http://www.forbes.com/sites/tomgroenfeldt/2012/12/28/quartet-helps-financial-risk-management-move-to-real-time/

Tom Groenfeldt, financial technology blogger for the internationally renowned business and finance publication, Forbes, outlines the benefits of using real-time risk management in the face of the Big Data conundrum.
Comparisons are drawn between the trend monitoring capabilities of ActivePivot and the analytical approach used by intelligence agencies. Georges Bory, MD and co-founder at Quartet FS highlights the breadth of the technology: “Whether it’s Homeland Security or fraud detection or operational risk or control in a trading room, you are trying to apply statistics to huge amounts of data.”
Of course, regulatory pressure continues to remain at the heart of data analytics and the need to draw new insights from the information already available to banks. Bory summarises:
“Clients want to mine, analyse and check for outliers. They check for results that are not following trends. For example, if you have a business that is doing a certain regular profit and loss, and you suddenly see the P&L going up, they want to look into it. Is a group taking more risk or are they keeping risk under control? They are trying to use all the data available to control their risk.”
Groenfeldt also suggests that banks under severe budget pressure are increasingly looking at internal clouds as a way to deliver the analytics infrastructure needed while controlling costs – a capability ActivePivot is now able to offer in partnership with software provider TIBCO.

In recent years regulatory demands have permeated all types of financial institutions. Banks are finding themselves under increasing scrutiny to account for their actions with never seen before speed and accuracy. The likes of Dodd Frank, Basel III, and EMIR are necessitating an increasing need to assess, on a pre-trade basis, the credit impact resulting from new OTC transactions.

The most sophisticated methodology available is to use a simulation-based Credit Value Adjustment (CVA). Principally, the methodology involves using Monte Carlo simulations at a set of future time points for every trade. The simulations are then aggregated, netted by legal entity, and a final calculation is made using averages and maximums across all simulations. The number of future time points can vary between 150 and 300 and the number of simulations is typically 1,000 to 10,000 P&L simulations (or more). This methodology therefore encompasses probability of default, potential future exposures, full portfolio effect and netting node awareness.

The problem the banks are facing is that with current technology available, CVA calculations can take anywhere from 30 minutes to 30 hours or more to run for large over-the-counter (OTC) portfolios – clearly not something which fits into the required pre‐trade category. In a typical CVA model, 5 to upwards of 50 million simulated values are calculated per trade. Large sell side portfolios consist of hundreds of thousands, if not millions, of outstanding OTC transactions. The resulting data size alone is in excess of 10 terabytes of data, making effective CVA a true Big Data problem.

It is time for financial institutions seeking to tackle the 3Vs of Big Data – Volume, Variety and Velocity to look at the benefits that they could reap from using in-memory aggregation and analytics technology. Combined with distributed capabilities, in-memory analytics technology is now bringing immediate and tangible value to the complex discipline of CVA, by turning it into an operational tool used by trading desks to make the best trading and hedging decisions. This technology can provide an immediate and detailed understanding of risk exposure by counterparty, and revolutionises risk management by allowing users to hedge CVA precisely with up to date sensitivities. The technology also performs ‘what‐if’ analysis for both pre‐deal checking and counterparty credit change, in real-time.

The Basel III proposals for counterparty credit risk contain significant enhancements to CVA, including the need to account for variation in CVA more precisely than ever before once the legislation comes into effect in 2015. Financial institutions need to see compliance as a positive step, with the most effective technology elevating CVA from being a “after-the-fact” reporting routine, to something which can revolutionise the accuracy of tricky trading decisions.

The collateral world is changing, and changing fast. The transition of the derivatives market from OTC to an exchange-traded, centrally cleared environment, as framed by the Dodd-Frank Act and European Market Infrastructure Regulation (EMIR) regulatory reforms, is a game changer for all market participants – dealers, prime brokers, custodians, asset managers and hedge funds alike.
The need for financial institutions to have real-time access to their exposures, pledged collateral and collateral requirements across all asset classes and counterparties is no trivial matter.

Consequently, institutions have been rapidly building operating models around collateral management, inventory/position management and collateral optimisation. With these operating models in place, the timing is now right to look at putting in place a collateral optimisation layer that empowers firms with access to real-time, up-to-date collateral analytics at a group-wide level, and enables the efficient management of collateral to minimise costs and maximise return on assets.

