Thoughtline February 2009 - Banking & Financial Services e -newsletter from Wipro Technologies

Volume VI Edition XV

FEBRUARY 2009

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ethe Banking & Financial Services -newsletter from Wipro Technologies

the Banking & Financial Services -newsletter from Wipro Technologies e

Feedback & Suggestions aremost welcome. Please email to

[email protected]

Editorial Team

Jayaprakash KavalaJiju George

Tathagata Biswas

Volume VI Edition XV

Index

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2the Banking & Financial Services -newsletter from Wipro Technologiese

For Wipro internal circulation only

..................................................................................................� Foreword

?Know your Domain Terms...........................................................................

?Leveraging Technology for Financial Modeling.............................................

?Historical perspective of government intervention in banks across the world..

?Private Equity Firms Intervention & Letting in Vulture Investors...............

?Effective approach to risk management- Lessons to be

learnt from the financial crisis..................................................................

?Emerging Operational Risks in Retail Payments.........................................

?Fun Corner

?Indian Miniature Paintings........................................................................

3

4

5

8

11

13

15

17

19

20

?Demystification

- FINANCIAL MODELING – The Harbinger of Predictive Performance............

- Spot the duo!..........................................................................................

Foreword

“May you live in interesting times”. So goes an ancient Chinese curse. Well, we are certainly going through an interesting juncture of the global economy. Economists are comparing the current global economic scenario with the Great Depression of 1929 to the mid 1930's and not without reason.

What began as a “credit crunch” in July 2007 with a loss of confidence by investors in the value of securitized mortgages was followed by the collapse of the US housing market in the so called “sub-prime crisis” in early to mid 2008. Financial institutions engaged in the securitization of mortgages tumbled next-Bear Sterns being the biggest victim. IndyMac Bank-the largest mortgage lender in the US was the next prey, however the Feds came to their rescue. Fannie Mae & Freddie Mac were also taken over by Feds in Sept 2008 after they were in a near bankrupt state. The loss of close to $ 6 trillion in housing wealth then claimed Wall Street in the form of Lehman Brothers declaring bankruptcy in Sept 2008 with AIG, Citigroup and Merrill Lynch being bailed out by the US Government in the form of huge financial aid. Circa Feb 2009-we are in the middle of a full blown global recession which has cascaded to nearly every industry sector & prompted governments to launch “help packages” for their economies.

In this issue of Thoughtline, we try to dissect how effective financial modeling can help organizations prepare for economic meltdowns in a better manner and analyze the various options open to organizations to come out stronger in an economic meltdown. We have also explored historical perspectives on government intervention in times of crisis and how technology can help in effective financial modeling for organizations.

Lastly we also explore the roles of private equity & vulture investors in hours of crisis and debate whether they really add value to an organization or work in their own vested interests.

We have enjoyed a very high level of support from our erudite colleagues and thank them profusely for their contributions. We take pride & pleasure in presenting this edition of Thoughtline to you and hope this edition would do justice to your valuable time.

Regards & Best wishes,

The Thoughtline editorial teamJayaprakash Kavala, Jiju George & Tathagata Biswas yldneirf oce


The Wipro editorial team compiles this newsletter with contributions from Wipro's staff members. The views and opinions expressed in the articles / other contributions by individuals are strictly those of the authors. The content of these articles has not been reviewed or approved by Wipro.

Parts of the Images used in this Thought Line isSculptural details of Hoysala Architecture

the Banking & Financial Services -newsletter from Wipro Technologiese

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4

Jiju George


Basis risk:

Capital Adequacy:

Cash budget:

Capital Asset Pricing Model (CAPM):

Financial Modeling:

Exposure of a transaction or portfolio to the differences in the price performance of the instruments in the portfolio. Also referred to as correlation risk, basis risk may also be used to specifically describe the risk that the rate or basis relationship between a transaction in one market and a hedge of that transaction in another market in the same currency will change.

The ability of a bank to absorb losses or shrinkage in the value of assets. Banking supervisory agencies have set standards to ensure that a bank's capital is sufficient to absorb a reasonable degree of losses and remain a going concern. These standards have been established primarily to protect depositors and the global banking system as a whole.

Financial plan that is a summary of estimated receipts (cash inflows) and payments (cash outflows) over a stated period. Two common methods of cash-budgeting are (1) Adjusted net income approach and (2) Cash receipts and disbursements approach.

A classic and widely used model of the relationship between expected risk and expected return for a marketable asset.

The process by which a firm constructs a financial representation of some, or all, aspects of the firm or given security. The model is usually characterized by performing calculations, and makes recommendations based on that information. The model may also summarize particular events for the end user and provide direction regarding possible actions or alternatives.

Hedge Fund:

Interest Rate Risk:

Netting:

Operational Risk:

Recapitalization:

Hedge funds are designed for wealthy individuals and institutional investors, and are not regulated as other forms of investment funds are (e.g., mutual funds). Hedge funds are allowed to employ riskier investment strategies that other investment funds are not allowed to use, including using derivatives (such as stock options), selling short, borrowing money to leverage returns and trading in currency. As a result of these strategies, hedge funds often have higher returns than mutual funds and other investment funds, and can earn positive returns in down markets. On the other hand, hedge funds can also face enormous losses.

Risk that interest rates will rise leading to an increase in the interest liabilities of borrowers or the risk that interest rates will fall leading to a decline in the interest income of floating rate investors/lenders. In a bank, interest rate risk arises from interest rate mismatches (fixed vs. floating) in the volume and maturity of interest-sensitive assets, liabilities and off-balance sheet items.

