beyond monitoring where is your solution on the business ......solutions enable firms to immediately...

Velocimetrics

Beyond monitoring: Where is your solution on the business value

spectrum?

www.velocimetrics.com

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Copyright © 2009-2016 Velocimetrics Ltd. All Rights Reserved

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Contents

Executive summary 3 The analyst’s view 5

The APM versus ITOA view: why neither perspective offers it all 7

A model for deriving real business and client value from monitoring in financial services 9 Stage 1 Chaotic: Infrastructure health monitoring 9 Stage 2 Reactive: Basic responsive monitoring 10 Stage 3 Proactive: Understanding the business process 12

Identifying data cause and effect relationships 13 Payment or trade reconstruction: the regulatory angle 15

Payment or trade reconstruction: the profitability angle 15 Mapping business flows 16

Stage 4 Service Value: Assessing quality in its business context 17 Stage 5 Business and client value: Driving improvements 19

Avoiding an SLA breach 20 Avoiding an inevitable breach unnecessarily impacting clients 20 Proactively managing a client’s experience when impacted by a breach 21

Future gazing 23 Where does your firm lie on the monitoring spectrum? 24 About the author 25 About Velocimetrics 25

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Executive summary

Over the past 5-10 years, the technology available to support IT monitoring in fast paced, high volume, complex and geographically distributed trading and payment processing environments has matured at a considerable rate. Whilst the solutions previously available limited firms to reacting to incidents that had already happened, today firms can use tools that can predict and in many cases proactively avoid these problems occurring to start with. In the event that the incident is unavoidable, these same tools can be used to swiftly bring the issue to its resolution, whilst also quantifying and more effectively managing the business and client impact. Increasingly firms at technology’s leading edge are deploying solutions that look at so much more than just server or network traffic health. These firms are focused on assessing the real-time status of each and every trade or payment flowing across their servers, applications and networks. Tracking these items as they traverse business critical processes from the very moment they enter their environment to the point they complete. In doing so, these solutions provide a real-time understanding of how changes to these flows are impacting performance, the business or the end user’s experience. This is achieved by delivering critical, real-time insight into the entire technology stack supporting the business. Monitoring is no longer about technology that can just detect a queue that’s building, a full disc or a network delay, it’s about also enabling users to precisely comprehend the real business implications of these issues, identifying for instance, which trades are currently being impacted, how they are being impacted and the clients they belong to. By providing answers to these questions, when an issue occurs, advanced monitoring solutions enable firms to immediately comprehend their exposure in business terms, providing the insight necessary to make quick and effective decisions. In doing so, rapidly reducing an issue’s on-going impact, whilst proactively managing client experience levels. Ultimately, it’s about solutions that deliver as much value to the business as they do to the technology teams. It’s the industry’s more innovative players that have deployed these advanced solutions and they are now reaping the benefits. They are able to confidently analyse complex technical metrics and gain critical business insight. They can automatically comprehend how current system characteristics compare with past trends, and based on this insight accurately predict an activity’s impact on the orders, trades or payments being processed. All of which can be achieved in real-time. This information can then be used to proactively drive real business and client value. Machine learning is advancing faster than many people previously thought possible and developments in this space are influencing innovation in the world of monitoring. Routine and previously manual tasks are now being automated. New approaches are

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now emerging that will mean adopters of intuitive solutions will no longer need to spend large amounts of time teaching their technology what normal looks like. By detecting when a change in conditions is causing abnormal activities to occur, these approaches will drastically reduce operational costs as users will no longer need to continuously maintain and update semi-static rules and data points in environments, that are in reality, subject to frequently change. Developments are now underway that will enable solutions to teach themselves and automatically adapt to environmental changes. Furthermore, manually collating information from multiple sources to assess a potential or actual incident’s business and client impact is no longer necessary. The latest tools will very quickly and accurately do this for you, freeing up resources to spend more time remedying the issue, supporting impacted clients and working with the teams across the business to expedite the process of bringing the situation back to business as usual (BAU). The visionary companies, that have positioned themselves to take advantage of these smarter technologies, are now operating highly automated monitoring solutions, enabling them to deliver improved client service levels, be more competitive, flexible and significantly more cost-efficient. However, the vast majority of industry participants are still using solutions that deliver limited value and from a maturity perspective are really starting to lag behind. This white paper will explore the trajectory of advancements currently taking place, demonstrating how more contemporary approaches to monitoring are now delivering real business and client value to forward-thinking firms operating in the financial services market.

