However, it’s important to understand that AI is more of a trader’s sidekick than their replacement. The factors that impact asset prices are complex and non-linear, which makes DNNs the best option for detecting these relationships. Then, the AI can make informed decisions about when and where to trade. Visit the official site of Quantum AI Trading to get more information.
While there is an opportunity for pure-play AI trading success, building a strategy that outperforms the market takes more than just a clever algorithm. Traders need to factor in transaction fees, slippage, and the fact that markets change constantly. All these things add up and can cancel out any profits that an algorithm might make in a simulation.
AI algorithms can identify patterns in data that may go unnoticed by humans, which is particularly beneficial in high-frequency trading. They can also process data much faster than humans, which helps recognize ephemeral trading opportunities.
Sentiment analysis is a valuable tool for companies to identify and capitalize on market opportunities. It examines the perceptions and attitudes of consumers through online platforms like social media. Sentiment analysis can help businesses understand their customers’ needs and expectations and determine their preferred choices. It can also identify trends in customer sentiment and anticipate changes in consumer behaviour.
Many machine learning algorithms use data mining to make predictions based on patterns and correlations in complex data sets. This can improve the accuracy of predictive models and enable traders to take advantage of new trading opportunities.
A growing number of financial institutions are deploying machine learning technologies to detect and capitalize on market opportunities. They can also automate processes and reduce human intervention, enabling them to make thousands of trades per day.
In a trading environment that is increasingly complex and nuanced, factors that impact asset prices do not always have straightforward linear relationships. DNNs, which rely on layers to process data hierarchically, can discern such relationships that may go unnoticed by traditional models.
Moreover, ML-powered algorithms can reduce the time and resources needed to assess risk factors in large data sets. However, these tools are prone to errors related to bias and variance. These risks require attention and mitigation.
Traders must be aware of the limitations and risks associated with AI for trading. For instance, the accuracy of input data and the availability of reliable data are crucial. They must also be mindful of ethical and regulatory considerations, which are evolving rapidly. These factors can influence how AI and ML are deployed in the financial sector and their impacts on its performance and stability. Ongoing research is necessary to better understand the evolving adoption of these technologies and address any emerging issues.
Traders must scour a huge amount of data to find information that will increase their profit margin. AI algorithms can help them do that by analyzing market information and identifying patterns that humans may not be able to detect.
It is important to note that despite all the hype about the role of AI in trading, it will not replace human traders any time soon. Instead, it will be a trader’s sidekick, improving their ability to spot opportunities and make smarter trading decisions.
From chatbots to credit underwriting to stock market predictions, there is no shortage of use cases of machine learning in banking.
Despite the fact that risk management has always been at the top of banks’ agenda, many processes are still plagued with inefficiencies that are continuously draining bank resources. In this article, Andrey Koptelov discusses how banks can apply machine learning to streamline regulatory risk management and advance their fraud detection methods.
In the banking context, risk management and regulatory compliance are closely aligned. Banking employees have to manually monitor updates of thousands of regulatory documents, which involves visiting the websites of regulatory authorities and sifting through countless policy documents. This process is not only extremely resource-intensive but also error-prone and overall ineffective.
Machine learning can be used to automate a major part of the regulatory change management process. For example, Compliance.ai, a Silicon Valley startup founded in 2016, provides an ML-driven platform that helps banks keep up with regulatory changes. Using NLP and machine learning, the Compliance.ai platform automatically sources all relevant regulatory content from financial authorities, whitepapers, and news media. Importantly, when significant regulatory changes have taken place, the tool immediately alerts compliance officers.
Bank of Marin, a commercial bank that primarily operates in the Bay Area and has over $2 billion in assets, turned to compliance.ai to streamline its regulatory change management processes. Now Bank of Marin employees has access to all relevant regulatory content in one place, which significantly simplifies regulatory change management. As reported by compliance.ai, the Bank of Marin got returns on its investments in a short time by decreasing the number of resources for compliance.
After the 2008 financial market crash, financial authorities substantially strengthened reporting requirements to ensure that banks can withstand significant economic downturns. This is why financial institutions need to routinely define and report their solvency to stay compliant, and banks with $50 billion or more in assets have to have their risk management teams conduct stress tests.
While undoubtedly important, these risk assessment processes often require hundreds of field experts and inordinate amounts of hours to complete. Moreover, finding relationships between various economic variables in relationship to banks’ performance is an increasingly complex and resource-intensive task.
Machine learning can help compliance experts to identify which exact combinations of values can cause risks, increasing reporting accuracy and decreasing the time it takes to complete these tests. This is exactly why Citi, one of the world's largest financial institutions that operate in 98 countries, joined forces with Symphony AyasdiAI to develop an ML-driven risk forecasting model.
Prior to machine learning adoption, Citi had a hard time passing the annual Comprehensive Capital Analysis and Review (CCAR) conducted by the US Federal Reserve. CCAR requires a bank to submit its annual capital plans to the Fed to prove the bank's ability to deal with severe economic shock. Given the sheer number of economic variables at hand, Citi’s increasingly manual modelling approach took too long to complete, leaving business leads little time to understand the logic behind the models. As a result, Citi couldn't confidently defend its models in an annual submission to the Fed.
To create an accurate revenue forecast model, Ayasdi started with enriching the macroeconomic variables stipulated by the Federal Reserve. Then, Ayasdi used its proprietary machine-learning software to reveal how exactly these variables impact the revenues of each business unit. This allowed the company to define which unit-specific variables have the most impact on revenues. The detected variables were then used to create a custom ML-powered model that can accurately predict business units’ revenues under stressful economic conditions. As a result, Citi has managed to make this compliance process three times faster and less resource-consuming.
