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					                                        2008 Sales Conference, Ascot, England



Algo ALM and Algo Liquidity Risk Summary
Document

1 Introduction
ALM and Liquidity Risk are important areas for Algorithmics in 2008. We see the
bundling of ALM with other risk types (Market & Credit) particularly important for winning
Tier 2/3 banks who wish to receive a system that provides a holistic solution. In addition,
the launch of Algo Liquidity Risk Standard Edition, Algo ALM Standard Edition and a
significantly enhanced Algo ALM (sellable independently of Algo Market Analytics) allow
for focussing on the particular strengths over our competitors (see Section 5 & 6 for
strengths and gaps). Given the events of 2007, liquidity risk is high on the agenda of
many risk managers and we have functionality that can meet this need.

Software is not only for assessing or measuring risk. Software is for effective
decision making. A good product entails not only a strong ability in
quantitative/analytics and software developments. It also requires a clear understanding
of market developments and of how ongoing phenomena in the real world are changing
clients’ needs. We believe that we provide a combination of excellence in both
quantitative skills and market knowledge that puts us one step ahead of
competition.

In the following, we will:
 • Show how the strategic importance of ALM and liquidity risk management has
    surged dramatically as a result of market developments, and how Algo products can
    effectively help firms to cope with the new challenges
 • Outline the main functionality strengths and explain the reasons behind apparent
    gaps
 • Provide a list of questions that would be useful for us to thoroughly understand the
    client’s needs so as to effectively address them in a tailor-made approach
 • Provide a list of questions that are commonly asked by customers, so as to help
    sales people to respond to client’s doubts
 • Outline resources and initiatives that we aim at exploiting to improve effectiveness of
    our response to market demand, including announcement that a dedicated pre-sales
    resource will be allocated to our team, description of the Practitioner Forum we are
    willing to initiate on an ongoing basis, and a full list of team resources with individual
    areas of expertise
 • Summarise product roadmap and suggested sale strategy.




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2 Surge of the Strategic Importance of ALM and
  Liquidity Risk Management
The strategic importance of ALM and liquidity risk has been increasing dramatically in
recent years. The 2007 liquidity crisis (please see Liquidity Risk Whitepaper Series 1 ) has
brought liquidity risk management into the spotlight. Firms have learned from hard
experience that failing to manage liquidity risk properly and cope with unexpected crises
can threaten their very survival. On the other hand, the competitive pressure on interest
margin and capital ratios has forced firms to pursue all means to exploit every possible
return from balance sheet items, emphasizing the importance of a consistent and
comprehensive management of both assets and liabilities, which is typical to ALM.

As a result, ALM and Liquidity Risk Management are different now to what they were
only 3 years ago.

We are pleased to note that the new developments we have been experiencing are
well in line with the strategic view that we poured into the development of Algo
ALM and Algo Liquidity Risk from the outset. Market developments and evolving
customer needs therefore put us in an excellent position to collect the fruits of this
forward-looking effort.


3 How Algo ALM and Algo Liquidity Risk Can Address
  New Challenges
Let us go into some of major market developments to show how Algo ALM and Algo
Liquidity Risk can help banks to address the new challenges.


A. Liquidity risk: regulations
The 2007 crisis has generated a new awareness of the need for a comprehensive
refinement of regulations on liquidity risk. There is no common regulatory framework
across national jurisdictions and only some local supervisors require banks to provide
regulatory reports on liquidity risk.

Regulations are bound to become a major issue in the near future. Algo has shown its
thought leadership on liquidity risk supervisory regulations with a white paper published
in December, 2007 (see above for reference).

        Algo Liquidity Risk Standard Edition will address the need to comply with
        regulations by including the production of standard reporting for banks
        which are currently subject to this obligation. We will start with reporting


1
“Liquidity Risk: Comparing Regulations Across Jurisdictions and the Role of Central Banks” & “Liquidity
Risk Management Assessing and Planning for Adverse Events” published in December 2007 and
available from: http://www.algorithmics.com/EN/solutions/myinterests/alm.cfm


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       required by the FSA for banks in the UK and subsequently add other
       jurisdictions.