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The analyst’s view

The needs of various industry sectors often create very specific requirements that arguably only apply to that particular vertical. Financial services, for example, has some very specific characteristics that make it very difficult to align it with traditional analyst views of the offerings in the market. There is, in short, a dichotomy between the horizontal factors that analysts use to assess IT monitoring solutions against and the very specific characteristics of the financial services market. Whilst analyst reports are often incredibly well researched and deliver a significant amount of value, there can be situations where those responsible for implementing IT monitoring infrastructures, inside financial services firms, follow models built for broader intentions such as the ‘IT Management Process Maturity Model’ which was used and widely referenced in the mid 2000’s (an interpretation of which is depicted below). By slavishly following this horizontal approach, these firms can fail to take advantage of the more sector specific models and offerings.

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Those adopting more sector specific approaches often combine different tools to create a model that is more applicable for their sector and its requirements. Take for example the model below. This overlays a variant of the ‘IT Management Process Maturity Model’ with information sometimes used by analysts to map out the capabilities offered by application performance management (APM) and IT operations analytics (ITOA) vendors. In doing so, it is often suggested that over recent years the worlds they occupy have started to merge.

Models like this move their studious reader’s up a stack of layers. They demonstrate that at its very basic level monitoring should provide an understanding of what is happening, as it’s happening, across different processes. As firms then start to join up the dots, by aggregating the data generated by these different processes, the possibility of monitoring delivering real business value can become a reality. Ultimately, resulting in the ability to predict when an undesirable event is likely to occur, and if possible, prevent its development. Considering how monitoring in financial services is maturing in the context of these models can prove very useful. Especially for the CIOs that are understandably keen to determine how they can generate more business-wide value from their IT infrastructures. For them, these models advocate a way forward. They suggest, that with the correct processes in place, it is possible to move from a chaotic environment with multiple

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dashboards and processes, where huge swathes of time are swallowed up by teams reacting to and resolving problems, to one where incidents can be accurately predicted, proactively managed and their impact effectively controlled. One where, for instance, the firm is able to understand their client’s behavioural patterns on a weekly or monthly basis so well, that they can very quickly detect when something isn’t quite right from a service perspective, potentially before the client even realises an issue has occurred. It is these vital, actionable, business insights that make an immense difference. However, the reality is that whilst many firms would like to be operating a fully coordinated and coherent monitoring solution, that really delivers business and client value, it is often the case that their current approach restricts their ability to scale to such heights. In fact, many firms find that with their current set-up they struggle to move beyond the ability to react to events that have already occurred. As such, generating a significant gap between what is currently being achieved and their concept of a monitoring nirvana.

The APM versus ITOA view: why neither perspective offers it all Some firms look to either APM or ITOA vendors as a starting point for how they can build a more value driven monitoring environment. However, the fundamental DNA of the approaches taken by these firms can present substantial limitations. APM vendors often have a very network and/or application centric view of the world. Predominantly focused on the health of infrastructure components, they can often effectively monitor a single piece of data, such as a market data tick, traversing relatively simple hops, correlate this information and generate performance metrics. And they are usually very effective at doing this for extremely high data volumes. Whilst these techniques can be very useful in working out why something went wrong, by retracing the flow’s path, they are limited in their ability to predict that something is likely to go wrong. For this to be possible, the complete chain of events that could impact a trade or payment’s success or failure requires real-time examination. The first step in doing so is to not only be able to correlate data across simple hops, but also the more complex ones. Trading and payment environments are often very complicated and knowing a piece of data went into a process inside a server at a particular time and came out again a fraction of a second later, often isn’t good enough. What’s needed is a detailed understanding of exactly what is happening inside that process, so when something unexpected occurs it can be identified as such. Secondly, it’s essential to understand the data not just as bits and bytes, but in its business context and to be able to determine how one piece of data has impacted another. This is because trades and payments are the result of a series of cause and effect relationships that exist between different pieces of data. For example, to effectively analyse a complete trade in real-time it’s necessary to identify the driver