Since the beginning of banking, fraud in its various shapes and forms has always been a persistent problem for many financial institutions. While many banks use sophisticated fraud detection systems, the rule-based nature of these solutions leads to a high probability of false positives. Importantly, fraudsters also keep innovating, which makes banks’ fraud detection systems grow obsolete.
Machine learning models, on the other hand, can learn and evolve together with the fraudsters. In very simple terms, a machine learning model can detect unfamiliar deviations from the normal pattern, notify the human employee about it, and, based on human feedback, learn if this kind of deviation is the new acceptable pattern or a case of fraud.
Just a few months ago, IBM launched a new generation mainframe that allows financial institutions to conduct fraud analysis of 100% of their transactions in real-time. To compare, around 10% of transactions could go through an AI-based fraud detection engine. In a nutshell, this means that banks that use the z16 mainframe can significantly reduce the number of false positives and increase customer satisfaction.
Risk management is a natural playground for machine learning. With the amount of data that banks have accumulated in the past decades, it’s only right to use the technology for its analysis. Machine learning models can drastically increase the accuracy of forecasts, decrease operational costs, and make risk management more effective overall.
Yet the use of such emerging technologies also continues to grow exponentially across a host of industries, according to McKinsey. However, financial services organisations are likely to be the biggest beneficiaries with the adoption of such technologies in terms of actual bottom-line business value.
A critical reason for this is that these companies already have access to extremely complex and large datasets, which are a requirement to create different predictive models from this type of technology. Such models are also hugely powerful. They can be applied across a wide variety of financial products and situations, helping these organisations to better understand everything from the possibility and probability of defaults on loans, to customer purchasing intention to detecting fraudulent transactions.
Even the BoE and FCA believe that ML technologies can “make financial services and markets more efficient, accessible and tailored to consumer needs”.
Despite such advantages, to really harness the power of AI and ML and put in place for projects that make an impact with everyday consumers, organisations within the sector do still face some very specific and sizable challenges.
Firstly, financial services is a highly regulated industry whereby personally identifiable information (PII) is required to be protected. Yet this does hinder collaboration as a huge amount of time is necessary to clean and compliance check this information. Due to this, a project’s timescale can really lengthen.
Secondly, despite collecting and managing such a wealth of data, financial organisations face some limitations with the information they have. Even when data has been prepared to develop AI solutions, the actual dataset itself may be under-representative and such limitations are the most cited major barriers that prevent finance organisations from utilising their data assets. up to three quarters (73%) of data actually goes unused for analytics by companies, according to Forrester.
In fact, it is actually quite common that the most valuable information for an organisation is hidden in an under-representative customer category. A biased dataset means the insights gleaned will also be biased. The knock-on effect of this can be quite damaging. It can lead to false assumptions about customer segmentation that leads to higher costs for the acquisition of customers (banks already spend over £279 each year on acquisition per bank account), inappropriate offers being made to customers, which ultimately makes them less likely to purchase, or worse.
Its biggest impact could come in the area of personalisation of financial products to everyday consumers.
Advanced approaches using AI and ML are helping to tackle these challenges. Synthetic data generation technologies have emerged as a highly credible method of protecting PII, while also eliminating the limitations that organisations are facing with their data. The technology, underpinned by AI and ML, constructs a new, entirely synthetic dataset from the original information, one that is highly statistically accurate (up to 95%) but crucially does not reveal individuals’ PII.
It could be transformative for the financial services industry, with organisations like JP Morgan already touting its potential.
Its biggest impact could come in the area of personalisation of financial products to everyday consumers. Undoubtedly such personalisation has improved from tactics such as the ‘Fresno Drop’ which saw over 60,000 pre-approved credit cards mass-mailed to consumers in the Californian city in 1958. However, AI and ML technologies are built specifically to extract insights from data which encapsulates consumers' preferences, interaction, behaviour, lifestyle details and interests. Not only this, but the technology is also developing to such a stage that it can spot and, in effect, ‘rebalance’ biased datasets.
When this approach is implemented accurately, research has found that synthetic data can give the same results as real data. Yet crucially the key benefits include full data privacy compliance and a major reduction in the time needed for product development and testing.
While the successful personalisation of offers, policies and pricing makes a large contribution to the revenues of the business, it also keeps customers happy as they aren’t being bombarded by irrelevant information. This matters hugely as McKinsey found that highly satisfied customers are two and a half times more likely to open new accounts and products with their existing bank.
Having access to such deep insights into all segments is not something that can be put off much longer as consumer behaviour, across generations, is undergoing radical changes already. Research from PwC found that half of younger consumers (those under 35) will open a primary bank account based on a trusted referral from friends or family, by contrast, however, one out of two consumers over 35 will choose a primary bank based on the local presence of a branch or ATM. Such generational differences need to be spotted quickly to keep a financial organisation in step with rapidly changing preferences.
While it is encouraging that more and more financial organisations are using AI and ML technologies, any approach to maximise data’s value must have a coherent strategy behind it. Used in the right way and with the right strategy in place, the opportunity from these technologies offers unlimited potential to financial services organisations.
Richard Harmon, Managing Director of Financial Services at Cloudera, discusses the importance of relevant machine learning models in today's age, and how the financial sector can prepare for future changes.