There are a number of key factors necessary for success:
• A cross-silo asset pool – an effective optimisation layer needs to be able to aggregate across a firm-wide asset pool for efficient and cost-effective optimisation
• The ability to have access to an incrementally updated asset pool and a real-time optimisation framework ensures that firms are able to react effectively to changes in credit or market events
• The need for flexible front-office tools and analytics that help collateral trading desks pledge, substitute and recall their assets to increase revenue generation
• Ownership and customisation – firms need to be able to plug in their own bespoke algorithms that reflect their own collateral models.

Real-time aggregation and analytics technologies offer the myriad of capabilities necessary to meet these needs. To conform to regulatory reform seamlessly, financial institutions need continuous, up-to-date information on exposures, collateral positions and requirements at a moment’s notice, safe in the knowledge that they are truly optimising on the latest set of information available at that time. The most advanced tools allow users to manipulate data and perform instant ‘What-If’ analyses on large data volumes to evaluate alternative scenarios. For instance, the technology can be used to monitor the effect of potential ratings changes, market shifts, and asset withdrawals to make more efficient and informed business decisions. Moreover, the technology must also be flexible and customisable by the end user.

With Basel III due to come into effect in 2015, the regulatory burden is only set to increase. The time is now for banks to invest in the correct technology to maximise their business operations and easily comply with the challenges which lie ahead.

Businesses are increasingly recognising the importance of Supply Chain Management (SCM) in the overall performance of their operations. In the Gartner “Hype Cycle for Supply Chain Management 2012” , Gartner analyst says that “After years of a cost-cutting focus, SCM continues to rise up the corporate agenda.” In fact, effective SCM presents a clear path for beating financial inefficiencies and retaining important customers, and is becoming increasingly important across industry.

For example, a large car manufacturing company needs to be able to efficiently manage the supply of new cars to depots and retailers both nationwide and at a global level. Delays or poorly managed unplanned events are costly both in terms of customer relations and monetary penalties, and therefore need to be spotted early in order for potential errors to be rectified at the first opportunity.

However, achieving effective SCM is no minor undertaking. Quoting Gartner in the above mentioned report “The military term “VUCA” (volatility, uncertainty, complexity and ambiguity) aptly describes the environment in which most supply chains now operate”. Facing this challenge, organisations are increasingly recognising the inability of traditional BI tools to deliver the real-time insight into key performance indicators that need to monitor activity and mitigate risks. The time has come to look into new, innovative technological approaches that are better suited to turn SCM into a competitive differentiator.

Here, effective in-memory analytics tools can be invaluable. The technology allows large amounts of data to be analysed in real-time or ‘on the fly’ as needed, to pre-empt potential holes in the supply chain; ensuring issues are caught early, rectified or accounted for swiftly. This makes the business more reliable for its customers, and prevents last minute penalties in scenarios that are often avoidable. There is growing momentum for in-memory analytical technologies poised to become an enabler of effective SCM. Quoting Gartner, “Overall, SCM technology innovation continues, with functional systems, such as transportation and demand planning, continuing to be developed and enhanced to support functional performance increases. New capabilities are evolving in technology areas that take advantage of in-memory/high-speed analytical processing, extremely large datasets and cloud-based platform (providing infrastructure elasticity and easier integration/on boarding capabilities).”
Ultimately, for SCM, the latest technologies help facilitate customer demands and strengthen existing client relationships. Now that analysts are linking the benefits of analytics technology with SCM, it is highly likely that the most forward thinking businesses will follow suit – as some already have. It’s time for the benefits of the most cutting edge technology to be recognised outside of the financial services arena, with this latest step representing the first towards industry-wide adoption.

