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5 LESSONS ALL FINANCIAL SERVICES PROFESSIONALS NEED TO LEARN ABOUT AI AND MACHINE LEARNING

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Marcin Kacperczyk is a Professor of Finance at Imperial College Business School, and teaches on the AI & Machine Learning in Financial Services virtual Executive Education programme

 

Our workplaces and industries are becoming increasingly more digitally driven, and the Financial Services sector is no exception. According to a report compiled and published by the Economist Intelligence Unit at the end of 2020, 54% of financial services organizations with more than 5,000 employees have already adopted AI technology, and smaller financial services providers are not only taking note, but following suit. A Bank of England survey has reported that financial services firms expect to see significant growth in AI and Machine Learning technologies over the next three years.

And it’s not hard to see why. Such technologies not only speed up and increase capacity for the day-to-day basic tasks such as transferring and moving money or making payments, they boost convenience for customers and companies alike. This latter point has been particularly important over the past 18 months as Covid-19 has dramatically altered the ways in which traditional business is conducted. A switch to digital and automated services within the financial services sector has reduced the need for face-to-face meetings, made services available to customers 24-7 and has provided greater visibility and autonomy to customers at a time when trust has been paramount.

But these technologies have even smarter and more nuanced uses which, if deployed correctly, stand to provide huge returns. For example, by gathering customer and industry data and identifying algorithms within it, such technology can improve decision-making and reduce the potential for human oversight and error – not to mention reach conclusions in a quicker, more accurate manner. Aside of such capabilities leading to increased productivity which, in turn, boosts company and industry performance and profitability, the insights they can provide can give companies the inside track on how the wider industry is operating and how they can use this to their advantage.

However, investing in, and making effective use of such technologies is not as straightforward as it seems – and can be the cause of significant losses if not deployed correctly. Alongside those industry reports predicting huge levels of digital take-up come the less-optimistic reports from the likes of Gartner, a global research and advisory firm, which predicts that up to 85% of AI projects will result in flawed outcomes due to bias and mistakes not only in the data they collect but also due to the humans in charge of managing them.

We cannot slow or oppose the impact that technologies such as artificial intelligence and machine learning have had on the world, so how can financial services professionals help ensure that they can keep pace and be able to work effectively in an increasingly smart landscape?

The key to making good decisions about AI and Machine Learning – and avoiding expensive failures – is, unsurprisingly, being able to understand it. Of course, firms can invest in basic training for staff by enrolling on education programme at business schools specifically designed to upskill financial services professionals. But aside from this, there are a number of other simple steps professionals in the sector can take to help protect against bad investments or poor decision-making when it comes to bringing such technologies in-house.

Through my own research, and in working with financial services executives on the AI & Machine Learning in Financial Services Executive Education programme at Imperial College Business School, I have identified the common missteps most financial services professionals take when it comes to AI and machine learning deployment, and from this have devised five simple, yet essential, lessons for them to learn.

 

  • AI and Machine Learning are different

It might sound obvious from the fact that the two have different names, but it’s easily to confuse their purpose as they provide very similar functions. However, the subtle differences between the two are not to be ignored as they operate in wholly different ways and have different requirements for their success. Being able to make the distinction between the two is vital for financial services professionals in order to better decide where and how to use them – or even if they need to be used at all.

AI, very simply, creates programmes which mimic then enhance human processes. However, it has its limitations. Current AI technology is routine-based – learning from common data patterns and making decisions based on the typical actions and results of similar scenarios it has previously recorded. Because of this it cannot forward think or innovate in the same way a human can. It is limited to only making decisions and providing results based on probability from historic data. The sci-fi perception of self-aware, self-thinking AI bots are no more than fiction so far.

But Machine Learning might be the closest thing we have to autonomous AI so far. It exists as a sub-category of AI’s capabilities. It’s a technology designed and implemented with a specific purpose – to either fit models or identify patterns in the data it is provided with, without explicit programming or needing human intervention. A good example of Machine Learning in action is how a search engine might rank page results for the words and phrases that users type into them.

 

  • Train your Algorithm

The likes of Google may have AI “super brains” that can outsmart world champion human chess players, but it is misleading to think that these machines have secured such results under their own intelligence. These super-computers are the result of hours upon hours of human intellect, filling them full of data and teaching them to follow and identify certain traits within it. They cannot outsmart humans on their own, instead they’re making judgements of probability, having been fed all the possible information there is to know about the topic at hand.

It’s a fantastic example of how, with the right lever of detail and understanding, Machine Learning can be applied to great success in scenarios where there are clear boundaries and a finite set of outcomes (like the rules of Chess). This sort of structure is where Machine Learning can really thrive.

Apply this thinking to the finance sector – if trained effectively, such Machine Learning algorithms could tidy-up messy, complex data sets with a slimmer margin for error, and suggest best routes forward. For example, Machine Learning can help analysts distil hundreds of potential indicators of future investment returns into a few, more robust, measures – something much more manageable for human professionals to work with. Which leads nicely into my next lesson

 

  • Machines cannot beat human rationale

These technologies excel at identifying relationships and patterns within a set collection of data; however, they are incapable of doing so in less structured environments. And this is where effective human decision making and rationale must come into play. It is not just unsuitable but also potentially dangerous to attempt to use a tool such as Machine Learning to solve problems where discretion or special consideration may need to be made, or where ethics might be brought into question.