The past six months have been turbulent. Business disruptions and closures are happening at an unprecedented scale and impacting the economy in a profound way. In the financial services sector, S&P Global estimates that this year could quadruple UK bank credit losses. The economic uncertainty in the UK is heightened by Brexit, which will see the UK leave the European Union in 2021. In isolation, Brexit would be a monumentally disruptive event, but when this is conjoined with the COVID-19 crisis, we have a classic double shock wave. The duration of this pandemic is yet to be known, as is the likely future status of society and the global economy. What the ‘new normal’ will be once the pandemic has been controlled is a key topic of discussion and analysis.
In these circumstances, concerns arise about the accuracy of machine learning (ML) models, with questions flying around regarding the speed at which the UK and EU will recover relative to the rest of the world, and what financial institutions should do to address this. ML models have become essential tools for financial institutions, as the technology has the potential to improve financial outcomes for both businesses and consumers based on data. However, the majority of ML models in production today have been estimated using large volumes and deep histories of granular data. It will take some time for existing models to be re-estimated to adjust to the new reality we are finding ourselves in.
The most recent example of such complications and abnormalities, at a global scale, was the impact on risk and forecasting models during the 2008 financial crisis. Re-adjusting these models is by no means a simple task and there are a number of questions to be taken into consideration when trying to navigate this uncertainty.
ML models have become essential tools for financial institutions, as the technology has the potential to improve financial outcomes for both businesses and consumers based on data.
Firstly, it will need to be determined whether the current situation is a ‘structural change’ or a once in a hundred years ‘tail risk’ event. If the COVID-19 pandemic is considered a one-off tail risk event, then when the world recovers, the global economy, the markets, and businesses will operate in a similar environment to the pre-COVID-19 crisis. The ML challenge, in this case, is to avoid models from becoming biased due to the once-in-a-lifetime COVID-19 event. On the other hand, a ‘structural change’ represents the situation where the pandemic abates, and the world settles into a ‘new normal’ environment that is fundamentally different from the pre-COVID-19 world. This requires institutions to develop entirely new ML models that require sufficient data to capture this new and evolving environment.
There isn’t one right answer that fits every business, but there are a few steps financial services institutions can take to help them navigate this scenario.
When facing a crisis of unprecedented size such as this one, it’s time to look inwards and review the technology investments in place and whether crucial tools such as ML models are being deployed in the best way possible. Financial institutions should face this issue not as responding to a one-off crisis, but as a chance to implement a longer-term strategy that enables a set of expanded capabilities to help prepare them for the next crisis. Businesses that put in time and effort to re-evaluate their machine learning models now will be setting themselves up for success.
Of course, the rise of Human Factors Analysis Tools (HFAT) has forced financial services firms to push the envelope, but AI is gradually beginning to be integrated into the operations of firms across other industries. Perhaps, one sector which lags behind is insurance. However, according to Nikolas Kairinos, CEO and Founder of Fountech, attitudes are definitely shifting and in large part, this is due to the possibilities presented by AI toolsets.
Indeed, the venture capital community considers the insurance industry to be so ripe for disruption that Lemonade, a US InsurTech company, managed to raise $300 million in seed funding earlier this year. As an AI developer myself, I believe that the technology can drastically improve insurers at all levels, but only if industry leaders understand what AI actually offers and how to effectively integrate it into their organisations.
The first, and arguably, most important part of this process, is having a sophisticated awareness of what AI in insurance actually means. For most firms, the benefits of AI actually come through robotic process automation (RPA); in other words, automating existing processes to save time and resources. For example, insurance AI exists which could remove the need for firms to manually classify documents, write contracts or process claims.
However, the most significant advantage that AI offers to insurance firms specifically stems from the way in which sophisticated algorithms can use vast datasets in order to predict and monitor risk. This would have many applications across the crucial functions of underwriting, pricing and risk management. Going further, the technology could even be used to prevent fraud by detecting tiny inconsistencies in either publicly available data or a client’s financial history.
However, AI doesn’t simply provide a competitive advantage for the forward-thinking firms who employ it, it also benefits policyholders who would enjoy cheaper premiums as a result of lower overheads and reductions in the amount of fraud.
Still, some within the industry remain apprehensive about the impact of AI on either the employees or customer base of an insurance firm. The first thing is to say that many of these concerns, particularly around data security, are legitimate but it’s important that industry leaders do not see these apprehensions as an insurmountable obstacle. Integrating AI is not about saving resources for the sake of it but rather adopting new tools with the potential to improve the industry as a whole.
I’ve been developing software for professional services companies for years and based on what I have seen, I believe that successfully integrating AI into your services boils down to three things. Understanding the limitations of both the technology and your organisation, working with developers as much as budget and time constraints allow and being critical about where and why you’re integrating AI into your company’s operations.
At Fountech, we think it’s important for firms to understand what AI has to offer the insurance industry, and so we recently released a new white paper which explores how insurers might integrate AI into their business. Ultimately, with a proper understanding of AI’s strengths and limitations, industry leaders can begin adapting their firms to the rigours of the new data-driven landscape.
As AI begins to play a central role in the functioning of insurance firms, it’s important that industry leaders remain invested in the technology’s potential to change insurance for the better. At root, this means having a sophisticated understanding of how AI can benefit your organisation but also remaining vigilant to any problems that might arise as a result.
Finally, as we move towards a more data-driven insurance industry, it’s essential that insurance firms begin playing a more active role in the development of new AI either through investment, active feedback, or by providing a breeding ground for new tools to be refined. Now is the time for insurance firms to begin playing a more active role in the development of the tools that are going to fundamentally reshape the industry over the next few years.
