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How to cut through the data noise and leverage actionable security and threat intelligence

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By Kristofer Mansson, CEO and co-founder, Silobreaker

 

When it comes to the power of good intelligence, it’s important to first make a distinction: data is not intelligence. Data is the raw material that needs refining before it can become a finished product – the intelligence that can prompt actions that keep businesses safe and secure.

Data can typically come in four distinct forms: open-source data, available from the internet most of us use; underground data, which can only be found on places such as the Dark Web; premium data, specifically curated and often only accessible behind paywalls; finally, there is internal data, often purchased or produced by organisations themselves.

It is fair to say we live in an age in which we are ‘drowning’ in data, particularly when you consider that the volume of data worldwide has grown by almost 5,000 percent between 2010-22. But how do we make sense of all this data, and why does good intelligence matter to businesses today?

Understanding the intelligence cycle

In the early-2000s, having the greatest amount of data gave businesses an advantage over others, but as the volume of data has grown, its unit value has decreased. It’s now less about the quantity of data organisations have but more about how to best use it. But in order to make sense of the overwhelming amount of data at their disposal, businesses need to be able to find what matters and learn from it in a meaningful way.

Kristofer Mansson

To do so, it is helpful to follow a basic intelligence cycle that consists of five distinct stages:

  1. The company considers what intelligence they need and why
  2. Data collection is undertaken from all or a subset of the sources mentioned above
  3. Data is processed by algorithms and other technology to read, contextualise and make sense of it
  4. Analysts examine the processed data and use tools to analyse it and produce the required intelligence
  5. The intelligence can now be disseminated back to the relevant teams and decision makers.

Once the intelligence is disseminated, key stakeholders can action it and use it to support their decision-making, as well as set new requirements for the next cycle. Of all the stages, processing of data is where technology plays the most critical role. Context and knowledge at speed is key for any intelligence operation, and humans are incapable of sifting through, reading and processing the amount of data available today.

Without the right perspective on threats and opportunities, a business’s decision-making abilities are significantly compromised, which can ultimately leave an organisation unnecessarily vulnerable.

Breaking down the intelligence silos

For security and threat intelligence to be the most effective, another key approach of is to break down traditional intelligence silos. To understand the threat landscape facing an organisation requires awareness of how intertwined threats and risks are across the cyber, physical, strategic and political spectrums. Data, systems, processes and teams and their workflows must reflect this new way of thinking.

It’s important to view the world of intelligence as more of a Venn diagram where cyber, physical, strategic and political intelligence all coexist with overlapping parts. For example, while an election may seemingly have little to do with cyber or physical risk on the surface, the leadup and outcome is likely to trigger phishing campaigns and other cyber activity from hostile actors, or spark a protest that could result in physical damage. As a result, what started in the political sphere can directly lead to harm in the cyber world, as well as the physical one.

For instance, a US-based bank with over 10,000 employees was initially concerned about physical security threats impacting their locations, ranging from crime and extreme weather conditions to fire and other potentially disruptive events. As a result, the bank turned to a centralised platform to gather the physical threat intelligence in an effective and timely manner, with information on everything from travel advisories and weather warnings to news reports and on-the-ground tweets. Once the security team saw the insights gained from physical threat alerts, they decided to expand the usage of the tool across cyber and other use-cases of the bank to help senior leadership make even more informed and timely decisions.

When it comes to cutting through the data noise, there is tremendous value in breaking down intelligence silos and embracing a more holistic and multi-disciplinary approach. With the right planning and perspective, businesses can be best prepared to predict, protect and respond to the broad range of risks and threats facing them today.

 

Business

How Big Data is Transforming Bilateral Trading

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By Stuart Smith, Co-Head Business Development – Data & Risk

 

Since its inception, Big Data has been an important part of how firms have identified and constructed quantitative trading strategies with hedge funds depending more on quant strategies which rely heavily on big data driven analytics.

As big data technology continues to move from being a specialised technical capability to being a commoditised capability available on a range of easily consumed technology platforms, its use within the financial derivatives will continue to increase beyond the initial quantitative driven capabilities.