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relationships between different business items flowing across an environment, such as the market data tick that generates an order, leading to a trade. It’s by chaining this information together that the complete end-to-end business flow can be generated. By then comparing the performance of these flows, at every step along the way, to historical behaviour in real-time firms can predict an issue’s development before it happens. This incident might be a client SLA or regulatory commitment that hasn’t yet been breached, but based on past evidence, under the current and very particular set of circumstances, is very likely to occur if manual intervention is not quickly instigated. Conversely the IT operations analytics vendors can only analyse information after the fact, once it has been sent to their database. This isn’t real-time analysis, it won’t tell you what’s happening right here, right now, just that something has already happened and unfortunately that losses are probably already occurring. So its reactive versus proactive and in fast moving trading environments especially, this approach can prove incredibly expensive. Also, the data being collated by ITOA vendors may not be correlated as complete end-to-end trading or payment chains. Thus further limiting the degree of analysis that can be performed. It’s almost a fast data versus big data debate, where neither approach offers it all.

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A model for deriving real business and client value from monitoring in financial services

The desire to implement monitoring solutions that will deliver greater value begs the question of what do financial services firms need to do if they are to effectively move up through the different stages depicted by the models previously discussed? Along with what does a chaotic environment really look like, compared to one that is proactively managed? And what new capabilities will be required? To answer these questions, we have taken our business’s collective knowledge and experience of the performance monitoring and analytics market. We have looked at the different approaches to monitoring such as examining network traffic compared to data quality metrics and business processes, and overlaid these capabilities onto existing models used by industry practitioners. In doing so, demonstrating what monitoring capabilities we feel are needed to move up these different stages and the type of business and client value a more advanced solution can really deliver.

Stage 1 Chaotic: Infrastructure health monitoring The most basic type of monitoring is infrastructure health monitoring. The vast majority of financial services companies will have in place some degree of monitoring of this sort. It essentially involves examining the physical computing resources being utilised and checking they are actually working. For instance, a firm may monitor their environment to assess:

• Are their servers alive? • Are the processes that should be running on those servers up or down? • How are their networks performing?

These basic health checks examine the raw infrastructure. The type of analysis that may be being performed at this stage could include, for instance, microburst analysis. The rapid and unexpected increases of network traffic, known as microbursts, are seen as a measure of congestion. Their detection is often an early indication of capacity issues or that a component may be failing, which could consequentially cause difficulties in other parts of the process. This could be, for instance, a failing switch that is queuing up packets and suddenly releasing them all at once, causing stress to downstream components unable to efficiently process such a large influx of incoming packets. This type monitoring is purely infrastructure focused. It’s chaotic because monitoring isn’t being managed in a cohesive manner; in fact frequently nothing is joined up. The systems and networks may be monitored in silos, what the data traversing these processes actually represents, in a business sense, is often unknown and the knock-on effect of issues on the business process literally unpredictable.

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When an issue occurs, whilst technical teams may be able to identify the faulty component, it can be very difficult to quickly and accurately determine which trades or payments were being processed at the time. The internal and external business impact cannot be assessed. Consequently, the firm often cannot manage client experience levels or quantify the financial implications when an incident has occurred. In summary, firms monitoring only at this level are focused on assessing how healthy their technical infrastructure is, but this is in complete isolation from the business processes they support.

Stage 2 Reactive: Basic responsive monitoring At this stage, firms are starting to examine the actual data traversing their systems and networks at a basic level. The first step in doing this requires at least partially decoding the data being processed, so rudimentary details contained within the data can be accessed. This could involve picking out a piece of identifying information, such as a trade identifier (ID) in a FIX