An arrangement to settle all contracted but not yet due liabilities to and claims between banks by one single payment, immediately upon the occurrence of one of a list of defined events (such as the appointment of a liquidator for a particular bank etc.).

Risk of direct or indirect loss from the failure of internal processes, personnel, systems, or external events. The loss could be catastrophic or occur incrementally over time. It includes fraud, theft and computer system failure.

The process of changing the capital structure of a company by either increasing or

Know Your domain terms

decreasing the amount of corporate debt and/or equity.

A ratio that measures how well a company is generating profit from its assets. It is calculated by dividing a company's annual earnings (net income) by its total assets.

An indicator that measures a company's profitability relative to the equity invested in the company. It is calculated by dividing net income by average shareholders' equity.

Refers to the use of formal econometric techniques to determine the aggregate risk in a financial portfolio. Risk modeling is one of many subtasks within the broader area of financial modeling. Risk modeling uses a variety of techniques including market risk, Value-at-Risk (VaR), Historical Simulation (HS), or Extreme Value Theory (EVT) in order to analyze a portfolio and make forecasts of the likely losses that would be incurred for a variety of risks. Such risks are typically grouped into credit risk, liquidity risk, interest rate risk, and operational risk categories.

Total value of the potential risk of loss while holding a specific market position. Under the amended Basle Accord, banks can now use VaR models to compute the market risk in their portfolios that must be covered by the various tiers of capital for capital adequacy purposes.

Return on Assets (ROA):

Return on Equity (ROE):

Risk modeling:

Value at Risk (VaR):


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FINANCIAL MODELING – The Harbinger of Predictive PerformanceVinodh Ravishankar

proem to financial modeling: A In this day of pall and gloom in the financial services industry, state sponsored bailout packages have come as a shot in the

arm for firms in turmoil. These packages may come as a welcome relief to these troubled financial institutions and help them get back on their feet. They might serve the purpose of straightening the books in the near term. Will these federal initiatives help build a strong sustainable competitive advantage? As we speculate on the answers, it is important for these financial institutions to retrospect on the financial models they have so strongly relied upon. While a call for a total revamp might be far away, plugging the holes in the financial modeling systems should be a top-priority item in their to-do list.

In an effort to walk before we run, it is important to understand financial

modeling at the grass-root level. Financial modeling is the process of building a tool designed to forecast the performance of a financial investment, like a credit portfolio of lending products or an investment portfolio of equities and debt instruments. It uses correlations between operational, financial and macro-economic variables to predict performance. The fundamental drive for a financial institution to use a model is to understand the effects of uncertainty on its business model and projected revenue stream.

A financial institution's success relates to its capability of identifying win-win products for both the consumer and the company. A financial product model identifies this set of win-win products by integrating the consumer's preferences for price and returns with the company's preferences for profit and market share. The selection of the optimal product (loans, mortgages, credit cards) from this set would be in alignment with the institutions strategic and tactical imperatives.

Financial institutions are subject to market, credit and operational risks. Risk modeling uses formal econometric and statistical techniques to evaluate the aggregate risk in a credit portfolio or an investment portfolio. Value-at-Risk (VaR), Historical Simulation (HS) and Extreme Value Theory (EVT) are risk modeling techniques used to quantify and simulate a variety of risks. Risk models are important levers in allocating capital reserves for absorbing portfolio related losses.

Credit risk analysis (finance risk analysis, loan default risk analysis) and credit risk management are important to financial institutions which provide credit products like bank mortgages (or home loans), auto loans and credit cards. Credit loans and finances carry a risk of default. To understand risk levels of credit users, it is important for credit providers to collect vast amount of information on borrowers. Predictive analytical techniques can be used to analyze or to determine default risk levels.

Personal credit scores computed from information available in credit reports may indicate personal financial history and current situation. The conundrum here is calculating the level of risk for the lending and the solution lies in effective segmentation, profiling and building credit score distributions.

Credit risk profiling identifies the 10%-20% of the lending segments which contribute to 80%-90% of the credit defaults. Profiling also identifies factors or variables that best summarize these segments. A good profiling analysis drills-down data systematically and detects important relationships, co-factors,

Relevance of credit risk modeling to cards and mortgages

Market Risk Credit Risk

Operational Risk

ØProbability of default

ØLoss given default

ØThird party credit rating

ØCollateral and guarantees

ØEconomic capital

ØFinancial liquidity

ØProcesses

ØSystems

ØPeople

ØLoss probability

ØLoss on exposure

ØInterest rates

ØForeign exchange rates

Demystification



interactions, dependencies and associations amongst many variables and values accurately and generates profiles of the segments under the scanner.

From the past credit information, predictive models can learn patterns of different credit default ratios, and can be used to predict risk levels of future credit loans. Decision trees and regression models are able tools to predict default risk levels for different customer segments.

Credit scoring measures the level of risk factored into lending. Risk models predict default loans with lower scores and loans fully-recovered with higher scores. The default probability associated with a customer segment is deduced from a drawn-up credit score-default probability distribution.

Active mortgage brokers, optimistic investment bankers, oblivious rating agencies and gullible investors have all been faulted in the financial crisis. It's all well and good to call for substantial new oversight; but if market players were oblivious to the danger, the question is why.

The models failed to keep pace with the explosive growth in complex financial products, the resulting intricate web of risk and the dimensions of the danger. But the larger failure was human, in how the risk models were applied, understood and managed. While markets were booming, the focus was on heaping more profits by doling out more debt, trading more and more asset-based securities, and making larger and larger bets. Complexity, transparency, liquidity and leverage have given this crisis a negative reinforcement and these are things that are not generally modeled as quantifiable risks. Risk modeling systems fell short in calibrating the lending risk on individual loans.