At the same time, the number and range of available data sources is increasing rapidly. Whether it’s the increase in alternative data sets or new technology enabling firms to simply keep more of the data they have been creating, the volume of data available is increasing dramatically.

 

Big Data in Risk Management

Risk Management has always had requirements which have driven a close collaboration between business and technology to make available risk analytics useful for the business to make better decisions. As technology becomes more advanced, the metrics available continue to improve as well. This is typically because many risk metrics require high numbers of scenarios and valuations to correctly identify risks in multiple scenarios. To maintain flexibility, this has led to an explosion of data to manage. Firms are increasingly keeping all this data available which can run into many Terabytes (TBs), much of which needs to be ‘In Memory’ to make it accessible to analysts.

Stuart Smith

To achieve this big-data, technology is critical to allow firms to move large volumes of data quickly and easily from affordable long-term storage into high performance in-memory analytics. Big Data technology is ideal for this type of problem to enable large volumes of data to be recalled from across multiple stores and appropriately aggregated or filtered based on the analysis which users are requesting. Whereas in the past, analysts would have to accept that data outside of the last 3-5 days is only available in a summarised format, they can now expect that the data can be re-hydrated quickly and easily from cloud data stores and available to them in an easy-to-consume web interface.

This can enable much more dynamic types of analysis, for example where a new risk is identified, through analysis of a recent data set it’s now possible to find a long history of that risk, whereas previously it would have been lost through summarisation and fixed reporting processes.

 

Collaborative Data Sets

More big data stores are being created as the industry becomes more collaborative and uses increasing numbers of fintech solutions and platforms. With this change come new ways to analyse data and provide new insights.

For instance, through the automation of collateral exchange, an historical store of margin calls, payments and disputes has been created. This history provides a resource for banks to understand their performance in accurately issuing and making margin calls based on derivatives and compare their performance to that of the industry as a whole. The example below shows how a firm can be benchmarked while holding other institutions data private.

These types of analysis are new and could not be delivered without the centralised collaborative data model. It can prove to be instrumental in improving firms’ overall operational efficiency and client service.

It also provides an opportunity for Machine Learning techniques, based on big data sets, to analyse and predict payments requests which are likely to be disputed and potentially identify causes before an actual dispute is even raised. This type of ‘self-healing’ process can only be enabled by a large history of data through which algorithms can be trained.

In the case of Initial Margin (IM) calculated by ISDA SIMM* a new set of challenges have been introduced through having a two-sided risk calculation as part of the process of deriving payment information. This adds another level of complexity to the resolving of disputes; however, the potential offered by having large volumes of data opens up new options on how this challenge could be solved. The long history of Common Risk Interchange Format (CRIF)** data provides a long-term view of the sensitivities for most OTC derivatives, which can enable firms to identify basic issues like stale market data day over day. However, as with most detailed analysis differences in models, they can also be identified through looking at differences over long periods of time. Identification of these types of model discrepancies can help firms to be more proactive about reviewing their modelling deficiencies to ensure that differences don’t lead to disputes.

 

Looking ahead

The sheer volume of data can be an industry-wide challenge with firms having to manage disparate, needlessly duplicated and ultimately overwhelming information. Creation of an industry standard for reporting and analytics is, therefore, crucial to enable firms get clarity and valuable insights from the masses of data and centralise the information as a single data layer. Acadia has designed Data Exploration (DX) suite to be one-of-its-kind big data analytics platform to help sell-side, buy-side and fund administrators see its market positioning, trends and analysis of industrywide metrics.

The impact of big data will only grow and the industry is left with no choice than to evolve the use of technology, whether that is to drive quant strategies for hedge funds, more dynamic forms of risk management or larger shared industry data sets. All of these applications rely on underlying big data technology platforms to provide distributed analysis capabilities. As these capabilities continue to develop so will the types of analysis which are available to firms.

*The ISDA Standard Initial Margin Model (ISDA SIMM™) is a common methodology for calculating initial margin for non-centrally cleared derivatives, developed as part of ISDA’s Working Group on Margin Requirements (WGMR) to help market participants meet the BCBS-IOSCO margin framework for non-cleared derivatives.