Regulators increasingly require banks to rely on behavioural models for a more realistic
view of their liquidity exposures.

       Algo ALM behavioural model functionality therefore has an important role
       also here.

Regulators are increasingly recognizing the power of sophisticated quantitative analysis
for effective liquidity risk management, just as they have done for a long time for market
and credit risk.

       Thanks to its powerful quantitative framework, Algo ALM and Algo
       Liquidity Risk is positioning itself ahead of an important market
       development which is increasingly affecting the market.

       Algo ALM and Algo Liquidity Risk scenario-based framework, especially
       when equipped with the ability to produce dynamic views of the future
       evolution of the balance sheet via Dynamic Trading Strategies (DTS), is
       already now an effective instrument to help comply with Basel II Pillar 2,
       whereby banks are required to calculate the amount of capital to cover all
       their risks, including those not in Pillar 1 (e.g. liquidity risk and interest rate
       risk of the banking book). Thanks to DTS, such analysis can be performed
       on a time horizon than fits with the bank’s strategic and financial planning
       horizon, ensuring the more comprehensive consistency of capital need
       calculations. This is a unique feature our solution provides. No other
       software vendor is capable of providing such decision making
       oriented analysis.


B. Liquidity risk: best practice
One on the major outcomes of the 2007 crisis is a new awareness that liquidity risk can
no longer be the neglected risk, as failing to properly manage it can expose firms to
extreme risks.

Quantitative methodologies based on stochastic scenario generation provide the ability
to produce the most risk-effective tools for decision support. They allow more realistic
views of future exposures, increased effectiveness in detecting the risk factors to which
the firm is most exposed, and the performance of more significant stress testing.
In the white paper on best practices for liquidity risk management (see above for
references), the power of scenario-based quantitative analysis for effective management
of liquidity risk was thoroughly documented.

       Algo ALM can offer one of the best equipped software products in the
       market, that couples with a very deep knowledge of best practices for
       liquidity risk management.




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       Particularly significant to Tier 1 banks, the DTS functionality allows firms to
       go one step ahead of traditional static analysis by adding the time factor to
       risk analysis.

       DTS allows firms to include in their risk analysis their future developments,
       business plans and funding strategies, and to perform scenario-based
       analysis and stress testing on the resulting picture. This puts them in a
       position to assess the sustainability of their planned business
       developments under both likely and unlikely external contexts, and even
       under extreme and unexpected circumstances such as sudden market
       events. No competitor can offer such a powerful instrument for this
       task.


C. Behavioural models
Competitive pressure on interest margin has boosted the importance of exploiting all
possible returns from balance sheet instruments. This has increased the demand by
banks for the ability to model the actual behaviour of retail demand deposits, mortgage
prepayments and other instruments with non-contractual maturities.

Behavioural models also allow firms a more realistic representation of risks, and this
factor is also achieving a steadily increasing consideration in the more and more risk-
aware climate which is widespread after recent crisis events.

       Algo ALM and Algo Liquidity Risk are fully equipped to provide firms with
       functionalities for effective behavioural modelling. In addition, thanks to
       the DTS functionality, behavioural models can be subject to advanced
       scenario-based analysis for a comprehensive risk-driven view of the future
       that no competitor can offer.

A widespread use of behavioural models for managing positions requires a sound risk
management framework as it tends to add risk when, for instance, demand deposits are
treated as medium or long term finance. Scenario-based analysis instruments, and
particularly stress testing, provide a fundamental advice for a correct and sound
management of instruments under a behaviourally-adjusted model.

       Again, Algorithmics is better equipped than any competitor to cope
       with this need thanks to its powerful scenario-generating tools.


