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Basel Committee 1988 Accord

• Focus mainly on credit risk
• Later amended to include market risk
• Set minimum capital ratios

New Basel Capital Accord

• A more risk-sensitive approach to regulatory capital measurement
• Focus on overall risk management
• Broadened scope to include operational risk
• New asset class included in the calculation - advised line
• New categories - Specialized lending, securitizations, equity investments and investments in related entities, retail and SME
• New Analysis - Repo-style transactions

Objectives of New Capital Accord

• Produce a flexible structure and provide incentives for banks to improve risk management
• Refine the regulatory capital charge to include a more granular measure of risk
• Framework compatible with economic capital
• Eliminate arbitrage opportunities (e.g. 364-day revolvers)

Benefits to the Industry

• A more risk sensitive approach to regulatory capital estimation that minimizes existing regulatory capital arbitrage
• Encourages better risk management practices through the three mutually reinforcing pillars
• Provides incentives to invest in credit risk management infrastructure

US Regulatory Status

• U.S. regulatory agencies announced a delay of at least one year in implementation of the Basel II framework.
• The Notice for Proposal Rulemaking expected first quarter 2006
• Capital floors in place for at least three years instead of two

Basel II Basic Structure: Three “Pillars”

•  Pillar I: Capital Adequacy. Prescribes Regulatory Capital based on Economic Modeling
• Pillar II: Regulatory Oversight. Evaluates each bank and adjusts general requirements as necessary
• Pillar III: Market Discipline. Prescribes “uniform” public disclosure of each bank’s risk profile

Data Warehouse, data quality is the single most important thing as the poor quality can impact organization decision capabilities. The Data quality has to be defined during the initial stages of the application design when ETL requirements for operational systems are developed and has to be managed throughout the application development, use and maintenance stages of the project.

Data profiling typically takes place at the beginning of the design and development process of integrating systems.Source data collection can be analyzed and the metadata including data rules available about these data can be corrected and completed. As a result of data profiling step,data quality rules are defined which can then be monitored during the ETL processes. Data quality issues found during ETL can be corrected in two ways; providing applications to correct data offline and generating data quality reports to quality issue management to take the necessary actions.

Adoption of the implementation across the enterprise ? single version of truth and cost saving on multiple implementations among different user groups.
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Data Quality and ease of use ? warehouse will be used to make strategic decisions
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The return of investment ?  increase in revenue attributing to warehouse implementation
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The value of knowledge gained by users? conduct a user survey to rate the implementation.

One stop shop to check the growth, returns, and risk level ambitions of the various parts of our Organization.

Ambitions will also be compared against Actual results.

Ambitions and Actuals will also be reviewed by Corporate and Peers on a short-term scale and a longer-term horizon.

Tool used by Corporate to ensure that no region, business line, product segment, or channel has acquired more risk than it should.

Tool used to spread best practices beyond just process practices and delves into product or service practices.

Corporate Image and Brand Equity are the top concerns of the Corporate and Business leaders, and this tool will ensure that the chase for growth & returns will not dilute these important enterprise assets.

If recruiting, training, and retaining talent is the single most important source of competitive advantage in your business model, then TaM Bank is what you need.

What kinds of talent does our business need?

Where is our business falling short?

Can we define the three most important qualities required from each kind of talent?

Do we have a methodology to rate those qualities in individuals?

Are we tracking the careers of all your employees?

Are we in sync with the aspirations of our employees?

Can we find common ground between the aspirations of our employees and the aspirations of the organization?

Are we making an attempt to understand the potential hidden within each employee?

Do we make an attempt to match potential with opportunities within the organization?

Do we believe in promoting and building from within and to what extent?

How do we ensure that ‘promoting from within’ does not create an insular environment within the organization?

Which came first chicken or egg - do we need to define whether customer comes first or employees?

Are we trying to create a competitive environment within the organization or a cooperative one?

The Pain:

Businesses use a variety of Data collection packages
Large cost to centralize information for Financial Control organizations
 –Large amount effort is manual or on spreadsheets
 –Takes too long to complete Data collection
 –No documentation of Rules applied to the data
 In-house built collectioncalendars, manual data security and operational intervention.
 Not all sources haveinformation at the same level of detail
 complex memocalculation in spread sheets
 Information is movedmultiple times to arrive at needed control

The Solution:

 Combine Industry bestpractice knowledge and expertise in the latest technology to create Warehouse           solution
 Comprehensive Datacollection mechanism
 Rules and calculationengine while data collection

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