Machine learning and associated algorithms are making real waves not just in banks’ front offices but also in analysing and spotting trading opportunities in the stock markets.

Machine learning (ML) is being used to identify trading patterns, initially in historical trade and quote data. This may sound familiar to those who are cognisant of technical analysis and the end goal is indeed the same, that is, to find useful patterns in historical and even real time data that lead to decisions that may result in profitable trades either long or short.

According to Tom Finke, head of machine learning product management at software and data provider OneMarketData: “What’s different now is that the techniques have evolved in performing that analysis. In particular, we are able now to use machine learning algorithms to help improve some of the more historical sorts of algorithms that were used to try to detect patterns. When we train machine learning models, such as neural network models, they are able to find patterns that traditional analyses like regression analyses might not otherwise find. These sorts of algorithms are able to find patterns that mere humans are not able to find because of the extent of the vastness of the data that can be analysed.”

An arms race?

Of course, if every trader was to follow the same analytical signals they would all be doing the same thing at the same time. Finke admits that funds, brokers, trading firms, banks and asset managers are facing a “sort of arms race”! He added: If you don’t participate, there is a risk, you’ll indeed be left behind. It would be advisable for investment firms and investment funds that want to stay on top of things that they really should form teams to at least investigate how machine learning can help with their investment and trading decisions.”

But the machines are not completely taking over, well not yet. “It takes some human ingenuity and cleverness, to decide the parameters around those machine learning algorithms. For example, what is the most appropriate data set on which to build a machine learning model?”

Right now, the human element is still required. It takes a person to decide which ML algorithm should be used and what pool of data should be analysed. However, there are companies working on this with ML algorithms being developed with the aim of having them choose which are the best ML algorithms to use.

Watching the markets move

With algorithms being used to analyse trading patterns it should come as no surprise that one way this is being leveraged is in market surveillance. OneMarketData’s core product is a time series tick level database on which that company had built various vertical applications – one of the most popular being trade surveillance.

“We have that particular application being used by a major exchange in the US and by quite a few investment banks. They’re analysing order books; some historically and a few in real time to try to detect patterns that are nefarious such as spoofing  or layering [both forms of illicit manipulation in which a trader may attempt to deceive others regarding the true level of supply/demand for a given financial instrument]. Traditionally there are patterns that you can look for and detect to try to find these activities and now we are in the early stages of applying machine learning algorithms to that,” said Finke.

One thing that has changed in the financial markets in the last few decades is the sheer volume of data and number of trades. Finke noted that in the last week of February 2020 when the world’s financial markets became seriously ‘spooked’ by  concerns over the global Coronavirus (COVID-19) outbreak, the number of ticks during the volatility was such that “some of our customers were running out of memory in their memory databases”.

Seeking the right signal

ML and the appropriate algorithms are capable of analysing more than just price data. For example, Bloomberg now provide a machine-readable news feed that is tagged to make it easier for computer software to parse the text.

This parsed news may then be stored in the same way as trader quote ticks. Run it through a natural language processing algorithm to parse it into meaningful chunks (a technical term!) and then as stage two use another ML algorithm to decide whether there is enough information to provide a trading signal… and if there is, what should that signal be?

Such algorithms are being developed with the analysis of historical data but once the models are trained, they can be applied to real time streaming news data to try to generate real time trading signals. Sure-fire success in such endeavours is by no means guaranteed. “This still a hard problem. It’s hard for humans, it’s hard for anybody to pull meaningful market sentiment out of newsfeeds. We’re still in the early stages of having any sort of effective results, but that’s certainly not stopping people from making the attempt,” concluded Finke.

The Catalyst for Change

Billed as a game changer by most in the industry, Open Banking witnessed a managed roll out in the UK in April 2018, paving the way for customers to experience enhanced banking services through a variety of authorised providers. The Competition and Markets Authority ushered in Open Banking with the aim to improve the quality of banking and financial services, ensuring banks remain customer-oriented in an extremely competitive market.

Optimistic market forecasts estimate that Open Banking could generate more than £7.2bn by 2022 if various sectors tap into its massive potential.