In recent years, the securitization of the mortgage market has prompted lenders to move increasingly to automated underwriting systems, relying mainly on computerized credit-scoring models instead of human judgment. So lenders had scant incentive to spend time infusing the human factor and scrutinizing the creditworthiness of individual borrowers. When the incentives and the systems changed, the hard data meant either less than it did or something else than it did. The modeling became too mechanical. The quantitative methods underestimated defaults for sub-prime borrowers in a systematic failure of models. The crisis bears testimony to how models can zoom into decimal places and yet be totally off target.

Regulators relied on complex risk models that told financial institutions how much risk they were taking at any given time. Risk management has been dominated by models like VaR which predict with 99 percent probability that institutions cannot lose more than a certain amount of money. Institutions and

Where did the models go wrong?

regulators compared this "worst case" with their actual capital loss reserves. If the amount of capital loss reserves was greater, they continued in their state of peace. Lurking behind the models, however, was a colossal conceptual error: the belief that risk is randomly distributed and that each event has no bearing on the next event in a sequence. This belief was unfortunately more than a folly.

Just as Capital One's use of credit scoring and risk-based pricing revolutionized credit card lending, improved risk modeling will help mortgage lenders identify the most attractive segment in terms of risk profiles. It will be imperative to move beyond the traditional pricing grids—which are largely based on combined loan-to-value ratios, documentation levels, FICO credit scores and mortgage performance—and to take a comprehensive view of the collateral, credit, prepayment, and channel-related fraud risks.

While financial models can be used to estimate the distribution of possible future financial outcomes, such as changes in interest rates or a firm's credit quality, they cannot incorporate all possible risk outcomes. Historical experience has shown that they cannot capture sudden and dramatic changes in market circumstances. A more enlightened view of the credit risk models would suggest that extreme events occur more frequently than the models predict. It would be wise to embellish the model with "fat tails" and model these tails on historical extremes such as the post-Sept. 11 market reaction.

To address the shortcoming of existing risk models, effective "stress testing" has to become an important element of the supervisory monitoring of financial firms. It should consider credit risk in loan portfolios as well as the impact of extreme unanticipated changes. The distinct advantage that stress tests have over other credit risk analysis methodologies is their explicit linking of potential losses to a specific and concrete set of events viz., collapse of the mortgages market, depreciation of currency value etc.

With respect to credit risk, stress testing can be that of credit spreads and loan portfolios. For stress testing of loan portfolios, variables such as borrower credit ratings and collateral values are stressed, often using scenarios based on shocks to the macro-economy.

Efforts to integrate credit risk stress tests would require sufficient historical data for such analyses as well as the system infrastructure to generate integrated credit risk profiles. Re-engineering the presentment of information from these risk

What do we do with the learning's?

Does this mean we have a potential source of leverage and traction?

Demystification



models to better enable the human element in decision making might well be a latent revolution.

The impending change in the risk management landscape is a potential goldmine for data warehousing and risk analytics business opportunities. Domain expertise in retail and commercial lending together with a technology expertise in data warehousing and analytics could be the magic potion

Demystification

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Leveraging Technology for Financial ModelingJiju George & Abhishek Gupta

A

Faster time to answer:

Scenario analysis:

successful financial model is one that helps an organization to address the credit, market and operational risk. IT solutions play integral roles in helping

the management take up the strategic decisions in order to address the future risk. Financial models can be constructed in many ways, either by the use of computer software, or with a pen and paper. What's most important, however, is not the kind of user interface used, but the underlying logic that encompasses the model.

Spreadsheet based modeling is quite common in this financial industry. This has been there for ages. Enterprise business intelligence solutions are also used by various organizations in financial planning and forecasting. There are non-spreadsheet software platforms available on which to build financial models. However, the vast proportion of the market is spreadsheet-based, and within this market Microsoft Excel now has by far the dominant position.

Even though the spreadsheet based modeling is very popular there are various cons attached to it. It takes significant amount of time, effort & money to maintain accuracy and relevance. There is a high chance for error in entering values or formula in spreadsheet. It is not a good tool for planning .Organizations should integrate various systems for their planning and forecasting. Software solutions pitch in here with reduced human error in data entering, integrated business intelligence with inbuilt graphs and report generating ability.

IT solutions play a major role in financial modeling. It helps not only in financial planning & budgeting but also understands the various risks surrounding the business. A successful solution should be help the management take up strategic decision even when there is an economic challenge. The basic features that an organization would expect a solution modeler to address would be:

Unforeseen events are often the catalyst for building models that answer urgent questions. A solution could create a flexible accurate model that creates answers at this time of need. An example would be, to understand the impact of cash flow on your business in the economic downturn. Solution would be able to model a scenario where it would determine the company's exposure to delinquent accounts.

The customer should be able to add scenario and assumptions to the business model to see the full range of possible implications. The solution should have visual analytics to identify important trends and opportunities that would not be there in framed from a spreadsheet.

Model flexibility:

Transparency:

Adaptability:

Netting:Collateral:

Solution should be able to continuously update with the business realities. Spreadsheets and BI systems work in silos to create business models but it is more time consuming and error prone.

In fluid economic conditions the when resources are shifted an excel based model would be a liability as it would be very much proprietary and bind to the author.. New solutions in the market are transparent.

The modeling solutions are able to adapt to the rapidly changing market and business conditions.