D. The shift to fair value-based measures
The evolution of ALM over the last few years has seen a shift from interest-based
earnings to a valuation based framework. Several phenomena contributed to this
development. For instance:
 • New accounting regulations (IAS/FAS) have broadened the span of instruments
   under ALM scope that are valued at Fair Value
 • The development of securitizations has de facto brought large amounts of illiquid
   assets such as mortgages, consumer credit etc. much closer to fair value than to
   cost accounting


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• Perception of risk embedded in company’s balance sheet has brought fair value
  considerations into the spotlight as they entail a much more long-term and
  comprehensive assessment of risk
• As a fallout of the competitive pressure, banks are much more likely to hold
  structured and complex products on their balance sheet, also in their ALM portfolios

In such a context, gap based risk measures (which are traditional for ALM) must be
increasingly integrated with value-based indicators.

       With Algorithmics the RiskWatch engine is in place to handle sophisticated
       derivatives and structured products without over-simplification, so that
       valuation and earnings can be calculated in a much more accurate manner.
       More generally, the widespread recognition of RiskWatch and the Mark to
       Future functionality as a market leader for supporting value-based
       decisions is the best basis to help firms to cope with the new needs.


E. New accounting regulations
The new accounting rules IAS/FAS have produced substantial impacts on ALM. One
major change has been the increased range of instruments under ALM scope that are
valued at Fair Value, as mentioned above. Another one regards hedging, which is now
subject to much more stringent regulations that require more sophisticated analysis and
optimization capacity as well as monitoring capacity. Failing to match these entails
exposing the company to undesired profit volatility.

       Algo ALM IAS39 functionality has been designed to cope with this need by
       ensuring firms the ability to define optimal hedge ratios and test hedge
       effectiveness with a variety of methodologies compliant with the
       accounting rule.


F. Fund Transfer Price (FTP)
FTP functionalities are increasingly requested for effective management of financial
risks, capital allocation to different businesses, and performance measurement.

    Within Algo we have addressed this need with comprehensive FTP
    functionality that separates earnings according to commercial margins and
    risk factors (interest rates, liquidity, credit etc). The functionality goes live in
    Q1 this year and we already have several signed clients.


4 Key Sales Drivers for 2008 (i.e. Liquidity Risk)
• Liquidity risk is a key 2008 topic. At the end of 2007 there were 2 Algorithmics
  Liquidity events attended by over 50 people which shows the interest in this area.
  We stand apart from the competition in being able to combine scenarios and
  behavioural assumptions which is the key to stochastic liquidity risk and stress
  testing.




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• Aiming to build a business oriented decision support system to investigate the trade-
  off between long term and short term profitability. We have hundreds of reports to
  address these issues by looking at different dimensions – deterministic, stochastic,
  multiple time steps.

• Regulatory drivers – We are going to address regulation to improve interest rate risk
  on the banking book and comply with diverse local liquidity requirements beyond
  those just from Basel II.

• ALM is combined with Market, Credit and Regulatory Capital all on the same
  platform. The combined package is particularly important for Tier 2 and 3 banks.

• ALM as standalone to banks requiring more sophistication of analysis than traditional
  ALM analyses


5 Functionality Strengths
• Ability to handle complex payoffs that exist in the banking book these days –
  examples of this is the increased use of securitised products (MBS, ABS, CDOs),
  more complex hedging tools (numerous options, FX and IR products) and the
  issuance of unusual market specific products (such as blended rate loans where the
  rate paid is lowest coming from a basket).

• Standard ALM and Liquidity Risk Gap analysis coverage – these include interest rate
  gaps, duration gaps, cash flow gaps, maturity gaps etc.

• Advanced interest rate risk analytics through simulation based calculations – this
  allows the true risk of complex products to be assessed using a stochastic scenario
  set.

• Retail data pooling via DataMart – we can rapidly pool millions of retail products for
  ALM as well as for Basel II purposes.