Open Banking allows secure data sharing by using an integration technology called Application Programming Interface (‘API’) that accesses the account and transaction information of customers and even allows third party providers (‘TPPs’) to initiate payment on behalf of customers, only upon their explicit approval.

As we move into 2019, what has actually changed and what lessons can we learn? Has this ‘great disruptor’ in the banking sector lived up to its initial hype?

A Closed Mind to Open Banking

The CMA reported that in June, there were 1.2 million uses of Open Banking APIs, describing it as a slow but positive start to changing consumer attitudes and revitalising the banking ecosystem for the better.

However, one senior source at a financial technology company told The Daily Telegraph: “The lack of promotion by the big banks has been disappointing and it’s the main reason for the slow take-up”.

So what are the reasons for the slow start? Why are the big banks taking their time?

Anne Boden, CEO and founder of Starling Bank, has been quoted as saying that the big banks “are all using legacy technology that’s 20, 30 or 40 years old… there’s no commercial reason why they want to do it [Open Banking]. Without that it’s a very difficult thing to do.”

Though public sentiment towards Open Banking is far from effusive, do remember it is a complex change that will take time to transform the way banking is done. Open Banking inherently brings a raft of technological and economic risks for the traditional banking model and navigating those changes is going to be an uphill task. One of the biggest teething problems faced in the banking sector is the legacy technology that is still used in the major banks, preventing them from quickly benefiting from this ambitious regulatory-driven process. In some instances, the technology could be even thirty or forty years old. The cost of overhauling their legacy technology to allow integration with API is prohibitively high, adding further traction to the process of adoption. However, if banks and financial organisations are eager to monetise the myriad opportunities presented by Open Banking, they need to be quick about overhauling their systems and IT infrastructure. Further, they also need to constantly innovate and bring out banking apps and other technology-driven solutions to enhance the banking experience for their customers.

Though the CMA provides guidelines on security measures and details of regulated providers, it still fails to address the underlying issues of legacy technology to ensure that there is no loss in the transfer of customer data.

Driving the Change

Banks own valuable customer data and are fiercely protective of it. Also, consumers who are not familiar with the actual applications of Open Banking are reluctant to embrace it as they fear fraudulent transactions and other complications arising from this technology. Adding to this hurdle is also the lack of awareness of the risks and benefits associated with Open Banking that has limited its appeal among the masses.

Therefore, the challenge for the banking sector is in implementing these concepts on the ground. Any compromise on customer data will not only result in regulatory penalties but also in the damaging press. No wonder then that cyber and data security rank amongst the top priorities of every Bank CIO and CEO.

Since Open Banking requires banks to share detailed customer information (other than sensitive payment data), they are required to undertake due diligence while sharing the same, even under the express consent of the customer. Banks and TPPs need to ensure customer consent is taken with due emphasis on the customer’s ability to understand and appreciate the possible outcome from the provision of their data. Since banks are deemed to be the final custodian of customer information, they have to secure their systems against financial crime, fraud detection and AML, among other things. Further, a bank’s IT infrastructure will need to be more secure and resilient as it will now be exposed to threats ported through TPP systems. They have to invest more effort and energy to analyse and discover potential points of vulnerability and take adequate measures to address this holistically. Core banking systems need to adopt open API based peripheral development, delivering quicker implementation cycles and minimal customisation of the core product. Furthermore, the industry’s adoption of API standards should set a benchmark for all involved parties. Banks and TPPs should adhere to and promote development in line with these standards.

Finally, it is worth mentioning that many large payment systems and core banking providers have developed Open Banking-compliant solutions. Without going into a lengthy debate on the merits and demerits of each of them, it might suffice to recognise that these systems, along with robust identity and access management systems, can comprise a strong first line of defence for the Open Banking ecosystem.

The Best Has Yet to Come

While the consumer experience may not have altered significantly in the initial rollout of Open Banking, experts opine that it won’t be long before the positive effects of this innovative model trickle down to the end users.

Already, the market is charged with competition and has become riper for innovation. Positive changes are taking place internally and banks are strategising to become more customer-centric and proactive. This will bode well for the long-term relationships banks have with their customers. As we gear up for the next wave of Open Banking, we hope that its innovative model will lead to a level playing field for both customers and banks. For once, innovation will go hand in hand with pragmatism and plain grit, to script the winning equation for the future of banking.