The solutions in the industry focus not only in planning and forecasting but also on the mitigation plans during uncertainty. Various risk mitigation techniques included in the solutions are:

The solutions enable all types of netting agreements. Assist the provision of collateral support to provide risk mitigation

from collateral received and manages the process of collateral valuation and margin cells.

Integrated Financial Statements Model

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Risk Transfer:

Key IT solution players in the financial modeling space

Arising from credit derivatives as well as along counterparty hierarchy lines including multiple parent relationships.

The below table shall specify the various products of industry leaders, its features and the geographic spread.

Vendors Produ cts Key features Geographical reach

SAS® SAS Financial Management

SAS ActivityBased Management

Dynamic consolidation §Bidirectional, dynamic, secure link to the

common data repository. §On-demand access to actual and planning

data. §On-demand consolidation for enhanced what-

if scenario modeling. Accounting logic §Support for multiple accounting standards and

partial ownership consolidations. §Manual journal adjustments. §Balancing rules and allocations. §Roll forward of retained earnings. §Multiple exchange rate sets and currency

translation adjustment for single and multiple accounts.

Budgeting and planning §Rolling forecasts. §Driver-based, top-down and bottom-up

budgeting. §Workflow control and budget-cycle seeding. §Advanced SAS Analytics for intelligent

forecasting and supplementary schedules. Data management §Common data model for integration with

other SAS solutions. §Extraction, transformation and loading from

virtually any data source. §Validation of data while importing and

automatic generation of error reports when data importing fails.

Information delivery §Automatic notification for key information,

such as KPIs. §Workflow managed for individual users. §Data flow automated, minimizing the time

users spend gathering data

SAS has customers in 112 different countries spread across Asia Pacific, Africa, Europe, Latin America & Caribbean, Middle East and North America

Cost and profitability measurement §

product, customer and organization. §Capacity costing and modeling, what-if

analysis for resource planning, as well as activity-based budgeting

§Flexible cost pooling and flowing. §External and internal bills of cost can be linked

directly to an account. Modeling §Client-server, Web-enabled, multiuser

architecture with role-based security and support for multiple, concurrent modeling sessions.

§Model database independence and method independence

§Intuitive workspace to manage models, reports and OLAP views.

§Visual print-and-click assignment creation and views.

Analysis and reporting §Seamless data sharing with SAS Strategic

Performance Management to identify activity-based management measures and publish them as metric tables.

§Sixteen standard reporting templates available.

§Integration with SAS Business Intelligence allows Web-based and self-service reporting and analysis.

§Integration with SAS Add-In for Microsoft Office allows accessing the database through Microsoft Excel for reporting and analysis.

Data integration §Data warehousing capability. §Embedded data quality processes. §The SAS services API allows you to import

DBMS staging tables, automate model creation and maintenance, and import/export data to and from another DBMS.

§Ability to read from/write to nearly any data on any technology platform in batch and real time

§64 bit Processing for high performance, highly scalable enterprise modeling and analytics.

§User Roles and Permissions for controlled and secure sharing of models. Addresses many of

Dimensional profitability analysis across

Quantrix is used by customers in more than 40 countries and in five languages

Quantrix Quantrix 64

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the risks inherent with collaborative modeling.§DataLink™ for integration with databases, web

services, delimited data files, and other custom systems using JDBC/ODBC, SOAP/XML, and direct data import.

§Quantrix Application Programming Interface which enables organizations to develop solutions, extensions and plug-ins with Quantrix at the core.

§Comprehensive modeling methodology§Performance inference§Interactive weights engineering§Innovative modeling techniques§Unmatched modeling expertise§Flexible modeling options§Analytic support services

USA and Canada, Brazil (South and Central Americas), UK (Europe, Middle East and Africa), Singapore (Southeast Asia), Australia, China and Japan

Fair Isaac® Fair IsaacPredictive Analytics

Key industry players in Risk Modeling (Basel II)

Vendors Produ cts Key features Geographical reach

§Robust Calculations for Credit Riskand Capital Adequacy

§Determine potential capital requirements based on stress tests that explore changes in market data, business-partner ratings, transactions, positions, and other parameters of the calculation framework

§Supports the reporting transparency and traceability necessary for the supervisory review required under Basel II

§Flexible Functions for Disclosure and Reporting§Integrated Data Management by incorporating

both historical and financial databases§Help to reduce the complexity of IT systems,

trim software maintenance and development costs, and resolve the many integration challenges of a new regulatory environment

30,000 best-run businesses in over 120 countries run SAP financial applications across Americas, Europe, Africa/Middle East and Asia Pacific

SAP® SAP Basel II application

§Credit Risk capital requirement assessment for all Basel I and II approaches

§Market Risk capital requirement assessment for standard Basel I and Basel II approaches

§Operational Risk capital requirement assessment (Basic Indicator, Standardized Approach incl. ASA).

§Comprehensive regulatory report ing

capability§Capital stress testing and scenario analysis

capabilities§Large Exposures/Risk Concentration

calculations, reporting and analysis tools§Liquidity risk and interest rate risk can be fully

assessed using Fermat ALM which enables income sensitivity analysis according to

Algorith-mics

Algo Credit Regulatory Capital

More than 300 global clients in over 30 countries. Also 70 of the world's top 100 banks rely on Algorithmics' software, content and advisory services

§Rigorous and efficient data management§Optimized calculations§Audit capabilities§Pillar 2 analytics§Regulatory and managerial reporting

Fair Isaac® Fair Isaac Basel II Analytic Services

USA and Canada, Brazil (South and Central Americas), UK (Europe, Middle East and Africa), Singapore (Southeast Asia), Australia, China and Japan

§Streamlines IRB implementation§Provides superior analytics and operational

decision§Targets regulatory requirements and unique

business needs§Offers flexible engagement styles§Leverages ongoing research and development

Fermat Fermat CAD

Conclusion:

References:

With the rapidly changing business and economic condition any business institution should have their decision making tools up to date. Gone are the days where the organizations are satisfied when the decision making tool just provide them with forecasting, budgeting planning and modeling details. Some thing extra that a solution can provide for making future critical decisions would be of higher demand for the organizations.

