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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.


CBDCs: the key to transform cross-border payments




Dr. Ruth Wandhöfer, Board Director at


If you work in finance, you’ll have been hearing a lot about central bank digital currencies (CBDCs) and the moves different markets are making towards using, regulating and evaluating the viability of moving to an economy based on digital currency.

We are already seeing progress in the research, piloting and introduction of CBDCs into the financial system. The Banque de France for example, recently launched its second phase of CBDC experiments in line with the “triple digital revolution” unfolding in the financial sector. The infrastructures of financial markets and fintechs, however, are not prepared to accommodate their security, stability, and viability.

This could be an issue in the not too distant future. Each year, global corporates move nearly $23.5 trillion between countries, equivalent to about 25% of global GDP. This requires them to use wholesale cross-border payment processes, which remain suboptimal from a cost, speed, and transparency perspective. In fact, the G20 cross-border payments programme considers improving access to domestic payment systems that settle in central bank money, as one of the key components in facilitating increased speed and reducing the costs of cross-border payments.

The current state of cross-border payments

International transactions based on fiat are currently slow, expensive, and highly risky due to today’s disconnected financial infrastructure, messaging, and liquidity. Wholesale cross-border payment settlement can take 48 hours or longer, which is not practical in today’s digital world. Even if not every market moves to CBDCs, in an increasingly digital era, cross-border settlements between central banks will unavoidably involve dealing with CBDCs. So, not only will we have different currencies, we’ll have different technical forms of currency being exchanged – digital and fiat – as markets adopt CBDCs at different rates, adding another layer of complexity to cross-border settlements.

While there is much anticipation about the opportunities CBDCs can bring, the adoption of this technology will only be widespread if payment and settlement capabilities are overhauled to allow for new innovations in currencies.  This need for transformation represents an opportunity to redesign existing infrastructure to support cross-border CBDC transactions.

The current cross-border payments system involves correspondent banks in different jurisdictions using commercial bank money. Uncommitted credit lines used in cross-border transactions are a potential risk for any bank that relies on credit provided by a foreign correspondent bank. Interestingly, there is no single global payment and settlement system, only a complicated network of interbank relationships operating on mutual trust. While trust has allowed financial systems to function smoothly, when it begins to fail, as it did during the 2008 financial crisis, the result can be catastrophic.

Following the crisis, the Bank for International Settlements (BIS) implemented the Basel III agreement, which required banks to maintain additional capital against correspondent banking account exposures. These risk-weighted assets impose a costly capital charge on positions held by banks at other banks under correspondent arrangements. While this framework helps combat risk, it neglects to address the inherent problems in traditional correspondent banking that contribute to these risks.

Making the case for CBDCs

CBDCs can offer an improvement in settlement risks and are certainly thought to have potential benefits by the BIS. If implemented correctly, wholesale CBDCs can indeed accelerate interbank transactions while eliminating settlement risk. They can also encourage a more efficient and straightforward method of executing cross-border payments by reducing the number of intermediaries.

It is likely the evolution towards CBDCs will initially see the financial market supplement rather than replace existing payment instruments with new types of digital currency. CBDCs will coexist with current forms of money in a wholesale context, and their payment rails will also work alongside the existing payment systems. In simple terms, CBDCs will need to be linked to the broader capital markets ecosystem and applications such as securities settlement, funding, and liquidity.

If built with an innovation-first mindset, the future of banking infrastructure should provide full interoperability and convertibility between fiat, CBDCs, and any other type of digital money used in wholesale payments.

The future of CBDCs

To unlock the full potential of CBDCs, a ‘corridor network’ will need to be formed. This involves combining multiple wholesale CDBCs into a single, interoperable network under common governance agreed upon by all central banks involved. The legal framework of this platform would then allow for payment versus payment (PvP) or, where applicable, delivery versus payment settlement.

Practical wholesale CBDCs appear to be on the horizon, either as a supplement to existing financial systems or as part of a transition to a digital, cashless world. Looking ahead, central banks would benefit from collaborating with fintechs that provide innovative cloud native technology to enable seamless wholesale cross-border payments without interfering with the flow of funds. If wholesale CBDCs are to become a reality, fintechs must be prepared to accommodate them.


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Green growth: The unstoppable rise of climate technology investment




With the investment community focusing more and more on renewable technologies, investor interest is at an all-time high. Ian Thomas, managing director, Turquoise, reviews the current investment landscape and highlights the opportunities for investors keen to capitalise on this growing trend.