By Shuvo G. Roy, Vice President & Head – Banking Solutions (EMEA), Mphasis

We have come a long way from the first commercial use of Oculus Rift VR headset 0f 2013. Yet, most people associate the technology of Augmented Reality (AR) and Virtual Reality (VR) with the realm of gaming. However, many industries including marketing, healthcare, real-estate are accepting the immense potential of VR to improve their business. A report by Goldman Sachs group estimates the virtual and augmented reality to become an $80 billion market by 2025.

Even financial institutions like the banks are well aware of this conundrum, and many firms are aggressively experimenting with the new coming technology to enhance customer experience (CX). From basic apps that use customer location to help locate ATM branches nearby to promoting banking solutions in an engaging 3D environment. Some financial institutions are using it as a marketing tool, others are using AR to offer customer-centric apps that display real-time cost and other information associated with properties which are up for sale, offer a mortgage calculator and more.

According to a study, ‘AR/VR can transform financial data into a visual, engaging experience and can eventually bring the face-to-face experience into a customer’s home’. The possibility of hybrid branches is also in the pipeline where physical branches use AR technology to offer self-service like chatbots, or robots to provide information. If required, customers can also connect to an actual bank-representative via video conferences.

All things said and done, the idea of banking in virtual reality is still half-baked and the road to reach that reality is daunting and surrounded by skepticism about the possibilities of virtual banking. Nonetheless, there are a few corners in the financial sector where VR and AR have already made an impact:

Immersive Experience through Data Visualization

The financial industry has a lot riding on analyzing large amounts of data on a day to day basis. Data visualization helps financial traders and advisors to get a visual breakdown of the copious amount of data and make informed decisions about wealth management. Using the modern technology of VR and AR, data visualization is quicker and easier than ever before.

Remember we spoke about Oculus Rift earlier? Fidelity labs used the technology behind the Oculus Rift to create an immersive 3D environment to analyze data accurately. They created a virtual world where people can talk to financial advisors in virtual reality to learn about the progress of their stock portfolios. Their VR assistant, Cora, will display the stock chart on a wall of her virtual office just like presenting graph on a virtual projector.

Virtual Trading Workshops

Some financial institutions are using VR to create virtual trading workshops. In April 2017, FlexTrade Systems announced the launch of ‘FlexAR’ – a virtual reality trading application that uses Microsoft HoloLens to offer an extraordinary way of visualizing and presenting trading. It uses components from the real world and allows traders to see and interact with the markets and identify the holistic patterns in the trading environment.

Virtual Reality Shopping Experience

Taking customer and shopping experience to the next level, in 2017, MasterCard and Swarovski launched a VR shopping app that allows consumers to browse and purchase items from Atelier Swarovski home décor line and immerse into a complete virtual shopping experience. They can use Masterpass, MasterCard’s digital payment service to make payments.

Security

With biometrics as part of the AR experience, financial services can offer more secure and substantial protection against cybercrime. A number of banking applications already offer fingerprint authentication for many smartphones. With AR, iris identification and voice recognition, are being introduced as well. In 2018, Axis Bank became India’s first bank to introduce Iris Scan Authentication feature for Aadhaar-based transactions at its micro-ATM tablets.

Possibilities of Virtual Branches

As more and more financial service providers are incrementally moving towards digitized banking, the idea of a virtual bank doesn’t seem too far-fetched. Imagine never having to take a break during working hours and wait in a line at the bank. Now imagine, getting the personalized banking service at the comfort of your home, when it’s convenient for you while enjoying a cup of coffee. That’s what virtual branches have to offer. To aid customer demand for contact anytime, financial institutions are already offering services like Chatbots and are developing solutions to provide banking solutions exclusively in a VR environment. This would be a win-win for both- customers will get their service anytime, anywhere and banks will be able to reduce costs as they will not need to invest in physical locations.

Living in today’s high-tech world, we all know that technology is something that has been and will keep on evolving. With each day passing, reality adjacent technologies like VR and AR are becoming mainstream, and already impacting the way financial institutions operate, manage data, interact with customers and more.