By its very nature, the finance sector places great stock in human instinct and taking calculated risks. This is a skill that surpasses even the most intelligent of artificial services.  Whilst an algorithm might well do a good job of identifying patterns in historic data to help provide the information on which to make a judgement call, its intellect is limited to past performance which, as we know is not always an accurate indicator of future potential. For this reason, humans are still very much setting the standard in the sector and will remain the driving force for some years to come.

 

  • Good data is vital

Good data gets good results. For your Machine Learning application to be effective you need to have reliable data for it to work with. And that can be hard to come by. It is a misconception that the finance industry is awash with data. Whilst this may well be true for transactional areas such as payments, it is typically very hard to gather effective data in areas such as company performance. This makes it difficult for analysts producing quarterly reports to build a statistically robust machine learning model. In addition, in areas where there exists a grey area between how data is compiled or categorised it becomes difficult to then produce algorithms that are free of bias.

 

  • Machine Learning can go beyond numbers

Despite the many learning hurdles, there are good opportunities to use Machine Learning to enhance financial decision making, and as the technology improves so too will the advantages we can gain from it.

For example, software is currently being developed to allow Machine Learning to go beyond numerical analysis and instead conduct accurate textual analysis too. This development will soon make it possible to analyse customer’s communications as well as their actions. Additionally, image analysis capabilities are also being developed which could, one day, prove to be a real asset to the financial sector by creating more timely and reliable data. For example, an analyst could use real-time satellite imagery to record the number of cranes being put up in a city and use this information to help measure the levels of construction activity over a given time-period, which could then help produce information in advance of industry surveys. A far-off prospect, but one absolutely within the realm of possibility.

And these developments mean it is vital for financial services professionals to keep on learning – and avoid investing in AI and machine learning because of the hype, but to instead gain a truly competitive advantage. Failing to understand how and where such technologies can best be applied will not only result in loss in the short term, but also result in a significant disadvantage as these technologies develop and the focus turns to not only what can be possible, but ethically what should be possible.

As Machine Learning’s ability to digest different types of data expands there are questions which need to be asked about how to ethically use data from other sectors, particularly when it comes to using this data as part of the decision-making process. Those that can face those discussions and master the technological application process effectively stand to win big.

Most crucially, for that development to happen, it is vital that practitioners take the steps now to learn the current state of the technology, where it works well and where it doesn’t. Master that and you can master all that is to come.

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IS SCARCITY OF TALENT THREATENING THE UK’S FINTECH CROWN?

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Opinion From Rafa Plantier, Head of UK and Ireland at Tink

 

From the Square Mile to Canary Wharf, London has been the historic centre of global finance, with long-established trading exchanges and trusted financial institutions. In the digital era, it has also ensured that it’s moved with the times to become a thriving hub for fintech.

But the UK financial services sector is now at an inflection point. In the past year, London’s position as a global fintech leader has been under threat. Earlier this year, Amsterdam overtook The City as the largest European share trading hub. The European Banking Authority moved from London to Paris. And Dublin, Paris and Frankfurt are all competing to win a greater share of the European financial marketplace.

The culprits of the shift are the twin challenges of the pandemic and Brexit, combined with the speed of technological transformation in financial services – disrupting the traditional flow of people, capital and ideas. So the pressing question for the industry is: how do we maintain and, more importantly, accelerate momentum to retain London’s fintech crown?

The answer revolves around one key thing — people.

 

Diverse talent drives innovation

Attracting the best talent is crucial if the UK financial services sector is going to continue to thrive and retain its global position as the preeminent financial centre.

In February 2021, the Kalifa Review laid out a strategy and delivery model for the UK to lead the fintech revolution, covering five key areas. These included skills and talent, investment and international attractiveness and competitiveness. But what became clear was that access to the right level of highly skilled talent was one of the biggest challenges for UK fintech, with barriers spanning both domestic skills shortages and the need to access foreign talent seamlessly.

As a native Brazilian in the UK, working for a Swedish-owned fintech, I understand these challenges as well as anyone. I love London, but we must recognise that fintech firms need unique talent and skills, and such a talent base can’t be met by a single city – not even one as resourceful as London. Not only do fintechs require technology and data specialists, but also experienced managers with good knowledge of high-growth companies and financial services.

As someone lucky enough to have worked with startup and scale-up fintechs across the world,  I understand the unique grounding that comes from being a part of a high-growth global company. That’s why I believe it’s vital that we attract people from across the world with commercial experience at ambitious, rapid-growth businesses — so they can bring this experience to bear on the UK financial services sector.

At the same time, many companies face renewed pressure to create new services and products to meet expectations for growth. That is why it’s critical that the UK has access to people with the right technical skills in areas such as software engineering, DevOps, Cybersecurity and data science.

Put simply, having the smartest minds delivering the best products is good for everyone. It drives efficiency, productivity,  growth and, ultimately, prosperity.