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message, or a sequence number from a market data tick. In doing so, the firm can then generate simple performance metrics and start to analyse and improve technical infrastructure issues. By using these identifying factors and monitoring at multiple locations throughout a process, it is possible to detect that, for instance, the same piece of trading data was seen at three different monitoring points and join up the dots. By recording the time the data reached each of these locations simple latency metrics can then be generated. With this information users can determine, for instance, how long an order takes to traverse a particular component or network hop. In doing so, creating a basic data flow tracing how a single piece of data is moving through the environment. Should a problem then occur, these measurements can be used as part of the issue’s investigation, to determine if the data was delayed at a particular point in the process. Additionally in the case of market data, a firm able to detect the data’s sequence number can determine if it is, or is not, monotonically increasing as expected. Sequence gaps could indicate a component may be dropping data, resulting in trading systems for instance making incorrect trading decisions off faulty data. Firms that employ this type of monitoring can identify simple ways in which they can improve latency and detect gaps in the market data feeds being used by their systems to formulate trading decisions. This type of monitoring enables firms to react to problems that have already developed. Users can be alerted to basic issues and access analysis to more effectively resolve problems in simple business scenarios.

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Stage 3 Proactive: Understanding the business process As we’ve seen above, being able to determine how a discrete piece of data traverses an environment is useful. However saying that, it also has its limits. Other than in the simplest of high frequency trading (HFT) cases, financial transactions tend to involve more complex interactions between different streams of data; for example the market data a firm receives will impact price generation and quotation processes. Quotes cause trades to occur, trades require hedging and settlement. Therefore, gaining meaningful insight as to how different types of data interact is key to achieving an end-to-end understanding of a business process. This in turn requires the identification and modeling of the cause and effect relationships that exist between various streams of data. As a further example, of a more complex scenario, let’s look at equity algorithmic trading. Understanding why a particular algorithmic trading decision was taken involves the identification of:

• The client order(s) input to the algorithmic trading engine

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• The scenario that caused the algorithmic trading engine to decide to trade – for example a market price movement

• The market order(s) this generated, including an understanding of parent and child order relationships

• The resultant fulfillments with attribution back up the order chain. Performing this level of analysis requires the content of the data being monitored to be fully decoded. It’s necessary to extract client ID’s, parent and child order ID’s, prices, timestamps and other relevant information, so that all relationships between different pieces of data can be effectively linked. Tracking individual complex transactions helps the business answer critical questions like:

• Why did this trade lose money? • Why did we lose this order? • Did we provide best execution? • Why were we trading off the market? • Why did the algorithmic trading engine decide to trade in this particular case?

Increasingly these are questions that regulators are also asking. Given the individual transaction detail, it’s possible to build a statistical picture that looks at flow rates, latency distributions, how the systems react to heavy market conditions, or regular events such as non-farm payroll data publication. These statistics allow for more informed capacity planning and can be used to guide cost-effective investment in infrastructure. Again, regulators are now looking hard at whether firms have their arms around this problem.

Identifying data cause and effect relationships Through a process known as ‘association’, firms performing this type of monitoring can identify the cause and effect relationships that exist between the paths being taken by different pieces of data. So using the example above a firm could chain together, the market data tick that generated an order, to the completed trade and acknowledgement message. In doing so, forming the complete end-to-end trading flow.

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The chain of events and their associated timings can then be immediately examined. Enabling, for instance tick-to-quote, or tick-to-trade latency metrics to be instantly generated and compared to past performance to identify trends and outliers. Therefore, if your current trades are taking longer to process than previous trends suggest they should, technical teams can be alerted to the fact that degradation may be starting to occur in a part of the process and proactively investigate it. Today’s IT environments tend to be geographically distributed. Firms now often choose to co-locate their trading systems close to the exchange’s matching engine, whilst their traders remain in New York, London or Hong Kong, and their transactions frequently include international legs. Therefore, it’s important that these chains can be formed for trading processes that span multiple locations. Furthermore, for the analysis to be effective in preventing an emerging issue’s further development, it needs to be available in real-time. Employing real-time monitoring techniques that can easily identify cause and effect relationships is especially important for firms using algorithmic trading systems to route orders to multiple venues, as it provides a timely understanding of how orders are being executed. More sophisticated solutions should be able to immediately recreate the entire journey, correlating the same data as it is seen at multiple points and then accurately associating related information. As demonstrated by the following diagram, all the data related to an equity algorithmic trading decision can be correlated and associated together:

Real-time monitoring and analysis of these flows enables firms to gain a better understanding of how everything fits together and how their trades and payments are moving across business processes. This can have important implications from a pre-trade risk management perspective, enabling developing problems to be quickly identified and remediating action initiated before it’s too late.