Machine learning and AI can have huge benefits to the financial department and could allow companies to create and tailor their models based on the data they have collated. This technology can dissect the data inputted and try to perceive the deviating patterns in this – a good example already prevalent in the financial industry is the analysis of payment behaviour in fraud detection. Machine learning is able to signal that someone is making payments from two completely different locations in a short period of time, which can indicate a fraudulent purchase. Though this is a common example of machine learning in finance, there are a huge amount of other significantly beneficial ways that AI and machine learning could be implemented – so, why are European firms not applying this as eagerly?
Well, there are a number of reasons as to why this could be the case, the most common being that there is simply a lack of know-how in this area. Accountants and finance professionals, of course, have extensive knowledge and expertise in the field of accounting standards, risk management, investment analyses and controlling, but not in the area of emerging technologies, machine learning or AI. Therefore, those in the finance department are not able to simply implement this technology and must look to external parties to help this transition – which can be timely and also a deterrent. But this is unjustified, as many CFOs could quickly master the basics of machine learning through training and not necessarily take on these roles themselves, but at least understand the technology.
Many CFOs could quickly master the basics of machine learning through training and not necessarily take on these roles themselves, but at least understand the technology.
Not only is there a lack of know-how, but there is also a lack of time for a CFO to implement this technology, or find a partner who is able to do so. A CFO’s job role usually focuses on value creation and protection, and transactional tasks too. Only once less time is spent on these is there the possibility for CFOs to focus on strategic tasks, such as implementing new technologies. CFOs tend to be extremely time-pressed individuals, until they free up time to focus on these strategic areas, or employ someone in the finance department to do so, it is likely that the option of applying these technologies will not be possible.
Infrastructure, company culture and the risk and governance surrounding implementing this technology can all have a profound effect on the possibility of companies doing so too. Not every company has the designated ICT infrastructure to store, analyse and structure data, and of course, the extra computing power and server capacity that are also required to do so. In a company where the financial department culture is not data-driven, it may be hard to convince the necessary actors of the importance of implementing data in financial practices; the management needs to support this area of focus. The risk and governance related to data issues is also a major concern for companies, whether it be related to security, GDPR or compliance, which means that many firms may be reluctant to pursue this avenue.
All these barriers that a CFO may be faced with when trying to implement data analysis and AI into their practices can, however, be overcome. Whether it is redistributing money to focus on technology in finance, employing external firms or internal actors with knowledge of the technology, or investing in software and infrastructure which can facilitate data analysis, these all are worthwhile tasks for a CFO to implement in order to benefit from this new technology.
In this pursuit to apply AI in the finance department, the CFO should continue to play an overarching role in the company, but also add advanced automation and machine learning to their list of tasks. There is a need to have employees that excel not only at accounting and financial knowledge but also at the ability to work with new technologies, including AI. A data-driven finance department will better position itself as a strategic business partner.
In this pursuit to apply AI in the finance department, the CFO should continue to play an overarching role in the company, but also add advanced automation and machine learning to their list of tasks.
In fact, there are four concrete applications of AI that could be seen currently in the finance department. For example, these technologies have the ability to quickly evaluate potential investment opportunities, by scanning and consulting annual reports and management reports of the companies on the list of their potential investments. This can help companies to quickly understand possibilities of profit growth in these investments and allow them to come to a much quicker decision on their potential investments.
Machine learning and AI can also be implemented to analyse mass social media messages regarding the company’s practices, products or services or current prices. This will help companies gather mass opinions of them in a short space of time and give them the ability to understand how to better streamline their financial services and offerings in the future too.
This technology can also predict future business issues as well, by mapping the network and history of potential suppliers and collaborators. AI can provide a specific and sophisticated understanding of a company’s public image, which could help the company avoid aligning themselves with companies with the potential to have a negative image, and therefore save them money in the long-term by maintaining their positive brand image.
Every company looks to gain insight into the profitability of its customers, this AI technology can also help companies with predicting the potential reaction to new services and products that they are looking to offer. Therefore, companies are able to understand whether or not these will be financially worthwhile in the long-term, and whether customers will be likely to consume these.
New technologies such as AI and machine learning will have a profound impact on all business areas, including the finance department, and CFOs who look to embrace this as soon as possible will be one step ahead of their competitors. For the future of finance, it is important that the training of financial students and current employees includes a greater focus on technology - how to implement this and its impact on finance. This is something that education institutions like Vlerick Business School have adapted to, offering more and more technology-focused modules in their finance programmes and ensuring that the next generation of CFOs has a strong knowledge of both accounting & finance and technology.
More than a quarter (27%) of organisations surveyed by Gartner expect to adopt some form of artificial intelligence or machine learning in their finance department by 2020. With so much data, from so many sources, machine learning is often the only real way financial professionals can successfully sift through the noise in a world of information chaos. Jonathan Barrett, Managing Director of Dataminr tells us more about it.
Before delving into the details of exactly how machine learning can benefit financial professionals, it’s important to have a clear understanding of exactly what artificial intelligence (AI) is, what machine learning is, and how the two fit together. As stated by Oxford University, AI is concerned with getting computers to perform tasks that currently are only feasible for humans. Simply, it is human intelligence manifested by a machine. Within AI exists machine learning. This is when a computer is programmed to make decisions, learn from outcomes and adjust, in a way of self-improving, according to their environment.
As an industry that needs to remain at the forefront of adopting new, and better, methods of working, financial professionals, in particular, are seeing a huge surge in the use of this technology. By bringing together multiple data sets, machine learning can take on the process of sifting through vast amounts of data and provide people working in the financial sector with greater insight to inform critical decisions. In doing so, machine learning is proving to be invaluable.
