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AVOIDING THE PITFALLS OF AI IN POSITIONAL TRADING

By Pan Yiannakou, CEO of Swarm Technology

 

Artificial Intelligence (AI) has advanced exponentially in recent years. It is widely regarded as a must-have tool to help traders distil vast sets of data to make the most profitable decisions quickly, but how does this work when taking longer term positions? Chief Executive of Swarm Technologies, Pan Yiannakou, explores the traps to watch out for when deploying AI in slower, positional trading.

Once a decision has been made to take a position in a market, the initiator must quickly consider a vast array of options to achieve the most profitable outcome – the exact timing; execution methodology; instrument; location and/or intermediary, to name just a few.

Take the decision to buy gold, for example. A trade can be executed immediately, after a few seconds, a few minutes or a few hours. A trader can either ‘lift the offer’ or ‘sit on the bid’; they can set a price target (below) or set a price trigger (above). They could also wait for some other event to trigger the trade. A trade could be made in the cash market, futures, options, as a contract for difference (CFD), or through an exchange-traded fund (ETF). The trader can conduct their business on an exchange, through a broker or directly with a counterparty.

Without restrictions, there are more than a million combinations of ways to gain a long exposure in gold. To add another layer of complexity, these variations are not static and anything from updated broker fees to shifting liquidity to changing counterparties has the potential to exact a significant cost impact.

In an environment where speed is critical and the variations are so vast and constantly moving, the case for deploying AI to solve this data-rich, complex, multi-dimensional problems is clear: AI is powerful enough that not only can it perform well in a dynamic, complex environment, it can also identify genuine opportunities hidden in the data that humans could never recognise.

However, high-frequency trading also has its limitations, particularly issues with liquidity. Sometimes, it is not possible to put to work all the funds required by participants in short term trades. The alternative is slower trading: taking positions that run for hours, days, weeks or longer. Berkshire Hathaway has famously held Wells Fargo, Coca-Cola and American Express stocks for over 25 years. But slower, positional trading presents a particular set of challenges for AI which could immediately and directly hit profitability.

 

Weaknesses of AI

  1. Biased data

AI is designed to identify biases, and to adapt to new ones. If historical data contains a bias that is suddenly no longer valid, that can result in poor decisions.  Slow trading limits the speed at which AI can adapt. If there is a chaotic shock that changes the bias, damage can be difficult to avoid. Consider Covid-19’s impact on global financial markets. There was the initial reaction, which led to an almost instantaneous shift in bias across all asset classes. By the time AI systems pick up the switch in bias, the damage has been done.

  1. New price action

AI works well when trained on large amounts of historic data, or when sufficient new data is being  generated quickly enough. When markets move in a way they never have done before, there is no historic data for AI to base decisions on. Some events are so rare – like the subprime crisis of 2007-08 – that there may not be enough examples in the past.

  1. Random clusters

AI is very efficient at finding patterns in large amounts of data. But this can be a weakness. Often, large amounts of numeric data such as price contain random patterns – ‘ghosts’ in the data, or random clusters as they are known. There is a simple test to identify this problem: present an AI trading system with a series of genuinely random prices. Will it find patterns and generate trading signals? The answer is usually ‘yes’. AI is so good at finding patterns, it will find random patterns too. Executing trades based on patterns that do not contain a genuine bias will cause eventual losses due to trading costs.

  1. Feedback

There is a strong feedback loop that degrades the value of patterns that are easily identified by AI. Consider a specific event that leads to a price gain of 5 per cent in a particular market, for example. If that exact same event occurs in the exact same circumstances again, the participants will all know that it led to a 5 per cent rally the previous time. When the pattern repeats, sellers are likely to sell before the 5 per cent target is hit, not wishing to be the last out. Even with exactly the same trigger and exactly the same starting conditions, the market would behave differently. The more participants that identify the same opportunity, the less effective that opportunity is.

 

Trade like an ant!

AI trading solutions do not have to be limited to fast trade execution. There are ways of using AI that avoid the pitfalls listed above. Swarm Technology, for example, uses the natural principles of swarm intelligence in its AI solution; this is a form of biomimicry. Swarm does not use AI for pattern recognition or to aid trade execution. It generates a Swarm Matrix, that shares information between all the markets and the systems applied to those markets; in a similar way to that of ants in a colony, who share information and adapt their behaviour accordingly.

While AI does have its drawbacks in slower, positional trading, it is not impossible to overcome the pitfalls.