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Payment or trade reconstruction: the regulatory angle Once a firm is monitoring what is happening to this degree, these chains of data can be stored as complete flows. Should a trade then require further investigation at a later date, for instance for regulatory purposes, all of the constituent parts are already chained together. Therefore, trade reconstruction requirements can be more easily met as the complete flow of all electronic trading events that influenced the trade’s lifecycle, along with their associated timings, can be retrieved and examined with minimal effort. It also reduces the risk of mistakes. Because the data has been tied together as it was captured, by default it is already aligned to the business or algorithmic trading rules in place at the time. Manual errors can be easily made if data needs to be reconstructed at a later date and these rules are applied retrospectively. With MiFID II’s September 2015 regulatory technical standards stating that one of the key reasons why firms need to synchronise their business clocks to a common time source, is that, “Competent authorities need to be able to reconstruct all events relating to an order throughout the lifetime of each order in an accurate time sequence.” And do so “…over multiple trading venues on a consolidated level to be able to conduct effective cross-venue monitoring on market abuse.” Having all trading data readily available in a structured manner can only prove beneficial as regulatory requirements evolve.

Payment or trade reconstruction: the profitability angle Being able to reconstruct complete trading flows can also prove very valuable from a profitability perspective. Take for example a foreign exchange broker concerned that latency issues could be impacting their request for quote (RFQ) or request for streaming quote (RFSQ) processes, resulting in quote publication delays that are generating loss making trades. With this degree of monitoring, the broker could easily examine trades that have made a loss, identifying:

• The quote the client traded off • The RFQ submission that caused the quote to be emitted • The pricing tick that drove the generation of that particular quote • The market data tick(s) that drove the pricing tick

By examining the associated timings at every stage, latency impacting events occurring across the entire quoting infrastructure can be detected and acted on. In doing so, directing engineers to the issue’s root cause so they can be quickly remediated, without the need to trawl through time-consuming log files. Advanced monitoring solutions will be able to apply these association techniques regardless of how complex the circumstances may be, for instance, in situations where parent and child trading relationships exist and also when a given system’s trade ID fails to be passed on through every stage of the process. More sophisticated solutions should also be able to do this irrespective of how complicated, distributed or tightly

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coupled the infrastructure components that comprise the end-to-end process prove to be.

Mapping business flows Only once firms have introduced this type of monitoring can they truly understand how their business processes are working in reality. It is almost axiomatic that the biggest risk firms face is that they do not understand the complexity of their own business processes and the systems that implement them. Because of this they can struggle to assess the impact of a given problem, or forecast the potential consequence of changing the process. Implementing business transaction monitoring not only provides this understanding, but also tracks changes to the business process over time, allowing process change to be better managed, with the ability to compare operations with how it worked in the past. In certain parts of the industry gaining this level of understanding can be a fundamental enabler for innovation and effective risk management, as the projected impact of changes to the business process can be more accurately predicted. Take for instance a very complex payment-processing environment. These may include 20-30 different systems that predominantly operate in silos. Many of the systems are legacy, having been inherited through merger and acquisition activities, or act as tactical solutions. Logic says these environments shouldn’t be this complex; but they often are. Payments may be input through multiple entry points. They are then routed down various paths depending upon their content and the business process implementation, seen at checkpoints along the way, and can then exit the process in many different formats. Ultimately this creates a highly complex network of possible processing paths via systems that don’t fully interact with each other. The payments being processed may be batches of individual instructions or payments that require warehousing for a period, and are dependent on inbound funds being received before they can proceed through to posting. In both of these examples, tracking these payments requires the ability to recognise and tie together the relationships that exist between disparate pieces of payment data. Only by really understanding how all of the individual components are working together, and how payments are moving through the end-to-end process, can engineers confidently introduce changes. Without this detailed level of understanding, engineers could easily make a change to a piece of code that has unexpected consequences on a downstream system, resulting in a technical outage that generates payment delays and sequential SLA breaches. Really understanding what is happening to orders, trades or payments as they move across systems and processes as it is happening can help firms overcome these types of challenges, as they are better equip to predict an issue’s emergence before it happens and take steps to avoid it.