• Flexible decision based reporting tool – this allows ALM and Liquidity Managers the
  opportunity to spend their time making business decisions rather than just compiling
  reports. PWC in their report say that only 23% of respondents think risk management
  helps in ‘assisting management on a day-to-day basis with business/strategic
  decision-making state’ whilst 42% think it assists in measuring and monitoring risk.
  PWC summarise:
      “Few survey respondents believe that the risk management function and the
      individual business units are highly integrated; indeed, many think that there are
      coordination problems within the function, as well as between central risk
      managers and local risk managers 2 .”




2
    Creating value: Effective risk management in financial services, March 2007, PWC



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• Advanced behavioural modelling capabilities which coupled with Dynamic Trading
  Strategies produce a realistic risk-driven future picture that no one else in the market
  can offer.

• Stochastic scenario generation using the Algo Scenario Engine. This is a component
  which is unique in the marketplace and is important for understanding the tails of risk
  – those that represent the extreme of possibilities. Within ALM this is important for
  Earnings at Risk, Cash Flow at Risk and Economic Value of Equity at Risk and
  Liquidity at Risk.

• Patented optimiser to manage assets and liability portfolios – this is particularly
  useful for hedging and balance sheet management decisions.

• “What if” analysis – by separating Chart of Accounts and instrument data,
  hypothetical changes to the balance sheet and the risk impacts of these can be
  assessed for the purpose of deciding strategy.

• Powerful limit management system equipped with effective, easy-to-read indicators.
  These use a traffic light system to identify items that are in breach of limits (red),
  close to limits (amber) and safely within limits (green). Limits can be attached to any
  produced output within the reporting engine, even user-defined ones, providing a
  great range of possibilities for limit monitoring.


6 Functionality Gaps and Explanations
• Behavioural statistical data analysis – we are very strong at modelling behavioural
  assumptions but do not carry out the data analysis as we find this is best handled by
  one of the many statistical packages available on the market.

• Historical analysis – We are a risk management company and are therefore forward
  looking. We can perform historical analysis of risk factors for scenario
  parameterisation and the production of variance-covariance data. If the clients wish
  to perform further historical analysis we allow export of results into database (for
  SQL analysis) or Excel (csv) formats.

• Lack of full IAS 39 support – We calculate earnings in fair value and amortised cost
  approaches thereby aiding compliance with IAS 39 accounting criteria. We also
  calculate hedge accounting based earnings for hedges that comply under IAS 39
  rules. These include cash flow hedges, fair value hedges and net investment hedges
  (spot and forward). The prospective part of this compliance can be tested via a dollar
  offset method. We do not claim to comply with accounting standards in their entirety
  but we do think by providing the methodologies as described we produce results very
  close to accounting requirements.




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7 Algo ALM and Algo Liquidity Risk Information Flow
The following diagram describes ALM and Liquidity Risk information flow through
Algorithmics’ AlgoSuite.

    Load Historical Risk Factors                   Scenario
                                        ASE        Generation
                                                                          Reporting
  Market
  Market
   Data
   Data                                                                       ARA:
                                                                              ARA:
                                                                            (ALM &
                                                                             (ALM &
                  Algo                                      MtF                LR
                                      RiskWatch                                LR
                DataBase                                    Cube            Reports)
                                                                            Reports)
  Legacy
                                      (ALM & LR)
  Legacy
   Data
   Data


 Data
                                                                             csv file
 Loading          DataMart
                                Storage/SQL Lookup and optional              export
                                de-pooling to Account Level
               Data Pooling
                                Results



8 List of Questions to Understand and Evaluate
  prospect for Presales Stage
General
• Please give a brief description of corporate governance structure regarding ALM and
   risk management (committees, functions, reporting lines, headcount)
• What ALM and Liquidity Risk software do you use at present?
• What are the key reasons you are looking to upgrade ALM and Liquidity Risk
   software?
• What do you do to manage interest rate risk of the banking book at present?
• What do you do to manage liquidity risk at present?
• How many instruments do you have?
• What types of instruments are prominent in your bank? Do you have a product list?
• Do you produce daily ALM & Liquidity reports at present? If not, is this something
   you are interested in?
• Do you model products under behavioural assumptions?
• How do you plan your future liquidity needs in the medium/long term?
• What types of scenario analysis/stress testing are you interested in
   (Deterministic/Stochastic/Behavioural etc)?