Embracing the new
A recent report from McKinsey revealed that up to 50% of tasks we tackle at work today could be automated by 2055. But this is not a reason for worry. Machine learning on its own, with no human supervision or influence, is not where the future of finance is heading. And rightly so. Rather, this new technology opens up previously untapped opportunities for a wide range of finance professionals to prepare for and excel in roles of the future -- roles that we have not yet even imagined.
More than a quarter (27%) of organisations surveyed by Gartner expect to adopt some form of artificial intelligence or machine learning in their finance department by 2020.
Machine learning is already making its mark on industries that for decades have depended on technology to automate tasks and drive efficiency. Furthermore, we are beginning to understand the true value of how this technology can be used to reduce workloads, particularly in relation to the necessary but repetitive types of work we face in many roles. When used in this way, machine learning has the real power to free up time for strategic thinking and research. It can empower better decision-making by allowing financial professionals to focus on more valuable tasks, such as business growth and retaining vital talent. In this way, machine learning technologies can prove to be the crucial advantage against competitors.
As with most technologies, there still remains limitations with machine learning, especially within finance. The financial markets -- and the data they produce -- are complex and can change within a split second. They form a web of moving parts, influenced by factors both inside and outside of the financial sectors. Anything from changes in regulation to unpredictable world events, such as political risks or natural disasters, can cause a shift in market mechanisms.
Machine learning models are perfectly capable of predicting and taking certain risk elements into account but can fall short when it comes to these kinds of uncertainties. This makes it difficult to rely solely on machine learning to provide accurate or wholly reliable information when making financial decisions, especially within the context of investment strategies. In this instance, if there is no human input, machine learning could create unwanted and unseen risks.
When used in the right way, machine learning can complement human expertise. We need the creativity, emotions and the ability to form a point of view that only humans possess. But machine learning can mitigate the more repetitive and time-consuming elements of work, enabling people to be more productive, innovative and add new value. So we shouldn’t fret that machines will take over the role of humans in the financial workplace. We instead need to look at the opportunities to enhance the power of both.
Evolving skills, evolving opportunities
For example, hedge funds are hotbeds of new methods and platforms. Traders in this area are often turning to the quantamental approach, blending algorithmic-based and human-based decision-making to generate better results. Artificial intelligence is increasingly being used to reduce unnecessary information while at the same time draw relevant data together, with alternative and unstructured data playing a prominent role.
Machine learning models are perfectly capable of predicting and taking certain risk elements into account but can fall short when it comes to these kinds of uncertainties.
In turn, financial professionals are in a stronger position to give an insightful and accurate analysis. This means they can better understand the investments they are making and the strategies they are employing.
Other financial professionals, such as traders and investors, are increasingly relying on real-time information from alternative data sources to gain new insights, assess situations quicker and enhance their decision-making. The sheer amount of data that traders are faced with is vast and overwhelming. Utilising machine learning systems that can make sense of the information chaos and at a rate simply unachievable by humans allows traders and investors to stay ahead of the game.
Machine Learning and humans: a coexistence
Machine learning is revolutionising the finance industry. On its own, it is not enough to provide an entirely accurate and reliable picture upon which pivotal financial decisions can be made. But, used in tandem with human oversight, insight, and expertise, machine learning empowers businesses and individuals across the financial sector to make faster, smarter decisions that can generate new business value or drive revenue.
About Jonathan Barrett:
Jonathan Barrett is European Managing Director at Dataminr, an AI-enabled platform that discovers, distills and alerts on activity across publicly available data sources, enabling professionals to know and act on high impact events earlier. Jonathan has over 25 years of experience in the tech industry and is passionate about transforming businesses that have the potential to change the world.
We’ve all heard of the so-called ‘war-for-talent’ within the US’s Investment Banking and Financial services field. In fact, it’s no secret that there’s an ever-increasing demand for specific and niche skills, but short supply of the requisite talent.
According to EY, over the next two to three years, machines will be capable of performing approximately 30% of the work currently done at banks: yet the ability to attract technology experts into investment banking is arguably presenting the greatest challenge for many employers.
Regulatory changes, coupled with digital advancements, mean that business models are adapting at a rapid rate. Today, automated electronic trading powered by AI and machine learning mean that the skills of the top traders of yesteryear are quickly becoming obsolete. However, the data scientists and programmers needed to drive today’s systems are in short supply. And with increasing reports of tech firms such as DeepMind, the Google artificial intelligence division, stealing top tech talent from the world of investment banking, this is only going to get more difficult in the coming months and years.
Today, automated electronic trading powered by AI and machine learning mean that the skills of the top traders of yesteryear are quickly becoming obsolete.
Furthermore, recent research from Accenture has found that just 7% of US graduates see banking and capital markets as a top industry to work for. However, by predicting both the behaviors of internal employees and market demand fluctuations, investment banks can map out a coherent plan to overcome forecasted skills gaps and bring in expertise to guarantee future growth and profitability.
Despite the clear benefits of implementing an effective strategic workforce plan, a 2014 Workday/Human Capital Institute survey of 400 HR professionals revealed that 69% consider the function either an “essential” or “high” priority, but that only 44% actively engage in it. This is not because there are not tools available – there are. Both internal data and industry trends are usually an excellent source of knowledge of individual jobs’ attrition rates, which can lead to a surprisingly detailed forecast of skills needed for the future. Technological tools can also be used to predict the likelihood of employees jumping ship, including through social media monitoring applications. So why is this disparity in numbers?