- SAS Financial Management, http://www.sas.com/solutions/financial/fms/index.html - SAS Activity-Based Management, http://www.sas.com/solutions/abm/index.html - Quantrix, http://www.quantrix.com/Integrated_Financials.htm - Fair Isaac, http://www.fairisaac.com/ficx/Products/ - SAP, http://www.sap.com/industries/banking/pdf/BWP_SB_Improving_Bank_Credit_Basel

%20II.pdf- Fermat, http://www.fermat.fr/telechargement/web_Fermat_e-brochure%20Feb-2008.pdf- Algorithmics, http://www.algorithmics.com/EN/solutions/regulatorycapital/benefits.cfm

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Historical perspective of government intervention in banks across the worldJayaprakash Kavala

With the recent financial crisis, governments across the world have made concerted efforts to revive the financial institutions. Fearing the immense

spillover effects of crisis in financial system on the rest of the wider economy, governments intervened in the financial markets predominantly through the following measures: 1.Capital infusion into banks in lieu of equity stake 2. Debt restructuring plans 3. Ensuring the liquidity in inter-bank lending does not dry up. In this context, lets us look back and take a view of the historical government intervention in banks across the world.

United States has a culture of laissez-fair (hands off) free market capitalism as the economic ideal but the practice has been different at times. Over the last century, federal government has occasionally taken stakes in railways, coal mines and steel mills, and has even taken controlling stakes in banks when it was deemed to be in the national interest. Government Bank investment programs are usually called nationalization programs but in United States nationalization is word to avoid, given the aversion to anything that hints of socialism. Equity holdings by federal government were a valuable tool for shoring up the financial system during the first half-century after the United States constitution took effect.

In 1917, the US government seized the railroads to make sure goods, armaments and troops moved smoothly in the interests of national defense during World War I. Bondholders and stockholders were compensated, and railroads were returned to private ownership in 1920, after the war ended. During World War II, the US government seized dozens of companies including railroads, coal mines and, briefly, the Montgomery Ward department store chain. In 1952, President Harry Truman seized 88 steel mills across the country, asserting that unyielding owners were determined to provoke an industry-wide strike that would cripple the Korean War effort. That forced nationalization did not last long, since the Supreme Court ruled the action an unconstitutional abuse of presidential power.

In banking, the U.S. government stepped in to take an 80 percent stake in the Continental Illinois National Bank and Trust in 1984. Continental Illinois failed in part because of bad oil-patch loans in Oklahoma and Texas. As one of the country's top 10 banks, Continental Illinois was deemed "too big to fail" by regulators, who feared wider turmoil in the financial markets. Continental was sold to Bank of America in 1994.

During the great depression of 1930s, US formed Reconstruction Finance Corporation. The agency was established in 1932 and it made loans to distressed banks and also bought stock in 6,000 banks at a cost of about $3 billion. When the economy eventually stabilized, the government sold the stock to private investors or the banks themselves.

Europe has a demonstrated far more comfort in governments' having stronger hands in business. After the World War II, several European countries nationalized basic industries like coal, steel and autos. Governments continued have controlling stakes in these companies until the 1980s when most western economies began liberalization. When Sweden faced financial meltdown in 1990s, the Swedish government acted swiftly and bought controlling stakes in the banks.

Australia witnessed a financial crisis in The 1890s and the crisis was a notably Australian phenomenon although the severing of financial support by British investors was the trigger for the collapse of many financial institutions in 1893. Out of 64 institutions which accepted deposits in 1891, banks in one sense, only 10 weathered the crisis; the rest suspended or failed, 34 permanently. Of 28 banks proper, only nine stayed open continuously. Any bank that survived the crisis was due in large to belated government intervention in the form of large loans to the remaining institutions.

In Mexico, the devaluation of Mexican peso in December 1994 provoked a profound economic downturn and revealed a fragile banking sector. Although Mexico had already liberalized many parts of the economy and privatized state run banks, many banks still had lax management and all banks operated with in weak accounting and supervisory environment. Fearful that the financial system would collapse under a rising level of past due loans, the Mexican government mounted a rescue of the banking sector that included intervening in the daily operations of some problem banks while establishing a series of capitalization and restructuring programs available to all banks.

The performance indicators of the effectiveness of government intervention are improvements in banks' capitalization, profitability and liquidity post the intervention.

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Thus history suggests that government intervention in banks had positive outcome if not a resounding success. Let us only hope that the current efforts the governments across the world reviving the financial institutions would become successful and ultimately revive the wider economy. The performance indicators of the effectiveness of the government intervention in banks are improvements in bank's capitalization, profitability and liquidity post the government intervention. Capitalization, measured by net capital over risk weighted assts, demonstrates how prepared the institution is for unexpected development. Profitability, measured by return on assets, shows how well bank's assets are used to generate revenues. Liquidity, measured by ratio of loans to deposits, demonstrates how quickly a bank could convert its assets to cash. Lets us wait see how the banks will perform on these parameters in the next few years. If these efforts become successful, it could long be studied by the historians as a textbook case of the emergency role that government can play in rescuing a dwindling economy.