Gartner, Hype Cycle for Supply Chain Management 2012 by Tim Payne, 27th July 2012. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Faced with increased pressure and greater scrutiny from regulatory bodies, the demands on product control teams are increasing and are showing no signs of stopping. Teams require instant insight into Profit & Loss (P&L) data, the ability to quickly analyse a large number of KPIs, and the foresight to identify mis-matches to ensure the best possible operational decisions are made. It is in these demanding scenarios that in-memory analytics technologies bring significant advantages to the table and enable heightened visibility across the entire supply chain for product controllers. Three main reasons account for that:
Reason #1: As a result of the financial crisis, the product control function is now under pressure to deliver the official P&L at day+1 notice. The end-to-end validation process easily eats into resource bandwidth given that it is a complex procedure needing to be completed in a very limited time window. In-memory analytics is able to accelerate the task of aggregating massive amounts of heterogeneous data (front-office, risk, and accounting) in such tight time frames. The speed of data aggregation facilitates the P&L reconciliation process, and allows product controllers to spend valuable time on the “P&L explain” process.
Reason #2: “P&L explain” is more important than ever, and is a process which manual set-ups have considerably slowed down in the past. Product controllers now need to be able to rely on a decision-making environment that helps conduct the “P&L explain” process quickly and effectively. In-memory analytics enables personalised outcomes by offering product controllers the freedom to analyse data and KPIs using their own business logic or “what-if” scenarios, with the level of detail that suits them individually. Moreover, this type of technology has the power to generate smarter ‘on the fly’ analysis, as and when it is needed. For example, rule-based mechanisms can trigger automatic alerts to product controllers in the event of a data quality issue, a reconciliation problem or a risk limit breach.
Reason #3: Product controllers need to be able to secure rapid approvals on adjustments. More importantly, officially published and approved P&Ls need to be documented with traceable evidence so that they can be audited for regulatory reporting purposes. The most advanced in-memory analytics solutions can make significant contributions to the P&L sign-off process by enforcing automated workflows within the same analytical environment. This demonstrates a major improvement on traditional spreadsheets as a means to validate P&L adjustments. As a result, reporting times can be shortened and product controllers are better able to meet regulatory demands.
In summary, in-memory analysis has the potential to transform product control from a cumbersome process to a seamless tool that supplies teams with the full information they need, when they need it. This can drastically improve the communication of business performance between product control and the front-office, enhancing the transparency of the bank’s overall P&L and balance sheet. It results in a leaner and more effective organisation, with improved ability to meet stringent regulatory requirements.

With poor product control cited as a factor in the 2008 fall of Lehman Brothers, it’s no surprise that over the past few years the spotlight has been shining on this element of trading activity. Today, banks face ever increasing scrutiny from regulatory bodies, with the result that the product control function is now expected to address a widening range of issues. When the financial crisis kicked off, the FSA outlined key concerns about failings in the area of valuations and product control in its ’Dear CEO’ letter. Consequently, product control is now being recognised as playing a central role in the end-to-end trading process.
While this is broadly good news, greater investment in improved tools and processes is needed if product control is to realise its potential and meet these demands. Recent research from Investance shows that product control functions now need to demonstrate greater efficiency and responsibility than ever before. Investance identifies a trend towards product control being utilised as a stronger junction role between the business, risk and finance. Therefore, the function is expected to go beyond its traditional boundaries to tackle wider issues, such as the analysis of liquidity constraints or independent price verification. Many now expect product control to play new roles, such as challenging the front office or supporting finance in assuming general ledger ownership.
Consequently, product control processes need to evolve in order to meet an emerging need for end-to-end consistency and cross-functional assurance. This has a significant impact on the IT systems and applications that product controllers have traditionally relied on, with the result that older technology simply cannot cope with new demands. Unsurprisingly, financial institutions are now suffering from the consequences of a historical lack of IT investment in the systems needed to support product control effectively. A number of product control processes are still very manually-intensive, a situation which increases operational costs and risks. In addition, many systems are heterogeneous (for instance spreadsheets) and siloed, creating consolidation and consistency issues.
Combined, these issues mean that product control is now expected to answer issues that have simply not been addressed before. If investment banks don’t step up to the mark and invest in more effective technology, they will find themselves left behind in the race to take advantage of the advanced insights modern product control can offer.

Quartet FS commissioned a recent study by research firm Chartis Research, which investigates the key trends in the risk technology marketplace, and uses this to define exactly what is making the modern bank more risk intelligent.
Unsurprisingly, risk management and compliance are currently major priorities for financial institutions, with cutting edge technology increasingly necessary for banks wishing to navigate the risk regulation which has spread globally since the US’s Dodd-Frank Act. Consequently, global IT expenditure is expected to increase by $2 billion over the next 12 months – representing an annual growth rate of ten percent.
Chartis Research finds that firms are now looking for integrated, yet easily adaptable, modular solutions which can be used both cross asset and cross business-unit. The breakdown of silos and the growing complexity of the technology available mean that the industry is beginning to see a shift from banks building risk architecture internally, to a preference for external, componentised solutions. Risk analytics technology will likely continue to be the dominant recipient for risk IT expenditure, with faster analytics capabilities enabling risk insight and aggregation of data at the moment of need – something which simply was not possible just four years ago.
With IT budgets under close scrutiny, the need to do more for less will be a significant factor for decision makers, with ad hoc ‘on the fly’ analysis deemed increasingly necessary. This puts increased pressure on banks and technology providers to develop analytics capabilities which are easily scalable by the end user, and can be integrated effectively into existing architecture to enable smooth business growth in a cost effective manner.
Ultimately, regulators tend to show themselves behind the curve – reacting to market occurrences rather than taking a proactive approach to risk. Consequently it is essential that the risk intelligent, forward-thinking bank begins to put measures in place early, taking advantage of the risk analytics technology available.