 

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Finance

FIVE TRENDS THAT WILL IMPACT THE FINANCIAL SERVICES INDUSTRY IN 2021

Ian Johnson, Managing Director Europe at Marqeta

 

Coronavirus has shaken things up across all industries, and financial services is no different. This year, we are likely to see a much more risk averse industry, as fintechs and banks alike move into survival mode. Yet, this will also spur innovation. The shift away from cash will give a shot in the arm to digital payments, while lenders in particular will have to get creative to balance their risk against the need to dispense funds.

It’s likely to be an interesting, albeit bumpy, year. Here are five core trends that I see having a major impact in 2021.

 

Lenders will seek improved visibility to combat delinquency

An economic downturn unfortunately means higher delinquency rates for lenders. But businesses – in particular, SMEs – need liquidity to survive, now more than ever. To balance risk with need, more lenders will focus on enabling visibility and control after a loan is dispensed. Instead of issuing funds to a bank account, loans will be dispensed to virtual cards or wallets, allowing lenders to track exactly how and where money is spent. This way, lenders only release funds as they are needed – rather than in one lump sum.

Ian Johnson

They also have the power to approve or reject payments in real-time, based on whether the request is aligned with the terms of the loan agreement. For instance, if a company has secured a loan for IT equipment, but attempts to spend it on office refreshments, the lender can make an instant decision to permit or deny the transaction based on geolocation and other transactional data. So, borrowers should ready themselves to be much more transparent if they want to secure loans in the future.

 

Embedded payments to become more commonplace

Embedded payments has been around a long time – just look at pioneers like Uber, where payments are so integral to the customer experience that it doesn’t even feel like you’re paying anymore. In the next year, we will see this expand, with a wider variety of organisations making payments a core element of their customer experience strategies. This trend will be coupled with a shift towards transparency and privacy, where people willingly exchange their data for an improved, personalised experience.

This is something consumers do readily in many areas of online life already – shopping, social media, and so on. In 2021, we will see more banking and payment services operating off the back of this same exchange. In return for data, customers will be given smoother, more tailored payment experiences.

 

Use of cash to drop below 15%, falling from 23% of all payments in 2019

The UK and Europe’s departure from cash will continue to evolve into next year. Physical cards will begin to give way to a rise in digital payment methods – virtual cards, digital wallets, and the likes of Apple Pay and Google Pay. Banks will need to prepare for this shift; hopefully learning their lesson from the early months of the pandemic, where 88% were overwhelmed by demand for online and mobile banking. This means modernising behind the scenes, using technology to improve and streamline payment processing. Time and money also need to be invested into educating and supporting businesses and individuals that going cashless could leave vulnerable, such as small merchants and elderly people. Until this has been addressed, going cashless risks leaving the most vulnerable in our society behind.

 

Back-end bank modernisation set to continue

Traditional banks recognise that they need to be able to innovate faster, particularly on the front-end, to compete with the new waves of digital banks and fintech entering the market. While we will see continued modernisation on the back-end, as they try to unpick the complex web of legacy systems they sit upon, I would not expect this issue to be fixed in a year. Instead of taking on the risk of full migration, many banks will ‘hollow out’ certain services – leaving core services in place that are too risky to move, whilst shifting newer services onto more modern platforms to avoid coding them into legacy systems.

This will create the building blocks to build a standalone digital bank within a bank, allowing them to modernise the entire stack and then incentivising customers to make the switch. An example of this approach is Goldman Sachs’ digital bank Marcus, which has debuted to strong demand – it’ll be interesting to see if others follow suit.

 

Alternative lenders will open up the market to support post-COVID-19 recovery

The process of securing a loan has always been quite painful – involving lots of self-reporting, paper statements and credit reports. And it could take days to find out if you were successful and then even longer to access the funds. Thankfully, it is looking like those days might be coming to an end with the emergence of a new breed of alternative lender focused on transforming specific niches of lending. Take SME lending, which has traditionally been regarded as high risk/low rewards and neglected by traditional lenders.

New alternative lenders, such as Capital on Tap, are changing the stakes. Using data and modern payment platforms, they are able to make loan decisions in minutes, not months. We are seeing the same in Point of Sale lending with companies like Klarna – now, you can apply for a POS loan and get approved in seconds. These companies will set the standard in terms of expectations around lending, forcing bigger lenders to follow suit and helping to transform the loan experience.

 

Fintechs to continue leading front-end innovation

Fintechs hold the monopoly on defining what ‘good’ looks like in terms of features. From money management tools, to saving incentives, fintechs have the agility to create new, attractive products with a speed and creativity that traditional banks simply cannot match. However, true success stories of fintechs paving the way to long term profitability are rare. Established, traditional banks still hold all the capital and most of the main checking accounts, making it harder for fintechs to really get ahead. This is likely to continue into 2021, but we are seeing signs of convergence, with fintechs acting as the front-end for customers while banks provide capital in the background.