There is no doubt that the financial industry will need to integrate this new science into banking operations. Not only will this help them attract and retain customers, enrich the customer’s user experience (UX) but also help in operational cost reduction. Failing to do so, their customers are most likely to move toward non-financial institutions that offer ease of use and flexible services that they demand.

By Vikram Bhagvan, Associate Vice President, Business Operation, Maveric Systems Limited

At UK FinTech Week 2019, Secure Chorus brought to the stage a powerhouse of thought leaders in the field of post-quantum cryptography from UK government, industry and academia. The speakers discussed the quantum threats considered to be the next undefended frontier of cybersecurity and the significance of the problem for the finance industry.

Quantum-related technologies have the potential to massively disrupt the finance industry in algorithmic trading, fraud detection, encryption and transaction security. And yet, with these opportunities also come information security threats, as current encryption methods become simpler to break. Because organisations within the finance industry process and archive sensitive data over long time-frames (up to a decade or more), it is becoming clear that this industry needs to start upgrading all critical infrastructure to be quantum safe.

This was the theme of our recent Thought Leadership Platform addressing the finance industry at the UK FinTech Week 2019. Entitled “Quantum-Safe Finance: Preparing for the Storm”, the event was joined by government, industry and academic experts to discuss quantum threats for the financial sector. Speakers included experts from the UK National Cyber Security Centre (NCSC), ISARA Corporation, Post-Quantum and the Centre for Secure Information Technologies (CSIT).

The massive processing power that will be unlocked by quantum computers will make the public key cryptography we are using today vulnerable. This could bring on-line e-commerce and banking fraud to a systemic breach-type scenario. Blockchain-based technologies that rely on the Elliptic Curve Digital Signature Algorithm (ECDSA) would also not be ‘quantum safe’, exposing the burgeoning cryptocurrency markets to cyber risks.

The vision statement for our Thought Leadership Platform was to raise awareness on the need for greater cooperation between governments, industry and academia to develop successful quantum-safe initiatives.

The market has seen rising investment and excitement surrounding transformational opportunities created by quantum computing. However, the significant threat to our global information infrastructure posed by large-scale quantum computing has greatly overshadowed by it.

Our panel spoke about the design of quantum computers drawing upon very different scientific concepts from those used in today’s conventional or ‘classic’ computers. This could eventually enable them to factor large numbers relatively quickly, which means that they will potentially be able to significantly weaken the public key cryptography that has protected the majority of data to date.

Popular cryptographic schemes based on these hard problems – including RSA and Elliptic Curve Cryptography – will be easily broken by a quantum computer. This will rapidly accelerate the obsolescence of our currently deployed security systems, creating an unprecedented scale of the threat that will require a significant amount of time and resources to mitigate.

Without quantum-safe cryptography and security, all electronic information will become vulnerable to cyber attacks. It will no longer be possible to guarantee the integrity and authenticity of transmitted information. Importantly, encrypted data that is currently safe from cyber attacks can be stored for later decryption once quantum computers become available. From a legal perspective, these scenarios would mean a violation of regulatory requirements for data privacy and security that organisations are required to comply with.

This means there is now a pressing need to develop public key cryptography capable of resisting such quantum attacks. This can be achieved by developing post-quantum algorithms based on different mathematical tools that are resistant to both quantum and conventional cyber attacks.

Standards-setting bodies, including the US-based National Institute of Standards and Technology (NIST) as well as the European Telecommunications Standards Institute (ETSI), are currently in the processes of selecting the strongest cryptographic algorithms in a step towards standardising the relevant algorithms, primitives, and risk management practices as needed to seamlessly preserve our global information security infrastructure.

Of the various post-quantum cryptographic scheme candidates, lattice-based cryptographic schemes (LBC) have emerged as one of the most promising classes for standardisation. For three reasons: first, due to their efficiency and simplicity; second, due to their good security properties; and third, due to their manifestation into more complex security functions.

In order to make the transition from the security we use in the digital space today to a fully quantum-safe one, we need to fundamentally change the way we build our digital systems. We need technology solutions that bridge the gap between current cryptography and quantum-safe cryptography without causing a complete breakdown of systems because of one algorithm not being able to communicate with the other.