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By employing technologies, able to comprehend how complex business processes should be working, and monitoring behavioural trends, the development of unexpected events can be detected earlier than they would have otherwise been. This actionable insight gives engineers a head start on remedying the situation so developing issues can be more proactively controlled. For the firms operating at this level, the derived business value really starts to ramp up. It is no longer about systems, networks and abstract pieces of data, it’s fundamentally about tracking data as it moves across business processes, in its business context. We are now starting to explore more advanced solutions that allow firms to continuously assess in real-time what is happening to the order and payment transactions their clients have trusted them to process. It’s now all about business flow transaction tracking.

Stage 4 Service Value: Assessing quality in its business context Once firms understand how data should be flowing across business processes and are able to decode all of the data’s content, it is possible to start examining the data’s quality in the context of the process it is traversing. In doing so, issues impacting the data’s expected timeliness, its correctness or completeness can be quickly evaluated.

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Measuring data quality compared to expected levels of performance can reveal powerful, actionable insights that enable firms to take steps so they can more proactively manage their SLA commitments. Here are just some examples of the different ways in which data quality can be assessed compared to expected norms: Timeliness

• Is the market data being used to formulate equity trading decisions ticking as fast as expected?

• As an FX broker, are your FIX engines responding to client requests for quotes and requests to trade in a timely manner?

• Have all payments due to shortly reach their cut-off time completed a sufficient number of processing stages for this to be possible?

Correctness • Are the price movements between data ticks, what you would reasonably

expect? • Is there suspect information included within a market data tick? • Has a payment been halted by a system because information is missing and as

such, the payment will require manual assistance before it can proceed? Completeness

• Are your FIX engines receiving all individual pricing feeds that you would expect from your liquidity providers?

• Are you receiving market data ticks from all matching engines, even though overall the market appears to be ticking normally?

• Has the end-to-end business process for a particular trade completed successfully?

Measuring data quality and being alerted to divergences from the norm, can have a positive impact on a trading firm’s profitability levels. Take for instance, the ability to detect quality issues impacting the data used to formulate algorithmic trading decisions. In a fast market, the failure to do so could very quickly result in the execution of multiple loss making trades. Operating at this mature level enables the quality of every single of piece of data to be assessed in detail, so even very subtle issues, that could still have a big profitability impact, can be detected. Whilst monitoring at an infrastructure level may enable a firm to detect if a consolidated market data feed is not being received, this type of business flow tracking will detect if a particular market or instrument may be missing, whilst all other elements appear to be ticking as normal in an active consolidated feed. By measuring current quality metrics against trends over time, client SLA’s and regulatory commitments, firms can more accurately assess whether they are meeting their obligations on an on-going basis.

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Developments are now taking place to further automate this process through machine learning. Advanced solutions will be able to learn what is “normal” so the tracking tool can independently determine how many times per second for example, a particular instrument should be ticking, at a given time, on a given day, and then detect when the ticking pattern is abnormal. Or for example whether a specific instrument’s tick-on-tick price movement is within normal parameters. Progress in this area offers the potential for significant savings at both the initial implementation stage and longer-term as this information needs to be maintained and currently doing so is a very manual and time-consuming task. Especially when you consider a trading firm may receive feeds for thousands of instruments.

Stage 5 Business and client value: Driving improvements Business flow tracking solutions operating at this upper stage effectively bring together all of the information collected by the lower stages to provide a cohesive, intuitive approach. Solutions that do this using a common platform, versus attempting to integrate multiple point solutions, are able to generate the greatest value as everything is

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completely synched up. In doing so, advanced solutions are not only able to explain what is happening, as it is happening across business processes, measure performance and the quality of service being delivered against SLA commitments, but also actively drive business and client value improvements. In effect, it’s the difference between employing a solution that will assess whether SLAs are being met and one that increases the likelihood of meeting these obligations by facilitating predictive analytics and delivering actionable insights.