Although increasing percentages of businesses are recognising strategic workforce planning’s place within their growth plans, it can still be difficult to implement and sustain effectively. As well as needing the support of a CEO – or at least, a board member – to drive the initiative and free up resources, HR departments must also be star players in its success. This is because they can provide reliable data regarding which employees are eligible for up-skilling/re-skilling, helping to predict gaps within the workforce – although these may open and close as market demand fluctuates. In this way, the data can also be used to implement a policy of growing your own internal talent, which can subsequently help to close projected managerial gaps in the future. You can see that it is important to remember that technology is just a support tool and should not overshadow the input of your stakeholders – they also have real insight in the business’ needs.
The traditional trading desks of Wall Street in the early 80s are now well and truly a thing of the past.
One common misconception about a successful workforce plan is that it is rigid and set in stone when in fact, almost exactly the opposite is the case; what might be needed for a financial institution now may be totally different in five years’ time. Naturally, it is important to address the organisation’s most critical needs first, and not rush to implement an overarching strategy. This allows for progression and, critically, facilitates the avoidance of paying premium rates whilst trying to fill immediate skills gaps.
The traditional trading desks of Wall Street in the early 80s are now well and truly a thing of the past. But just as open outcry and hand signals have been replaced by predictive analytics and machine learning, no one knows what the future will hold for the profession. With this in mind, an effective plan must be adaptable and almost constantly fine-tuned in order to stay in line with market demand, new platforms, emerging markets and regulatory change – especially when reacting to or predicting competitors’ moves.
In fact, it is intrinsically important to keep your competition at the very front of your mind when constructing a workforce strategy. It is highly likely that you will be fishing from the same talent pool down the line, and predicting skills gaps means that your business will be able to create pipelines and contacts within these areas long before anyone is needed on board. This provides the best chance of winning the top talent – and these acquisitions can be the difference in staying a head and shoulders above the rest.
About Nicola Hancock:
Nicola Hancock has over 15 years’ experience in resourcing for financial services organisations. During her time with Alexander Mann Solutions, she has led a number of key clients globally, including RBS, Deutsche Bank, HSBC and BAML and has built extensive experience and understanding of financial services and the challenges and opportunities this brings to talent acquisition and management.
The emergence of AI has had a positive impact on the financial industry and has enhanced productivity, in particular in the accounting and banking areas. Therefore I anticipate that machine learning will definitely be a significant area of investment in the near future for this sector. However, as with any change of this magnitude, the benefits offered by the implementation of AI in the financial sector are met with a number of challenges – most notably businesses ensuring they are equipped with the right technology, staff and skills to embrace AI and automation.
Automation is now used to perform or enhance many administrative tasks, and Artificial Intelligence is already more a part of daily life than you might realise. Robotics, while commonplace in manufacturing, are beginning to show impact in other sectors. One of the key drivers behind the adoption of AI software in the financial sector is the time-saving benefits it offers users. Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries. Those tasks will be streamlined by machines, allowing workers to focus more time on complex tasks which require human touch. As well as working with advancing technologies, junior employees will be involved in more planning, reporting and analytical jobs, and as such their required skill set will change.
Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries.
Through machine learning, artificial intelligence can painlessly consume and process large amounts of data at an accelerated level. Its vast speed brings efficiency and productivity to the financial sector, and as it continues to develop and become even more efficient, it can identify more patterns than ever before, providing scope for customised offerings to customers. However, this being said, adoption of AI in the financial sector imposes many challenges to the industry. The use of AI’s ability to consume large amounts of consumer data raises questions about how this information is stored and processed and to what end. Organisations that encourage, and even mandate the uptake of these types of technologies must tread carefully. Individuals are already highly attuned to the sensitivity of their personal information and will require robust guarantees about the security of any further information they are willing to give up.
One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias. For instance, how fair can a financial system really be without human involvement? In a world where new technologies are quickly improving or even replacing existing processes, there is one area that cannot be automated, and that’s building strong relationships with clients. The human element is needed in these instances to perform certain job functions that AI is incapable of replicating. Individuals have the ability to be aware of their own emotions and those of others, but also their capability of showing empathy in the way they handle interpersonal relationships, which is known as emotional intelligence.
It’s crucial for businesses, in the fast pace of today’s world, to continue to develop and think about where their use of data can get them tomorrow, as well as where it’s got them today. Organisations must not become complacent, and instead continue to reflect on their processes, challenge routine and be future-facing in their approach to machine learning.
One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias.
Over the last two decades, technology has advanced at such a speed that many roles in the financial sector have either disappeared or wholly changed due to the implementation of AI technology. One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector. Artificial intelligence and automation can take on many of the tasks a transaction led accountant or data administrator would typically undertake, with little or no human involvement. The process is almost seamless, error-free and time efficient.
The challenge of economic survival of the financial sector is to not only accept these changes, but to capitalise on them. With any significant change in the market, there’s always a fear that it will eliminate jobs from the workforce. AI tools may well remove a number of job tasks carried out by accountants and data administrators, but rather than eradicating jobs and losing talented members of staff, employers will need to ensure that their HR directors are equipped to spot the right skilled professionals who are well versed with the latest AI technology. The HR function will also need to quell fears of job losses amongst employees and instead empower their staff to adapt and develop new skills to work alongside new technologies.
At this juncture, skilled employees are key – and we anticipate a change in the skills that businesses across the financial sector will be demanding from their employees and prospective hires. For years, Michael Page clients in this sector have been seeking candidates with financing and analysis skills; those that have a strong understanding of financial planning and reporting; people who are adept at using Excel and other such software. Our recently launched Skills Checker tool has taken the most in-demand skills for roles across the financial industry to highlight what employers are looking for today. But as we continue to see AI and automation adoption increase in the sector, we expect to see a rise in employers expanding the skill sets they require from new employees with coding and AI experience becoming ever more valuable.