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Private Equity Firms Intervention & Letting in Vulture InvestorsTathagata Biswas

ntroduction:I At its simplest, P/E firms, with their investors, will acquire a controlling or substantial minority position in a company and then look to maximize the value of

that investment over a period of time. The time horizon varies from 3 to 5 years on an average. P/E firms source their capital from High Net Worth Individuals, Pension Funds, Insurance Companies & Trust funds to name a few sources.

Private Equity firms receive a return on their investments through IPO's, M&A's and through re-capitalization.

Vulture investors, on the other hand are individuals and institutions that come to the aid of, or prey upon, troubled companies. They differ from P/E firms in 2 key aspects. They target companies who typically have strong balance sheets but have fallen upon troubled times. Basically this is nothing different from one of the fundamentals of the US Economy-if you have a bad credit history-you pay higher interest on your loans-if you manage to get one.

1. They do not control a majority stake in the organization-in fact they do not buy common shares at all. They either lend money at extraordinarily high interest rates or demand a chunk of equity in return for the loan or buy stock which is highly discounted to the current market price.

2. They operate on an extremely short time horizon- 1 to 2 years at the most.

Value Proposition:

Case-Studies:

Leading P/E firms share an investment insight and posses value-added operating capabilities to aid companies undergoing change, as well have a high degree of comfort in dealing with complexity and distressed companies without getting involved in the day-to-day operations of the acquired entity.

This has resulted in private equity firms shifting focus to enhancing the performance of their existing investments. The path to a leaner, meaner organization usually comes with hard decisions and strict financial discipline-which PE firms strictly administer to their holdings. Given today's global economic scenario-this is just what the doctor ordered.

However, given today's grim economic scenario-P/E firms are finding it difficult to raise capital either from HNI's or from Pension/Insurance funds. This acute capital shortage has hit the P/E firm's ability to invest in organizations.

Vulture Investors, on the other hand do not get involved in the operations of an organization. Their dealings are mostly financially oriented. However, when vultures do come in-the health of the prey in terms of investor confidence & balance sheet strength actually improves. And it gives the much needed breathing space to the prey in times of crisis. Where the prey goes from the temporary position of strength depends on the prey's fundamentals, leadership, merits of its business models and prevailing economic conditions

In 1986, a leading PE firm, KKR completed a friendly $5.5 billion buyout of Safeway-a giant supermarket chain-albeit in dire financial straits, to help

VULTURE INVESTORS

• Usual time Horizon of 1-2 Years on an investment

• Focus on Troubled Organizations

• No Interference at Operation level

• Returns usually immediate

• Usually do not aim for majority ownership

PRIVATE EQUITY INVESTORS

• Usual time Horizon of 3-5 Years on an investment

• Focus on organizations which can un lock value

• Usually get down to organizational management

• Returns gradual-either at IPO, Recapitalization or M&A

• Usually take up majority ownership

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management avoid hostile overtures from Herbert and Robert Haft of Dart Drug. KKR in return, forced the company to accelerate restructuring and more quickly trim the chain to its most profitable core.

The deal left Safeway burdened with about $5.75 billion in debt in 1986. Safeway pared that to about $3.16 billion within a year by aggressive sale of assets, concentrating on higher profit margin retail lines, decentralization of management, elimination of less successful divisions & optimizing operations. Safeway had 2,326 stores in July 1986; it has 1,161 in 1988. However, Safeway was making more operating income on a $13.5 billion sales base in 1988 than it was on $20 billion sales base in 1986.

Safeway lived to fight another day and made up for the sold stores by aggressively acquiring regional supermarket chains in the late 90's notching revenues of $40.5 Billion in 2007 and is currently ranked as the #10 biggest US retailer.

Warren Buffet has been always looked upon as a “value” investor and his investment firm Berkshire Hathaway has been credited with the Midas touch many a time. Organizations where Berkshire picked up equity were suddenly looked upon as good investments by the markets.

In the aftermath of 9/11, Warren Buffet in July'2002 (along with two other investors) bought $500 million in convertible notes from Level 3 Communications, Inc., one of the largest fiber-optics network companies. (Buffet's share totaled $100 million.) The deal boosted Level 3's cash position by 50 percent and bolstered its status at the markets. But it came at a stiff price. The notes pay 9 percent annual interest, and the holders can convert the notes, at any time, into common stock at $3.41. The stock traded at above $5 throughout July'02. Effective Returns for Buffet & partners-35% before the ink on the deal dried.

In a bull market, P/E firms have tended to make a quick buck. The focus is on returns that can be made as quickly as possible. This is usually done by either coming out with an IPO or by offloading equity.

In a bear market, however, things tend to be different. Starting with the sub-prime mortgage crisis in August '2008, the cost and availability of credit for private equity deals has significantly tightened. Credit concerns – combined with the intense pressures of a highly competitive industry – have slowed the flow of new deals. This has led to P/E firms concentrating on improving the organization metrics in times of crisis to make sure that the organization comes out of the crisis stronger.

Vultures, in general come to play only in times of crisis. Vulture investors, on the other hand do not add significant value to either the organizational bottom line or improve the organizational metrics. Vultures concern themselves with short term gains on their investment at any costs. Vultures do aid in short term liquidity & give

Conclusion:

the prey a fresh lease of life in a troubled environment but the cost for this privilege is in general, way too high. Having said that for many organizations who have strong fundamentals in place, a temporary infusion of cash is almost like a shot in the arm when you need it most.