Avoiding an SLA breach Sophisticated solutions enable firms to proactively avoid SLA breaches. Consider, for example, a solution that understands all of the possible processing paths a payment may take and if a payment is not received by a downstream system within an acceptable timeframe, will raise an alert. Or a system that will raise an alert, if payments approaching their cut-off times haven’t progressed through a sufficient number of stages for this to be achievable without manual intervention. These solutions require an understanding of the business process, the data’s content and its timeliness compared to past trends and current evidence. By assessing all of these factors, firms operating solutions of this calibre can produce predictive analytics so they can be notified in advance of payments that are likely to miss their SLA commitments unless they are quickly investigated. Acting on these advanced warnings can slash the number of SLA breaches and associated fines a financial institution incurs.

Avoiding an inevitable breach unnecessarily impacting clients Operating in this upper league also enables firms to more effectively manage how clients are impacted by an unavoidable SLA breach. An impacted client’s perception of how well a situation is managed can influence whether they choose to place future business with the firm or recommend its services to others. Therefore, being able to more effectively control these situations can be an invaluable tool in driving improved client experience, retention and profitability levels. It’s the difference between a firm having a client SLA that they know they are about to breach and being able to access the actionable insight necessary to proactively advise clients of this, versus just dealing with an impacted and possibly angry client once the breach has occurred. If the client is notified in advance, they are then given the opportunity to potentially take a different course of action. Tracking and analysing business events at this advanced stage, makes this possible. It’s about being able to aggregate information to generate accurate predictions, for example bringing together:

• What a firm knows about its client’s behavioural trends • How the firm’s systems have historically performed under different loads

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• And what today’s volumes are looking like in comparison to these performance levels

The firm can then use this information to accurately predict if they are likely to breach a client SLA due to expected capacity challenges. With this powerful actionable insight, clients with a high probability of being impacted can be notified in advance. With this information the client can take preventative steps to avoid being further impacted and for example, process a payroll instruction, which they periodically submit on the last Friday of the month, through an alternative channel. In doing so, the bank can help its clients to proactively manage the problems their inability to meet an SLA commitment generates.

Proactively managing a client’s experience when impacted by a breach A significant advantage of more sophisticated tracking solutions is that they provide oversight across all business processes and enable the same connected data set to be analysed by different users from multiple perspectives. Ideally offering a common lexicon so these different teams can effectively communicate issues between themselves and ultimately speed up the incident’s time to resolution. The ability to provide different users with these perspectives is vital if sophisticated solutions are to provide value to all of the business and technical teams involved in resolving an issue, quantifying its business impact, managing the experience of impacted clients and bringing the situation back to BAU. So when an outage occurs, for instance, technical management, application groups, operational staff and client support teams can all access the exact information they require. Therefore, more sophisticated business flow tracking tools should enable:

• Technical and application groups to quickly identify an issue has occurred and where it is located, in addition to providing the forensic analysis tools necessary to determine the root cause. With this information these teams can then focus on resolving the problem

• Operational staff to swiftly assess the business impact, so they can focus their time on providing the right level of communication to different business areas

• Client servicing teams to determine exactly which clients have been impacted and how. This allows more time to be spent working with affected clients

At each of these stages an intuitive tool will automate tasks that if performed manually, or required information to be consolidated from multiple point solutions, would take much longer. The longer an incident takes to resolve, the greater the resource, business and client management costs it generates. By automating these processes, the time saved can be used more profitably as resources can focus on problem solving, communicating the issue’s effects across the business and working with impacted clients.

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Future gazing

Once a firm is benefiting from an advanced solution, that enables them to accurately track everything happening in their environment, and use the information generated to drive improved business and client value – where next? Well, as financial services becomes more transparent and the availability of information on-demand becomes the norm, some firms are starting to look at how they can more directly provide clients with a real-time understanding of how their transactions are being processed. Furthermore, now a firm can track trades and payments as they move across their complete environment it’s likely there will also be a desire to extend this visibility beyond their own boundaries to their counterparties. So, for example, if a payment is sent to a correspondent bank for its next stage of processing, it will be about being able to track that payment as it leaves bank A, enters the gateway at bank B and then moves across their processes. In essence, in a very similar way to tracking an on-line purchase where you can see your item has gone through all of the stages necessary for it to leave the shop’s warehouse, has made it to the courier’s depot and is currently out for delivery. Whilst this level of visibility would be incredibly valuable in real-time, it is also beneficial from a post-trade analysis perspective as it can be used to identify areas for future focus and improvement. The ability for trading firms to gain a more detailed understanding of what has happened to their trades once they have left their gateway and been sent to an exchange will be much assisted by the requirements in MiFID II. This is because trading venues, their members and participants will all need to synchronise the business clocks used to record the time and date of reportable events to coordinated universal time (UTC). This means that a trading firm will be able to more accurately break down how long a message takes to travel from them, to the exchange and back again. With this insight the firm could for instance:

• More effectively detect if a certain venue is under stress and take appropriate trading decisions

• Conduct more informed conversations with the trading venue about whether they are, for example, accessing the exchange using the right gateway for their trading requirements

• Furthermore, if the firm uses a third party connectivity partner to link up to the exchange, a timestamp detailing when an order reached the venue’s gateway could provide more information on that connection, indicating if there is a network, connectivity or application issue.

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Where does your firm lie on the monitoring spectrum?

In summary, the sophisticated business flow tracking and analysis solutions now available to industry participants deliver exponential benefits compared to the previously more basic monitoring options offered. Over the past 12 months, there has been a shift in the way in which the industry is starting to think about monitoring and tracking payments and trades. Keen to overcome the challenges presented by attempting to bring together data captured by multiple point solutions, some firms are now looking for tools that will provide complete end-to-end visibility and analysis. So they can track payments or trades from their point of inception through to settlement, and derive considerably more business and client value from the information gathered at every step along the way. These firms are in effect looking for an extensible monitoring and tracking fabric that they can roll out across different business areas. A solution that will analyse:

• What is happening at an infrastructure level • How payments and trades are traversing their processes • The quality of the data being used to formulate business decisions • And correlate all of this information together to drive predictive analytics and

actionable insights. Ultimately delivering real business and client value The industry’s innovators have seen for themselves how taking this new approach has delivered firm wide benefit. Now the technology has been proven, and the advantages are becoming more widely understood, we’re increasingly talking to and implementing solutions for early adopters. These firms are looking at how they can:

• Cut unnecessary costs • Increase the profitability of their payment processing and trading operations • Effectively meet the needs of multiple regulatory requirements • Improve client experience • And address emerging client needs

Ultimately, monitoring is no longer a binary question. It’s not whether you have it or you don’t. It’s also not just about ticking a compliance box. It’s more a question of where on the value spectrum you want to be and where in reality your business currently sits? If this doesn’t match your business’s aspirations, it’s then about identifying how you are going to get it where it needs to be?

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About the author

Steve Colwill is the Founder and CEO of Velocimetrics. Steve has over 25 years of financial services experience. He is a software architect and senior manager, and spent 15 years of his career building high volume, real-time FX, fixed income and equity pricing, risk and trading systems for a number of tier 1 investment banks. For over 4 years he held the position of CTO at Iris Financial, during this time he gained experience of managing R&D and service teams of 60+ people, while driving the complete redevelopment of the product from the bottom up. Prior to which he held positions at Dresdner Kleinwort Benson (now Commerzbank), Investment intelligent Systems Corp and Siemens.

About Velocimetrics

Velocimetrics sets a new benchmark for operational oversight, delivering uncompromised end-to-end visibility across complex environments, accompanied by performance improving analytics. Its full-spectrum product suite enables emerging problems to be instantly detected and their root-cause rapidly understood from both a business and technical perspective. This significantly reduces an issue’s potential impact, whilst also identifying opportunities for on-going performance improvements. Providing asset class agnostic, highly customisable, agile, open and globally scalable solutions, Velocimetrics prioritises flexibility so it can build the solution that will effectively meet your firm’s specific requirements. Formed in 2009, Velocimetrics’ world-class financial services expertise continues to attract a growing global client base. Its sophisticated solutions deliver the level of transparency required to instil confidence and its innovative approach demonstrates the future potential for business flow tracking and performance analysis tools. For more information, please email [email protected] or visit: www.velocimetrics.com

beyond monitoring where is your solution on the business ......solutions enable firms to immediately...

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