One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector.
To the same end, the advent of these new technologies presents the opportunity for businesses to enhance their current workforce by equipping employees with the skills to work alongside AI and automation. A challenge in itself, such training should not be brushed off as a ‘nice to have’; it is vital for the growth, and even the survival, of a business. Employees are the lifeblood of any business, as the landscape of the financial sector changes, businesses must ensure that their workforce is keeping pace with the industry.
Before incorporating AI software into their businesses, organisations will need to think strategically about what their key objectives are and what they hope to achieve from using the technology. This is the only way they can truly expect to see any long-term benefits, through a strategic and considered approach – not simply thinking of AI as a ‘nice add-on’. It’s also important for organisations to have realistic expectations.
Businesses should start by looking at key areas where they can make an impact by using this technology on more routine tasks and go from there. This will help to build their confidence and understanding of the software over time, rather than trying to implement it all at once. Strategic thinking and patience are key here.
Although robots and AI will inevitably take a lot of the more data-driven job functions, there will be a change in how humans and machines interoperate for the highest level of efficiency and playing to each other’s strengths. The increased use of AI in the financial sector is going to spur on new innovation, and an entirely new landscape of jobs are going to emerge. Although there is always a lag between the adoption of new jobs and loss of current jobs, up-skilling and re-skilling are going to be the key to success in the future of the financial job market.
Salvatore LaScala is a Managing Director at Navigant Consulting, where he is Co-Lead of the Global Investigation and Compliance and Anti-money Laundering (AML) Practices. Mr. LaScala has over 20 years experience conducing AML and Sanctions compliance programme reviews, Risk Assessments, Monitorships and Remediations and regularly assists his financial services clients with Navigating regulatory or law enforcement actions. Mr. LaScala also applies his expertise by assisting clients with AML & Sanctions optimisation services that increases the breadth and scope of risk coverage while making the programme more efficient. He oversees Navigant’s AML Technology Team and has helped develop STAR, Navigant’s proprietary Case Management System and Rules engine regularly utilised for AML Look-Backs, Sanctions Look-Backs, CDD Remediations and other compliance and investigative projects. Additionally, Mr. LaScala provides his clients with outsourced Financial Investigation Unit (FIU) teams to both augment existing FIUs on a permanent basis or by providing FIU Surge protection services whereby the Navigant team is deployed to handle an increase in investigative or compliance activity pursuant to a compliance technology transformation or acquisition of another institution or large scale customer on-boarding.
Mr. LaScala began his career as an accountant, attorney and Special Agent with the IRS Criminal Investigations Division of the Treasury Department and thereafter spent over 20 years providing AML compliance and investigative services. He has been with Navigant since 2010.
This month, Finance Monthly had the pleasure to connect with Mr. LaScala and discuss AML in the US and the impact that AI, Machine Learning and Robotics Process Automation have had on the sector.
What drew to the AML field? What excites you about the sector you work within?
My background initially drew me in. As an accountant, attorney and former law enforcement officer, it all came together initially with a consulting job in 1997 with a Big 4 firm specialising in AML and Forensic Accounting. I enjoyed both but spent far more time in AML. I loved developing and dispositioning AML and Sanctions alerts and constantly found ways to make the process more comprehensive and efficient. Eventually I developed ways to make large scale AML remediations, including Look-Backs more efficient by building rules engines, false positive review platforms and custom case management systems. My perspectives as an accountant, attorney and former law enforcement officer helped make these technologies, auditable, regulatorily responsive and feature rich for investigators, respectively. These days I am still excited to be involved because I like working with clients, and because the regulations, financial institutions, and money launderers constantly change. It’s constant learning, which works for me - otherwise I’d be bored.
What is the current state of AML in the US?
This is a very important time for AML compliance - regulators, examiners and law enforcement now know more about AML programmes, compliance technology and payment platforms than ever before, and as such, the stakes for financial institutions regarding compliance become increasingly higher. Financial institutions are quickly adapting and upgrading their technology and overall programmes to maintain compliance and prevent and detect money laundering, terrorist financing and fraud. The ‘bad guys’ however, seem to have far more payment methods and venues at their disposal to commit crimes than ever before in history.
What are some of the key challenges you face on a daily basis and how do you overcome them?
The key challenges include finding innovative and cost effective ways to serve our clients, who are often faced with fines and expensive remediations. Providing the right breadth and depth of services to them in a cost effective way is critical. We also work for financial institutions of all different shapes and sizes, some have been through enforcement actions two or more times and are in a position to better plan their way through those actions with a great appreciation of the effort it takes. Others have either not been through too many regulatory or law enforcement actions, or are unable to communicate to a home office in a foreign country the gravity of a US regulator or law enforcement action, and don’t get the financial support they need to get through it. The challenge in both instances still becomes handling ongoing work or “business as usual work” (BAU) along with regulatory action or some compliance technology transformation. Without consultants helping, there are just not enough hours in the day. Regardless of a financial institution’s capacity to respond to a regulatory action, it’s often best if we get in there early and get them off to a timely start so they don’t also fall behind on BAU, or react to regulators too slowly, which can lead to additional issues.
What are the current AML issues and solutions affecting American businesses?