- http://www.washingtonpost.com/wpdyn/content/article/2007/03/14/AR200 7031402177.html

- http://www.boston.com/business/articles/2007/01/11/us_private_equity_ funds _ break_record/

- http://www.dealogic.com/ - http://en.wikipedia.org/wiki/Private_equity - www.cio.com

References:

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Effective approach to risk management- Lessons to be learnt from the financial crisisJayaprakash KavalaWhat changes are required in the current risk management practices of the banks in light of the current financial crisis? What can the risk managers learn from recent experience and do things differently in future? This article highlights some of the imperative practices in effective risk management in banks.

Data is crucial for risk management in the banks. Risk management systems are only as good as the data that is input to the risk management systems. Lack of transparency and accuracy in underlying credit risk factors is detrimental to sustenance of financial institutions.

Despite having a very strong framework in the form of Basel 2, Risk managers across the world have failed to avoid the financial crisis because many of the institutions have implemented bare minimum requirements of Basel 2 with a view to just comply rather than following the best practices. Banks must stop viewing risk technology as just a regulatory necessity and embrace it as a way of saving money.

Banks must go beyond Basel 2 compliance and aggregate a broad range of risk data in multi-year time series that is useful for both advanced analytics and rapid ad hoc analysis. This data must be linked with the customer life cycle is information is available at account level from origination, through servicing, all the way through recovery. This linkage through the lifecycle provides a key foundation for key analytics such as probability of default, loss given default.

Market risk data must be managed better and banks should address the integration between credit and market risk and analyze the impact of potential losses spilling from credit to market and market to credit. Banks must develop and maintain unique data for counterparties, with appropriate models to assess the enterprise exposure to counterparties across the trading and banking book.

Banks should ensure that processes are set in place for effective data governance across the enterprise. This includes management of data definitions including the taxonomy of risk information such as 'default' or 'yield', ensuring the data quality.

Aggregation of enterprise risk information to ensure transparency

Focus on risk data governance and data quality

The data dictionary helps define and communicate conceptual and logical data models as part of the integrated modeling framework. The framework becomes a reference for all levels of organization from programmers to the senior management. Changes to the data with out documented processes impact the reliability and credibility of the information. These tasks should not be left to IT staff. It is the core responsibility of the business in partnership with IT. The best practice is to manage the data dictionary using metadata tools. Meta data tools are vital in establishing the linkage of information and quickly determining how the data changes as it passes from front end systems through data base and ultimately to users in way of reporting.

Though Basel 2 has been major driver for improvements in risk management practices in financial institutions across the world, the banks have implemented risk management systems with a narrow view to comply with regulatory minimum for capital adequacy with minimum value to business leaving information silos untouched between retail credit, commercial credit, market and operational risk areas. Banks must deliver integrated views of portfolio concentrations across the business lines and be able to shock and stress these concentrations for adverse economic events. More predictive, forward looking loss models are required that incorporate multiple model types to reduce model risk.

Financial institutions, governments and investors who live with or manage risk will have to cooperate as peers to forge an industry solution to the crisis and negotiate

Adoption of risk applications across all levels of management

Collaboration among industry players

Banks must stop viewing risk technology as just a regulatory necessity and embrace it as a way of saving money.

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common grounds for risk assessment models and the open sharing of their underlying data. This is similar to the collaboration that exists currently to combat fraud and money laundering.

Financial services industry needs to embrace the idea of sharing the intellectual property (IP), so that everyone derives benefit. Many pharmaceutical companies, for example, contributed to the public genome project because their business model depends not on patenting genes but on discovering new drugs. In software industry, open source Linux has enabled IBM and RedHat to migrate the locus of competition from operating system to applications, integration, and services. The technology, data and risk assessment models that are used to value investment products can be shared.

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Emerging Operational Risks in Retail PaymentsRohini Sawant

Defining Risk

Redefining Risk

Speed-through lanes at toll booths, electronic checks, cell-phone payment and contactless card payments are just a few examples of new payment methods recently introduced to the market. We have seen a bevy of such new products that solicit your attention, many struggle to grow, some fail and few get accepted in routine commerce. All these face a variety of risks. The evolved forms of data breaches, identity thefts and fraud force the electronic payment landscape to redefine the underlying element of 'risk'.Emerging retail payments are those that differ from the established payments in a significant way- technologically, contractually, legally, or conceptually. In a broader sense any risk can be categorized into five major buckets: fraud, operational, legal, settlement and systemic. It is observed that emerging payment methods are particularly susceptible to fraud and operational risks; data security risk and risk of illicit use are two such notable risks posed.

Data security risk is a form of operational risk involving unauthorized modification, destruction, or disclosure of data used in or to support transactions. Risk of illicit use is the risk that a payment method may be used for illegal purposes, for example, money laundering, terrorism financing, or the purchase of illegal goods and services such as drugs or child pornography.

Access Channels and Payment MethodsACH e-checks:

Accounts-Receivable Conversion (ARC) -

TELephone-initiated (TEL) transactions -

Risks Involved –

The electronic debit transactions that allow banks and their clients to convert checks or information from checks into ACH debits are referred to as e-checks. The NACHA has developed rules and formats for six new e-checks of which we discuss two, ARC and TEL.

ARC rules permit businesses to transform checks mailed by bill-paying consumers into ACH debits. Roughly 6 percent of checks written are now being converted to electronic debits under ARC rules.

TEL transactions are debits to consumers' accounts authorized by the account holder via telephone to a merchant, vendor, or service provider. These transactions make one-time ACH payments available when written authorizations are not feasible. TEL transactions account for about 7 percent of e-check volume.

?For ARC, the largest risk is operational where as for TEL transactions, fraud is a larger risk.

?For ARC, retail lockbox processors convert checks sent by consumers to billers. In contrast, TEL transactions rely on customer input of account

information via telephone, a context in which the data and customer's identity cannot easily be verified.