Accounting (IAS/FAS)
• Does your company perform hedge accounting? Does your company perform
   “macro”/”portfolio” hedging?
• If yes, how does your company test hedge effectiveness?
• Does your company use trading instruments to hedge ALM risks (e.g. use trading
   derivatives to hedge treasury positions)?



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Scope of ALM / Performance measurement
• What instruments valued at fair value through profit and loss are used within ALM?
          o e.g. trading derivatives used to hedge risks in the banking book
• When assessing ALM performance, does your company consider:
          o interest margin
          o profit and loss from hedges of risks in the banking book
          o profits/loss from trading derivatives used to hedge positions of the
             banking book
          o changes in equity as a consequence of changes in fair value of
             instruments in the banking book (e.g. AFS assets)

Regulatory issues
• How does your company perform capital adequacy calculations under Basel II Pillar
   2 (ICAAP) regarding a) Interest rate risk of the banking book and b) Liquidity risk?
• Is your company subject to regulatory reporting on interest rate risk of the banking
   book or liquidity risk?
• Does your company use internal limits that reflect the structure and amount of
   regulatory guidelines on liquidity or interest rate risk?


9 Questions that the Potential Client May Ask – These
  represent some of the common questions we have
  received within the product team.
I have not considered ALM and Liquidity Risk to be a key area of Algorithmics. Why
should I look at it now?
ALM and Liquidity Risk today are not the same as they were 5 years ago. When we
conceived Algo ALM it was using a simulation based framework with calculation of
value, cash flow and earnings. This is a quantum leap from a gap analysis based ALM
package. Since its inception changes to accounting standards and Basel have meant the
requirement for fair value assessment is much more important. Developments have
taken place within our software for IAS 39, FTP and decision based, easy to use
reporting. Through our simulation framework and with continued upgrading to our
functionality we now have a market leading product, particularly for interest rate risk
management, behavioural modelling and Liquidity Risk management.

What is the benefit of choosing Algo ALM and Algo Liquidity Risk?
Algo ALM and Algo Liquidity Risk uses a simulation based framework to accurately
assess interest rate risk in a more sophisticated way than gap analysis. As well as
powerful analytics our system includes a web-based reporting tool for easy access and
manipulation of reports.

Where can I find out more about Algo ALM and Algo Liquidity Risk?
As a first step we can provide brochures, fact sheets, white papers and case studies.
Typically we then suggest a conversation with our ALM and Liquidity Risk team as a




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prelude to a software demo. This helps refine what features we focus on to meet your
requirements.

How do you handle non-contractual maturity deposits?
We have investigated these products thoroughly and have come to the conclusion that a
replicating portfolio approach is the industry standard and most appropriate for risk
analysis. The regressions for these deposits are done outside the solution using the
banks own data and a dedicated statistical package. The regression results are used for
modelling within the calculation engine. For cash flow and earnings analysis over time
these deposits are rolled over with the possibility of growth targets. In particular liquidity
stress tests these assumptions may not be used and instead withdrawals of deposits be
forecast.

What can you do for prepayments?
Prepayments are modelled for pooled products using one of the following methods;
using prepayment rate above the contractual amortisation, prepayment based upon a
time varying curve or via Intex and Andrew Davidson database links for CMOs and MBS.