Our industry report into the challenges investment banks face as a result of Big Data has drawn attention to a surprising in-house divide in attitudes to solving the problem.
There is clear discord between the attitudes of senior IT managers, versus systems architects/programmers, regarding the challenges of Big Data, and the ease of adopting in-memory analytics to combat it. Traditional technologies are favoured by IT managers, with 70% using data warehousing to manage their data. The same group sees the ‘newness’ of in-memory analytics technology as the biggest barrier to adoption at their bank, with 57% citing this as a significant problem. Conversely, lack of budget was chosen by the majority (47%) of systems architects as the main challenge to the use of in-memory analytics at their institution, suggesting more of a cost consciousness at ground level.
Knowledge of Board-level attitudes also differs greatly, with over half (53%) of the architects and programmers surveyed perceiving that the Board understands the benefits of in-memory analytics, and why the organisation should invest in it. Significantly, just a third (33%) of IT managers hold the same view. If it is to be assumed that those at managerial level have better insight into Board level feeling, the differences suggest that a lack of knowledge at Board level poses a major barrier to adoption – but crucially a barrier that is not recognised throughout all levels of the institution.
Despite this polarisation in views about how to tackle the Big Data challenge, the report highlights a general agreement that the technology is useful across a spectrum of business areas within a financial institution. In-memory solutions offer multi-dimensional analysis of highly volatile data, necessary to business success as data volumes increase and decision making is expected to keep pace. It is the only technology that can bring analytics closer to the user by incorporating it into those applications that run the business and operational processes. Consequently, it is only a matter of time until attitudes to technological change evolve and financial institutions respond more quickly to IT innovation.
Read the full report online at: http://quartetfs.com/en/white-papers/188-in-memory-analytics-solving-the-big-data-challenge-for-banks

Quartet FS research into the problems investment banks face as a result of Big Data has provided some thought provoking results. Of the IT Managers and Systems Architects surveyed, 100% find analysing Big Data a significant problem, giving irrefutable evidence that Big Data is something which needs to be addressed sooner, rather than later, in financial institutions.
The reasons behind this problem are varied. 47% of respondents say the main problem is the volume of data; 37% the velocity; and 17% the variety. For many, these results will be as expected, with high data volumes a notable problem across all industry sectors – not just financial services. Velocity of data is set to be the next big challenge for financial institutions as the frequency of data generation and delivery is overwhelming in industries like investment banking, and must still be analysed in near real-time. Conversely, data variety is a greater problem in other industries, such as ecommerce, where the data is unstructured and comes from a variety of sources.
It is unsurprising that there is currently little agreement within the financial services industry as to how to tackle the problem of Big Data. There is an inherent attachment to more traditional technologies, with over half of the respondents seeing data warehousing appliances as the best way to speedily work with Big Data. However a more progressive attitude is certainly evolving, with 35% of respondents already identifying in-memory analytics as the preferred solution. Crucially, the future is looking exceptionally bright for newer technologies, with the same research showing that the respondents believe in-memory analytics will become the predominant architecture within just 2 and a half years.
Read the full report online at: http://quartetfs.com/en/white-papers/188-in-memory-analytics-solving-the-big-data-challenge-for-banks

Watch this space for our next blog on the topic of in-memory analytics, which will dissect more of survey findings.

CIO magazine recently summarised the top five business analytics trends as ‘go big, go fast, go deep, go cheap and go mobile with business data’. This succinctly worded response to Big Data and other technology trends highlights the major changes that are currently being experienced in the world of analytics technology, and the importance for big businesses to stay ahead of the game.

But why is it suddenly so crucial for businesses across the spectrum to react quickly to situational changes? The ability to ‘go fast’ is highly prized in this competitive global market, and is arguably the most significant trend identified. One example would be a logistics operator delivering cars from the manufacturer to a network of dealers. The core of this company’s business will be in the terms of the various contracts across the supply chain, which ensure that a certain volume of cars are delivered on time in designated places. Real-time analytics technology is necessary to predict when these quotas will not be met or when storage space is unavailable – allowing the business time to negotiate the delivery periods, revisit the service level agreements or factor penalties into the balance sheet. Early warning of failure to meet targets is also beneficial to the relationship with the end customer, creating trust that problems will be spotted and solved early.

As businesses become increasingly global and data volumes get bigger, the need for timely analytics is becoming ever more apparent across all industries. It is no longer a waiting game to see which companies will be the early adopters of in-memory technology, real-time analytics will soon be a requirement for many in this global climate.