Process with IBM ~$2.0M + Q2/Q4

TMX Group Canada Engagement Started ~$1.0M (plus reseller license) + Q3

Sales Focus

Initial sales effort started in Thailand, Philippines, Indonesia and Vietnam due to the infancy of the financial system

Countries are mandated to implement BCBS guidelines as directed by the timelines provided by their home regulator (see market size in appendix)

Existing Partnerships

IBM

Deloitte, PwC, Pactera, Camelot, Digital China

Financials - Asia Financials - USA

Option 1 Option 2

Risk Data Aggregation

Report Templates

Analytics No Analytics

Purchase: $2.5M Purchase: $2.0M

Lease: $110k/month – 3 year contract Lease: $100k/month – 3 year contract

Purchase Option: Support is optional and fixed at 10% - No obligation

Lease Option: Support is included in lease payment

Hardware costs are extra and dependent on size requirements

Investment Proposal

$300K -$500K Required

Set up a syndicate structure – Limited Partnership

Funds Invested as Shareholders loan

Loan paid before majority Shareholders loan

Interest paid on the Investment beginning 12Months from date of Investment

Syndicate receives 15% - 25% of Equity depending on amount Invested

Board Seats

Use of Funds

Hire staff for upcoming projects

Bridge financing for operations

Finish documentation for RDAS solution

Marketing efforts

Finish development and packaging of the GCD Solution