Standards help technologies speak the same language. However, the required standards won’t be ready for several more years. In the meantime, we need a path to quantum-safe security. One method of developing quantum-safe public key cryptography is the deployment of a new set of public key cryptosystems for classic computers that can resist quantum attack. These cryptosystems are called ‘quantum-safe’ or ‘post-quantum cryptography’. The principle behind them is the use of mathematical problems of a complexity beyond quantum computing’s ability to solve them. The key takeaway message from our Thought Leadership Platform was that there is a pressing need to start planning for the transition to quantum-safe systems. This is especially relevant in industries such as finance, due to the complexity of their systems that will require several years to be updated.

By Elisabetta Zaccaria, Chairman Secure Chorus

The business of payments – and payments technology – has transformed. In the pre-internet age, banks made money primarily from lending and deposits, supported by batch mainframe systems, with payments a minor sideshow. As electronic payments volumes started to take off in the early dot-com era, banks began to treat payments as a distinct business, driven by fee and transaction revenues. They packaged their offerings as monolithic, silo-ed financial products—and mirrored them with a complex silo-ed technology architecture.

The payment hub was originally conceived as a response to this complexity, to help banks eliminate processing silos and streamline their payments businesses. As we approach nearly twenty years since the first hubs were brought to market, it is a good time to evaluate whether hubs have delivered on that original promise.

Unfortunately, they have not. Many banks that made significant investments in hubs are still running legacy systems, with some institutions even having ended up with different hubs for different payment types, an architectural oxymoron. Many hubs have also proved unable to adapt to the challenges of real-time payments, always-on open banking, and the move to the cloud.

The stakes are high: today, payments generate over $1tn in revenue, with that amount, and transaction volumes, set to double over the next decade. If the traditional hub won’t allow banks to capitalize on this growth, then what will?

Before answering this question, let’s take a look at the trends that are shaping the payments industry, and how these are affecting the basic business model of banking.

Business and competitive environments are now very different from past decades. Competition is depressing fee revenue and rising payment volumes are driving up processing cost, eroding margins. Open banking is allowing challenger banks and non-bank service providers to disintermediate banks from their customers and is placing a premium on innovation and “fintech-like” agility from banks. With complexity in clearing and settlement growing and regulatory pressure mounting, banks are struggling more than ever to bring new payments services to market.

Most importantly, in this era of rapid transformation, both consumer and corporate customers want something different – they want their banking experiences to match the seamless, tailored real-time experiences they are accustomed to across social media, ecommerce and mobile applications. Services above and beyond traditional product offerings are in demand and, with brand loyalty declining, customers are more than happy to switch banks to obtain those experiences.

The combination of competitive pressure, technological change, and shifts in customer demand is forcing banks to change perspective and become much more customer-centric. They are viewing themselves as value-added service providers in a digital customer experience ecosystem, rather than purveyors of financial products. This altered perspective allows the answer to our original question to come into focus—the correct technological response to the transformational demands of business is to move away from monolithic payments applications and hubs glued together by middleware, to digital ecosystems.

A digital payments ecosystem consists of a number of independent components that interoperate easily and symbiotically allowing for rapid development of new business services. It is open; designed to support open banking interaction models, and API banking, with every function accessible as a service or microservice. It accommodates services from multiple third-party vendors – and banks. It is cloud-ready; operating in public, private or hybrid cloud models and able to mix and match where services and data run based on a bank’s deployment and data security requirements. It is inherently real-time and 24×7, unlike legacy hubs with real-time workflows grafted onto batch/RTGS scaffolding.  Lastly, it enables banks to own their roadmap – loosening vendor dependencies by eliminating the need to wait for vendor upgrades in order to release innovative new customer services and experiences.

Traditional payment hubs are dead, or dying – but new ecosystem-based payments technology approaches are ready to take over. Long live the next generation of hubs—the digital payments ecosystem!

Data is widely acknowledged to be one of the business’ most valuable assets. Yet even data can depreciate in value. Like currency itself, it is always changing and evolving with new types appearing. Just as the financial industry has witnessed the rise of alternative and cryptocurrencies, businesses are trading on a recent boom of new forms of structured and unstructured data. Whether it has been digital or voice, every time a new channel is created a new kind of data is born alongside it.