AML is constantly undergoing transformations. Some of these are based on new and emerging AML and Fraud schemes that the industry has to respond to, other transformations are due to new regulations, such as NYSDFS Part 504 regulations, which add additional layers of accountability on AML programme owners. Still, other transformations are the result of enhancing the regulations and the technology behind it because every time we close a door on money launderers and fraudsters, they both seek out institutions without robust compliance and find new venues through which to launder money. The US and several other markets are attractive to money launderers, fraudsters and terrorists because the financial services industry is vast and because these markets are segmented. This means that some players in capital markets or money service business spaces are very technologically savvy with respect to compliance, while other smaller players in the same segment are not. In fact, we often see challenges where the larger and more sophisticated financial institutions de-market or close customer/client accounts which later pop up at smaller or less sophisticated financial institutions.
How has the introduction of Artificial Intelligence, Machine Learning and Robotics Process Automation impacted compliance and investigative solutions?
Navigant is highly focused on applying Artificial Intelligence (AI), a form of Machine Learning (ML) and Robotics Process Automation (RPA) to our clients in many different areas, including AML and Sanctions. For AML example, we believe that AI/ ML can help existing AML Transaction Monitoring Systems deliver enhanced detection scenario parameters by grouping behavioural patterning to cover more risk and produce fewer false positives. Concurrently, we are applying RPA to the expedite portions of the dispositions of such alerts by removing mundane rote tasks from the analysts purview so that he/she is spending more time on considering the facts, CDD, news and current transactions to determine whether the transaction should be filed on, and less time hunting for data and writing the disposition. Specifically, AI/ML, which helps increase coverage and reduce false positives, and RPA which provides the Investigator more time to analyse the actionable items, are remarkably powerful together.That said, there is a fair amount of work to do, and in the beginning, we need to focus AI/ML only on matters for which the data feed is clean and comprehensive and apply it in a way that is transparent and can readily be described to regulators, examiners and internal audit. The AI/ML revolution won’t survive if the providers that developed it and the financial institutions that use it are not completely transparent. Moreover, even RPA will be better received if it is introduced in stages and when implementations are accompanied by statistically valid data showing that it is more accurate, and saves time such that the ultimate work product contains more thoughtful analyses and is generating comprehensive filings useful to law enforcement.
Technology advances have changed every aspect of financial markets. For consumers, this transformation has made financial services more affordable, accessible and tailored to our individual needs. For financial institutions, digital tools, including emerging technologies such as artificial intelligence (AI), robotics and analytics, have delivered huge opportunities to radically improve the efficiency and effectiveness of risk management, while reducing costs and better meeting the needs of customers.
However, these advances have also raised fundamental questions around how regulation should adapt. For an industry still finalizing reforms introduced after the global financial crisis, financial technology and innovation present a new round of challenges. That’s why it’s time for financial institutions and regulators to ask: How can we build a regulatory environment fit for a digital future? Below Kara Cauter, Partner, Financial Services, Advisory Ernst & Young LLP UK, answers the hard question.
Technology’s potential to make financial markets safer
It’s inevitable that new technologies introduce new risks, and new twists on old risks, as well as different ways of working. Systems can fail and undermine market stability; machines can make decisions with unintended consequences that harm customers and markets; and the almost limitless data that is the lifeblood of the digital world can be manipulated, misused, stolen or inadvertently disguise criminal behavior. But new technologies also offer significant opportunities to improve risk management and enhance the efficiency, safety and soundness of markets and convenience to consumers.
As a result, financial services firms are constantly tapping into new tools to improve the customer experience and strengthen risk management and compliance:
Regulators are also exploring how to use technology in their role:
Time to ask new questions about old risk principles
But despite positive moves to deploy technology to improve the security and efficiency of global financial markets, it’s still early days. Both industry and regulators are struggling with fundamental questions around how to identify and describe the risks posed by new technologies and new ways of doing business.
Delivering regulatory answers fit for a digital future will call on all market participants to revisit old principles, ask new questions and work together. Building a transparent, balanced, and connected risk management ecosystem will require:
Ultimately, as regulators and market participants navigate the FinTech landscape, they’ll need to consider how to best use and regulate the use of digital tools to deliver effective risk management and compliance – without stifling the innovation that can help deliver better and secure financial services.
CFOs no longer rate Excel as most important skill, turning to new technologies, automation.
Adaptive Insights recently released its global CFO Indicator report, exploring finance automation progress and expectations of CFOs. The survey reveals that CFOs are embracing automation across various areas of finance, driven in large part by a requirement to be more strategic and provide better analyses. Financial reporting and period-end variance reporting top the list of automated processes today, according to the survey.
Automation initiatives are also impacting required skills for finance professionals. Whereas two years ago, 78 percent of CFOs considered proficiency in Excel as the most important skill for their FP&A teams, only 5 percent feel the same today. Looking ahead, only 7 percent of CFOs list better Excel skills as important for new hires. Instead, CFOs rated the ability to be adaptable to new technologies as the top skill for new hires, signaling a shift in desired skillsets for finance professionals in the future.
“We’ve seen CFOs increasingly take on the role of chief data officers in their organisations,” said Jim Johnson, CFO at Adaptive Insights. “At the same time, CFOs recognise the limitations in the way they manage and analyse data today and know it will only get worse with the proliferation of more systems with siloed data. That’s why Excel skills aren’t ranked as a top skill any longer. Proficiency in Excel is a given today. The new skills finance leaders need are those that can use technologies to access, analyse, and amplify data for insights to better manage the business.”
Limitations with manual processes like spreadsheets were recently documented in a Wall Street Journal article, Stop Using Excel, Finance Chiefs Tell Staff. The article noted that ubiquitous spreadsheet software that revolutionised accounting in the 1980s hasn’t kept up with the demands of contemporary corporate finance units, citing a lack of automation.
(Source: Adaptive Insights)