 

The UK is open for fintech

The UK should be proud of being a fintech pioneer and the driving force behind legislation that helped usher in the era of open banking. There is now an exciting opportunity to take this even further. Having access to a diverse pool of talent and skills will empower the financial services industry to create innovative products to tackle complex social challenges, such as better B2B payments, financial inclusion and climate change.

The good news is that the UK government clearly recognises the role the industry has to play in driving growth and innovation. The 2021 Autumn Budget reaffirmed commitments to reskill the nation. With £3.8bn budgeted for skills and a formal criteria for the long-awaited Scale Up Visa, the Chancellor announced a set of proposals that will support the breadth of our sector — from startups right through to unicorns and incumbent banks. This will be essential for fintechs like ours to continue to trailblaze and for the UK to differentiate itself on the global stage.

In an increasingly competitive global landscape, and to sustain momentum, we must keep talent avenues open to attract the best of the best in the industry. As one of the fastest-growing areas of the UK economy, the benefits of nurturing UK fintech to drive productivity, growth and lead the UK’s post-pandemic recovery, cannot be overstated. 2021 has seen a surge of activity in the industry and I am eager to see what London’s fintech sector can achieve in 2022.

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SET YOUR BUSINESS UP FOR SALES SUCCESS IN A POST-PANDEMIC WORLD

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SET YOUR BUSINESS UP FOR SALES SUCCESS IN A POST-PANDEMIC WORLD

Dean Fiveash, Head of FinTech Sales, IFX

Without doubt the Coronavirus pandemic impacted every aspect of our lives and fundamentally changed the way in which we all conduct business.

From the widespread adoption of working from home, to the amplified focus on employee wellbeing and work life balance, to simply acknowledging that people are more than their job titles and are often juggling childcare, pets and terrible wifi issues all whilst trying to do their job. The last 18 months have altered the way we work forever and in order to set our businesses up for success we have also needed to rethink how we operate.

Dean Fiveash

In a people facing sector like sales,  it’s  clear that the loss of face-to-face interaction is perhaps the biggest loss and an impending challenge as we slowly emerge from the confines of the pandemic. Gone are the days of instant downloads from ‘water cooler’ conversations with the team discussing deals or general matters. Instead, our inboxes and diaries are full of zoom catch ups. This isn’t to say that success has dwindled. Flexibility of working from home has helped many businesses to grow rapidly. In fact at IFX we have enjoyed our ten best months of company sales, but there is no denying the way in which we work within our teams has shifted. So how can you set up your sales teams to maximise its chances of success?

 

Adapting To The Times

For many businesses operating during these unprecedented times the shift towards the work from home culture has seen its benefits. Speed is key in the fintech industry and video calls on top of isolated working has greatly improved our time efficiency allowing us to do more for our clients in the long run. Equally, with the workforce being spread around the country and in some cases even globally, came the need for further rigorous checks and processes to ensure the high standards set in the office environment are still being met.

Despite this I would argue that this made us better sales people, and in turn a more successful and thriving sales team.

Post-pandemic success is grounded in not just the talent of your employees but also how you choose to structure your teams. For me, the old adage ‘People Buy People’ remains the most relevant factor for developing a slick sales team. At the end of the day, the technical stuff can be learnt over time but the proficient people skills needed in client facing roles is more innate.

When evaluating team skills, individuals who demonstrate determination and the ability to keep smiling through adversity are a vital asset, especially in the fast paced fintech industry.

Having worked in numerous team leader roles within the sales industry,  I know the difference that a collegiate and supportive team can make to successfully securing deals. The key is to have people at your disposal who are going to pitch in to help others, in turn making the team more robust. In the post-pandemic world, this will remain the key quality to look for and embed as a core value across the business.

 

Fostering A Successful Culture 

Whilst the team structure and core skills are an important part of the team set up, good management and personal development structure is crucial to success. At IFX, our sales leadership team all have client portfolios and are regularly signing and navigating deals. It’s through giving my team practical experience and regular client interaction that we can gain far better market insight than through managing team activity or KPIs alone.

More discipline is also required when working at home to retain the sales focus whilst navigating domestic distractions. As such, maintaining your employee motivation and focus is something each business should work on. A difficult feat without the physical presence of your team and one balanced on knowing your employees and their individual needs. But little things go a long way, so incentives and perks such as company socials, bonuses or simply a free breakfast can work wonders to motivate others. Another tip is to set  attainable goals and regular check-ins with your team to keep motivation on track to reach peak productivity.

 

Looking Forward

Team dynamics will continue to change to adapt to the ever-changing and rapidly evolving landscape, the secret to success will remain the same.

Something to look forward to in the next couple of years as a movement,  is the greater adoption of smarter contracts and embedded FinTech, which of course as businesses and as a team we will have to adapt to.

Ultimately, my biggest piece of advice to others is to get the basics right.  A leading-edge solution fails to achieve greatness if it isn’t backed with competent sales/relationship managers and attentive operational support. Traditional ingredients for success such as reputation and trustworthiness are built over time, often through word of mouth, but building a competent team who can make your clients happy is essential to that mix

 

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