Green, or climate, finance is a label for providers of finance who are supporting investments seeking positive environmental impact. The label covers investments in green infrastructure, venture capital investment in clean technologies and renewable energy. Green finance has grown by leaps and bounds in recent years, supporting public wellbeing and social equity while reducing environmental risks and improving ecological integrity.

Worldwide, energy investment is forecast to increase by 8% in 2022 to $2.4 trillion, according to a new report by the International Energy Agency, with the expected rise coming mostly from clean energy – $1.4 trillion in total. To put this rocketing figure into some perspective, clean energy investment only rose by 2% annually in the five years following the signing of the Paris Agreement in 2015. Energy transition investment has some way to go, however – between 2022 and 2025, to get on track for global net zero, it must rise by three times the current amount to average $2,063 billion. [1]

Turquoise has been active for almost 20 years as a venture capital investor and adviser to companies in the climate technology space that are raising capital and/or selling their business to a strategic acquirer. Reviewing current industry investment news, as well as drawing on examples from the portfolio of Low Carbon Innovation Fund 2 (LCIF2), managed by Turquoise, I have commented below the latest on the renewable energy trends most piquing investor interest.


Solar PV

Renewable power is leading the charge when it comes to investment, with wind energy and solar PV emerging as the cheapest option for new power generation across many countries, and now accounting for more than 80% of total power sector investment. Solar power is responsible for half of new investment in renewable power, with spending divided roughly equally between utility scale projects and distributed solar PV systems.

This huge increase in solar spending, which continues in spite of supply chain issues affecting raw material delivery, has been driven by Asia, largely China (BloombergNEF, 2022). Meanwhile, Europe is re-doubling its efforts to achieve an energy transition away from Russian gas and other fossil fuels, building on investment that was already rising steadily prior to the outbreak of war in Ukraine. Germany, the UK, France and Spain all exceeded $10 billion on low-carbon spending in 2021.[2]



Last year was a record year for offshore wind deployment with more than 20GW commissioned, accounting for approximately $40 billion in investment. The first half of 2022 saw $32 billion invested in offshore wind, 52% more than in the same period in 2021 (BloombergNEF, 2022). Taking into account also onshore wind, in 2021 investment was spearheaded by China, followed by the US and Brazil.[3]

In the UK, suggested targets include plans to host 50GW of offshore wind capacity, as well as 10GW of green and blue hydrogen production, by 2030. Investors will naturally be encouraged by proposals to simplify the planning process across the board for renewable projects.[4] France and Germany have also increased their offshore wind targets, signalling further support for investment.


Decarbonising housing: the business opportunity

The need to decarbonise residential housing, made all the more urgent by current energy prices, also offers substantial scope for investment. The gas price spike is naturally increasing interest in technology such as electric heat pumps, which had already enjoyed 15% growth in 2021 albeit from a very low base.

Recently, Turquoise announced an investment by Low Carbon Innovation Fund 2 (LCIF2) in Switchd, which operates MakeMyHouseGreen, a data-driven platform that allows homeowners to source and install domestic renewable energy generation, including solar panels and battery storage with other energy saving products in the pipeline. The investment will enable Switchd to roll out the MakeMyHouseGreen platform to a much larger number of customers. The latest episode of the Talks with Turquoise podcast series saw us interview Switchd co-founder Llewellyn Kinch about the UK energy market and national transition to decarbonisation, covering the rise of residential renewable energy and energy efficiency.


Adapting to the low-carbon economy

Meanwhile, investors should not forget opportunities on the other side of the energy market. Renewables are undoubtedly exciting investors, but there are also opportunities for fossil fuel companies to adapt their business models to the low-carbon economy. Turquoise advised GT Energy, a portfolio company from our first fund that develops deep geothermal heat projects, on its sale to IGas Energy, a leading UK onshore oil & gas producer. Under IGas ownership, GT Energy will progress its flagship 14MW project to supply zero-carbon heat to the city of Stoke-on-Trent through a council-owned district heating network.


A broad investment landscape

Forecasts show that renewables will increase to 60% of power generation in Europe by 2030, and 40% in the US and China by the same date.[5] As demand rises for climate technology, the investment opportunities in green finance are far broader than they ever have been. Undoubtedly, as the energy crisis continues, investor interest will continue to soar to even greater heights.


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