** The CRIF file (Common Risk Interchange Format) is the industry template used to hold and exchange sensitivity data. ISDA’s calculation specifications are used to produce Delta, Vega and Curvature sensitivity numbers at Risk Factor-level

 

 

 

 

 

 

 

 

 

 

 

 

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Banking

Three tips to help banks profit from the rise of managed services

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By Chris Mills, Global Head of Managed Services Sales, Finastra

Research from IDC finds that only 29% of banks claim to have a long-term, strategic digital transformation plan in place, despite results showing firms that had invested in transformation saw improvements of 27% in reducing risk, 27% in innovation and 26% in improved customer satisfaction. The days when banks’ IT teams operated in isolation of business goals should be very old news. Effective CEOs build digital transformation into their strategies from the start, and the most successful CTOs understand how to apply technology to achieve business success.

In many ways, CTOs have become more like orchestrators or conductors than individual instrumentalists. They need everybody on their team to work in concert to deliver value according to desired business outcomes. It’s less about building IT from scratch and more about assembling components and making sure that they operate smoothly and cost-effectively.

Chris Mills

One of the most striking findings is that 40% of financial institutions said that the pandemic meant they had to accelerate and increase all of their digital-first initiatives. They had to innovate to remain viable and competitive. It’s also clear that there is no longer just one, singular path of IT delivery. Instead, CTOs are facing multi-threaded challenges. It means CTOs must consider many different deliverables and leverage all the resources at their disposal, including internal and external partners.

Changing customer expectations

The financial services sector was facing a range of external challenges even before the pandemic arrived. For example, from a consumer’s perspective, the exponential advancement of a smartphone’s technological capabilities in recent years has increased their expectations for new updates and improvements. This behavioural change has impacted customer decision-making and they now expect a high level of service and responsiveness, whether they are customers of a retail or a corporate bank.

The banking industry also faces regulatory, compliance, resilience, and sustainability issues. As ESG agendas become an increasingly important priority for financial institutions, pushed by the rise of net-zero targets, CTOs must respond to these demands, and that’s why they see innovation as such a key focus.

But how can financial institutions that are late to the digital transformation party use technology to capture competitiveness and improve responsiveness for their clients?

One approach that has proved successful is managed services, which is a term used to capture the blending of services, product, and functional capabilities. When CTOs consider this option, they need to start by thinking about the business outcomes with the associated technical and functional expertise they need.

This includes the business uptime that is required, scalability and deployment speed. Does the bank need to roll out capabilities across the globe, and does it need to serve only the main financial markets, or emerging markets too?

Another question CTOs must consider is choosing what service partner to work with. Large system integrators have been providing these services for a long time, but a software partner like Finastra has advantages in terms of product proximity.

Service providers must offer tailored products focusing on the needs of its clients. Offering quality software allows banks to achieve their long-term strategic outcomes.

It’s important to look at all areas of a banks’ business, For example, what does the payments team need?

What does the head of lending need? What does the head of treasury need in order to grow their business over the next five years?

With that in mind, I offer three tips to banks when considering managed services.

1. Be very clear about what your business outcomes need to be. Really drill down into KPIs and metrics that we can look at to ensure we provide the service your bank demands. This can range from resiliency, compliance, regulation or even functionality and capabilities – such as how often you require upgrades.

2. Measure and assess your own resources, skills and capabilities. Understand where you want to draw the line between the responsibilities you would want a service partner to take on and what you want to retain. There shouldn’t be any grey areas. You want a clearly-defined line where responsibilities lie, so that everyone is very clear about who’s doing what and how KPIs and service levels will be met.

3. Be prepared to develop a long-term strategic partnership, over five or 10 years. We expect hard questions, and you should be expecting them back – ultimately that’s how good relationships and partnerships work.

As IDC writes in its report ‘New service models to accelerate innovation in banking’ these holistic and software-led models require banks to master a set of new skills, including governance and partner management. Service partners should be industry-savvy, should supply end-to-end expertise, and should be aligned to support the financial institution’s business goals, not just technical KPIs.

Digital transformation infrastructure management requires CTOs to act as a conductor, rather than a solo performer.

 

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