Yet this has consequences for the data that came before, and for the businesses that continue to store it. As technology advances, old data gets harder to read and slower to utilise. Eventually, it becomes obsolete and less care is taken to properly manage it. Once it has fallen off the radar, we call it dark data. When data goes dark, conditions can become very dangerous for an organisation. To overcome this challenge, financial services companies will need a more strategic approach to data management and an increasingly robust use of technology.

The dark age of financial data

From the days of the earliest banks, financial services companies have always used data to improve and streamline the customer experience. We have come a long way from personal customer information written on paper documents, to credit scores, purchase histories and the telematics data used by an increasing number of insurance companies. Yet, this long history of data collection is part of the problem.

As financial services companies evolve, old data loses its strategic and business value – going dark. With today’s limitless cloud storage systems, it is far easier to make use of digital data than it is physical written records. Inevitably, the latter is filed away and eventually lost. Yet dark data never completely goes away.

Financial services companies are particularly vulnerable to the rise of this dark data. Indeed, the industry holds huge backlogs of stale data, 20% of which are made up of old document files. As smart contracts and blockchain transactions grow in popularity, this type of old data is rapidly losing its relevance and value.

The financial services industry’s heavily regulated environment is partly responsible for creating a culture that is cautious to delete anything. The result of this ‘save everything often’ mentality is that old data takes up valuable storage space.

The out of sight, out of mind nature of dark data also means it stops being properly managed, maintained and protected. Over time, this can pose a major security risk to financial services companies and their customers. With data privacy regulations like GDPR now in effect, consumers are more likely to take action against irresponsible financial services firms than any other sector, so dark data represents a ticking time bomb for data security.   

 How good data dies

To fight the dark data problem, businesses must stop it at its source. Ultimately, dark data stems directly from a lax data management strategy. This is not a new phenomenon; indeed, it has long been an aspect of development culture in financial services. Historically, mainframe systems were siloed and when a new application was to be built it would be done in a separate environment. Unsurprisingly, the data these companies hold is now spread across many different databases found in the cloud and on-premises.

When data becomes dark, it is not because of negligence but the complexity of keeping it organised in deeply fragmented IT environments. Research shows that employees regularly struggle with an overabundance of data sources and tools, as well as a lack of strategy and backup solutions. According to our research, the majority (81%) of organisations think their visibility and control of data is unsatisfactory and even more (83%) believe it is impacting data security. Not only is this fueling the rise of dark data, but it is also hurting the ability of employees to find and utilise valuable data, resulting in missed business opportunities and wasted resources.

A better way to manage data – Creating a data management strategy

As data becomes more siloed and fragmented, it is harder to find, manage and protect. This is how dark data turns into a risk. To stop this happening in the first place, financial services companies must create data management strategies that accommodate both recent and obsolete data. At the same time, they have to resist the temptation of a ‘save it all’ strategy. Instead, they should take advantage of new tools and platforms that can locate, automatically classify and manage data across multiple environments.

Introducing and enforcing data management policies

Data management policies should be put in place and enforced from the bottom to the top. This means everyone knows what the data types and formats are and where they should be saved at all times. But it is equally important that these boundaries are not too restrictive. Data is changing all the time, so standards too will need to adapt. Employees should be allowed some freedom of action as long as they stay within the goal posts.

Using the right technology

Financial services companies should also be willing to adopt data management technologies for increased efficiency and protection. A single, unified data management platform can make use of intelligent automation, helping employees locate the data they need faster. This not only makes data less likely to become dark, it gives the company a strategic edge and the ability to make better business decisions faster.

It is not only old, established players that should fear the rise of dark data. Disruptive payments providers and challengers may be on the cutting edge now, but they are just as subject to time and the depreciation of data. Finding new ways to utilise and safeguard data is at the heart of digital transformation. It is the key to creating opportunities and value for a business. Good policies and a structured, automated approach will not only prevent the rise of dark data in financial services but also help financial services companies truly harness the power of their data.

By Jasmit Sagoo, senior director, Northern Europe, Veritas Technologies