 

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Finance

2021 IS THE YEAR FOR DIGITAL WORKFORCE MANAGEMENT IN FINANCIAL SERVICES

By Tyler Suss, Product Marketing Director at Kofax

 

Even before the pandemic, the UK financial services sector viewed digital transformation as a high priority. Though adoption of robotic process automation (RPA) technologies was already underway, the pandemic truly upended operations.

When health mandates closed offices, the ability to manage operations became more challenging and complex. Many processes still aren’t fully integrated or automated, leaving remote workers with the challenge of having to bridge the gaps in fragmented and often labour-intensive processes. More than ever, they need a digital environment in which back-office processes are automated end-to-end to be productive.

Consumers, too, are learning new ways to manage financial transactions in a COVID-19 world. They’re becoming more comfortable with mobile banking and cashless payments, behaviours likely to stick once the pandemic ends. As KPMG notes, improving productivity and meeting new customer expectations for engagement are the sector’s top priorities for the coming year.

That means firms will need to move even more quickly to digitally transform their operations if they want to remain competitive. In 2021, intelligent automation and digital workflow transformation will become the main vehicles for driving employee productivity and customer experience.

 

Tyler Suss

The Next Priority: Digital Workforce Management

There are many reasons why an intelligent automation program combined with digital workforce management will accelerate digital transformation, but the four that follow build a strong case for adopting this approach in 2021.

 

  1. Workforce Orchestration

RPA caught on like wildfire because it made automating routine, mundane tasks simple and fast. Motivation-killing work like monotonous, cut-and-paste data entry is now a drudgery of the past. What’s next? For savvy financial firms, 2021 will be all about harnessing their RPA automation expertise—and leveraging it with complementary technologies like process orchestration and document intelligence to automate their mission-critical business and create high-value workflows.

With an open intelligent automation platform, financial firms will be able to orchestrate work across people, in-house technologies, and third-party RPA bots. They can assign the right worker, whether it’s a human or digital worker, to the right task at the right time, while maintaining total control over the complexity and cost associated with a given task or project. Additionally, they can take advantage of more advanced AI technologies as they emerge.

 

  1. Risk Management and Security

In financial services especially, it’s crucial that automated processes meet audit and compliance requirements. Security is also of paramount importance, with risk mitigation being a high priority. Yet many firms don’t properly consider the security risks associated with RPA, such as the access software robots have to sensitive data. As human and digital workforces merge, a single governance environment is vital.

Central control allows managers to synchronise software robot releases with broader IT system updates, minimising disruptions and failures among the digital workforce. Robust digital workforce management software lets companies secure and monitor how information is used by all resources. The integration of identity management with financial security solutions supports unified governance over the access human and digital workers have to sensitive systems and applications.

Financial firms also need a way to address potential misuse of digital worker credentials. A sophisticated solution supports the segregation of duties, in which functions are spread out across people and departments. Managers can ensure a particular individual doesn’t have access to too much sensitive information based on the combination of digital workers they oversee.

It’s also important to remember that a digital workforce management solution should enable the organisation to manage and enforce policy controls throughout the entire lifecycle of the digital worker, from creation all the way through decommissioning. Control over the entire lifespan of digital workers enhances security, compliance and auditability.

 

  1. Total visibility into operations across the firm

In order to drive continuous improvement, achieving—and maintaining—total visibility into all resources performing tasks within a process is essential. Financial services firms need to be able to answer such questions as:

What tasks are being worked on?

What’s in the pipeline?

How does process performance compare with KPIs?

An intelligent automation platform including process discovery and visualisation provides insight into business processes across the enterprise. Executives and managers get a holistic view overcoming the boundaries between departmental silos, making it easier to identify opportunities for digital workforce automation that can have a greater impact across the entire firm.

 

  1. Scalability

 The requirement to keep pace with changes in consumer behaviour and agile competitors has only intensified during the pandemic. Scalability will be more urgent in 2021, and yet the majority of organisations have struggled to expand their automation initiatives. The biggest barrier is process fragmentation, in which resources performing the work, including automation and digital resources, exist in silos.

Fragmented operations increase overhead costs and eat into the ROI on digital transformation investments. An open, integrated platform enables common governance and permits financial firms to scale rapidly.

As the pandemic wanes, firms need to reimagine customer journeys and rethink operations to improve customer and employee experiences. The successful ones will build upon their RPA capabilities and rely on intelligent automation digital workforce management to foster more agile and competitive ways of working and thinking so they can work like tomorrow—today.

 

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