How do you model behavioural assumptions?
We can model behavioural assumptions in both a simple and a more sophisticated
method. These approaches are as follows:
Simple Approach: Products with expected terms and conditions that vary from the
contractual ones can be modelled using these new assumed T&C. This approach may
be most appropriate for products such as credit cards and the behaviour is not scenario
dependent.
Sophisticated Approach: The timing and amount of cash flows can depend upon specific
market conditions. Using this approach rollovers, reinvestments, new business and
business strategies can be modelled. An example of business strategy may be to
reinvest in fixed term products unless rates are a certain amount, then floating
reinvestment is used. Behavioural features such as the withdrawal of a certain
percentage of deposits in a stress situation can also be modelled in with this flexibility.

How does the implementation process work?
In our experience successful implementations have clear assessable stages and goals
which we work toward. Before the implementation starts we need to look at the
resources available on both sides and look where any gaps need to be filled to reduce
the risk of problems later. Through either a Standard Edition approach where key
deliverables, requirements and objectives are provided as a standard package, or
alternatively via a full discovery process where we work with the client to determine
these we can achieve confidence that the client will achieve what they set out to and we
will deliver within time frames and budget.


10 Product Development Roadmap and Suggested Sales
   Strategy
In its current configuration, ALM & Liquidity Risk is part of the overall Enterprise Risk
Management offering of Algorithmics. As a result of a continuous development effort,
ALM & Liquidity Risk has been enriched with functionalities that have made it a much



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more effective decision support instrument for ALM and liquidity risk managers than it
used to be in the past.

The product development roadmap for the months to come is summarized as follows:

• Algo Liquidity Risk Standard Edition is planned to be available by the end of April,
  2008. In the first stage, the Algo Liquidity Risk Standard Edition should be proposed
  to clients already using Algo Market Analytics. This would allow leveraging on the
  widespread recognition of Algo Market Analytics. In addition, earnings are not
  essential to liquidity risk management as well as to market risk.

• By the end of 1Q08, Algo ALM Standard Edition will be launched. Again, as a sales
  strategy we believe this should be presented alongside the Algo Market Analytics
  Standard Edition.

• Finally, by the end of 2Q08 the significantly enhanced standalone Algo ALM will be
  ready for launch.

We will be looking for early adopters for both the Algo Liquidity Risk Standard Edition
and the new Algo ALM to maximize our ability to match customers’ needs.

A detailed list of available and planned functionalities of the Algo Liquidity Risk Standard
Edition, Algo ALM Standard Edition and Algo ALM (sellable independently of Algo
Market Analytics) are available in the CD that will be distributed at the sales conference.


11 Practitioner Forum
We aim at establishing an ‘Algorithmics ALM and Liquidity Risk Practitioner Forum’,
which will be a platform to present our product planning, get the customers feedback
regarding future roadmaps, and discuss current market topics. This will be a one-day
meeting here in London, with 1-2 representatives from each existing ALM customer.

Typical attendees would be end users of Algo ALM, preferably power users but junior
end users are also welcome. The objective for the product management team is to get
direct customer feedback on future product directions, to help with the question on
where we need to go to be ahead of the market and how exactly do we need to realise
functionality so that they actually meet the markets requirements and not only the need
of one individual customer (ensure that new stuff is re-sellable).

Clients are usually interested in user events for three main reasons:
    1) They’d like to have some influence on the product roadmap and want to discuss
        directly with the product management team
    2) They want to hear what other institutions do with the same product, how it can
        also be used, etc.
    3) Networking.




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12 Dedicated Pre-Sales Resource
It has been agreed that there will be one dedicated presales resource, Carles Herrero,
allocated to ALM and Liquidity Risk. Also, see the next section for subject matter experts
who will support you in your sales opportunities

13 Team Members and Areas of Expertise
Within the team there are four team members each with their own area of expertise in
terms of knowledge within the realm of ALM and Liquidity Risk:

Fabio Battaglia, Regulation, Accounting Standards, Business Intelligence

Paraskevi Dimou, ALM and Liquidity Methodologies, Integration of ALM with Credit
Risk, ASE

Steven Good, Analytics, Integration of ALM with Market Risk, FTP, Data Management

Mario Onorato, Solution Director, Refer to Mario for all ALM and Liquidity Enquiries




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