?Also, inadequately researched bank relationships can undermine the “gatekeeper” function in this payment method, making it difficult to deny dishonest originators access to the ACH network.

?Use of third-party service providers for TEL can compound the difficulty of identifying illegitimate initiators by adding an intermediary between the payment-originating bank and its ACH debit-originating clients.

Established New

Well-known technologies initiate commonly knowntypes of payments or use new rules to create newtypes of payments.

Ex - ACH TEL

Existing access technologies initiate a new type of payment.

Ex - General-purpose prepaid cards

New technologies or networks access established payment methods.

Ex - ACH ARC

New technologies and networks initiate newtypes of payments.

Ex - Proprietary balance transfer

Noncash Payment MethodsTransaction, Clearing, and Settlement Processes

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General-purpose prepaid cards:

Risks Involved –

Proprietary Online Balance-Transfer Systems:

Risks Involved –

General-purpose prepaid cards, introduced in the 1990s, the cards function similarly to credit and debit cards at a point of sale. A customer swipes a plastic card through a standard reader, and the transaction is authorized and settled through a card network. In addition, some cards can be used to withdraw funds from ATMs and to make remote purchases or pay bills, similar to debit cards. Cardholders can often check the balances available on their cards through a website or telephone response system. Their distinguishing characteristic is that they require cardholders to turn over funds in advance for future purchases of goods and services.

?For general-purpose prepaid cards, nonbank institutions often stand between the cardholder and the bank that issues the card. This can complicate the regulatory of cards and introduce credit risk for the bank issuers and, potentially, the cardholders.

?Instances of fraud like using stolen credit cards to purchase prepaid cards at a self-serve checkout counter are a possibility.

?Third-party nonbanks may not have the same level of data security that banks have, potentially exposing consumer data to greater risk of theft.

In this type of scheme, customers establish an account with a service provider, such as PayPal, and use e-mail messages to initiate payments. The service is also used by small online companies and by individual customers who value the ability to transfer funds from person to person. For instance, Neteller, a similar service provider, is widely used for payments to online gaming sites. Other online person-to-person payment providers that follow a proprietary balance-transfer or similar model include GreenZap, StormPay, and eGold. PayPal is larger and more sophisticated than any of its competitors. eBay, the huge online auction business, acquired PayPal in 2002, and eBay transactions currently generate almost 70 percent of PayPal's dollar volume.

?Intentional user anonymity makes these services susceptible to illicit use, such as money laundering or payments for illicit purposes. Only the service provider has information about user identities.

?Operational errors and malicious attacks, such as “phishing”- employs

social engineering and technical subterfuge to generate “spoofed” e-mails that appear to be from a legitimate company- and “pharming” - attack redirects visitors from a legitimate website to an unofficial location by exploiting technical and procedural security weaknesses that compromise the domain-name server

?Generally in complex networks, the large number of digital “hands” and handoffs increases the difficulty of identifying and assessing risk severity and the exposures that can vary by user, channel, or product.

An important lesson to be taken here is that the products, services, rules, and technologies are all changing at an accelerating rate. So, too, are the tools for perpetrating fraud and data breaches as well as the techniques for mitigating them. Innovative payment mechanisms are making transactions less expensive and easier, while opening new commercial venues for payment transactions. As with more established forms of payment, however, the ultimate success of these inventive arrangements will depend on their ability to control risk.

Conclusion

Fun CornerFun Corner

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Rush your responses to: [email protected]

Spot the duo!Rohini SawantScrambled below are the names of vendors in the banking space and the products they offer, we want you to spot them out.

The winner for the January Thoughtline Edition is 'Swar Subhash'.The winner for the January Thoughtline Edition is 'Swar Subhash'.

N A C A S H A R B U

O L O A N P O S A I

I P R O C L M U N E

S S X B M U W E K R

N Y N O I S I V T U

E S O J S E D R R S

M T L F C C E E A A

I A L K B G A S D E

D R E L C O M I E R

H C E T D N U F L T

Rules of the game:1. There are 5 pairs hidden in the grid below2. You have to look out for the combinations in the following LOB's,

- Core Banking- Regulatory Compliances- Treasury Management- Cash Management- Trade Finance

3. You can look for the words straight; reverse, upwards, downwards, cross.4. If there are two words that make up one name, they can cris-cross each other or appear contiguously in any combination as stated above.

He will be receiving his mystery prize from the Thoughtline team soon...

The other correct entries were from:u u Joydeep Dutta u Dalia Davis Nidhi Mishra

Feedback &Suggestions aremost welcome.Please email to

[email protected]

Miniature Painting of IndiaBy Channakeshava

In western India from the 16th to 18th century miniature painting developed. These small paintings were part of manuscripts written at the time and illustrate the subjects of the manuscripts. These miniatures are found in some " Jain " manuscripts and are of 2 to 4 inches in size.

The paintings is filled with symbolism, and the use of flat space and pattern is clearly influenced by the Persian miniature style that preceded it. This piece, from India’s Punjab Hills, is an illustration of a traditional story of the god Krishna and his lover Radha.

The pattern of large scale wall painting which had dominated the scene, witnessed the advent of miniature paintings during the 11th & 12th centuries. This new style figured first in the form of illustrations etched on palm-leaf manuscripts. The contents of these manuscripts included literature on the Buddhism & Jainism. In eastern India, the principal centres of artistic and intellectual activities of the Buddhist religion were Nalanda, Odantapuri, Vikramshila and Somarpura situated in the Pala kingdom (Bengal & Bihar).

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