Maybe more than any other year before, in 2017 artificial intelligence will be one of the hottest topic in finance. Machine learning is redefining processes in financial institutions and challenges some of the decade-old business models. Wealth management companies are using deep learning solutions for long-term value investing, advisors are being replaced by chatbots, covering up to 95% of all queries, and companies like PayPal are able to keep fraud losses below the industry average by scanning transactions largely on artificial neural networks.

This article will outline essential developments in machine learning, describe successful use cases in finance and spillover effects from e-commerce to finance, as well as tackle some of the most common misconceptions about artificial intelligence. It is by no means an exhaustive overview of AI in finance, but constitutes a contribution to understanding machine learning, the factors that drive its success, and an appeal to reason about the future of AI in finance.

A Short Overview of Machine Learning Algorithms

Machine learning is the key to all processes that enable machines to generate knowledge from experience. This means that computers are learning from data without being explicitely programmed where to look. Hence, machine learning is used for classification, regression, clustering, recommendation systems, anomaly detection, and dimensionality reduction. Businesses apply machine learning to uncover hidden patterns in historical data as basis for reliable decision-making, a field which is generally known as predictive analytics.

There are many different machine learning algorithms for classification and prediction. The algorithms can be either categorized by learning style (supervised learning, unsupervised learning, reinforcement learning), or by similarity in form or function. I will skip the differentiation by learning style and go ahead to an overview by form / function. You can find the basic concepts of machine learning styles on Coursera lessons by Andrew Ng, including supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), and unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

While the categorization into learning styles is relatively easy to understand, it is a bit more difficult to structure machine learning algorithms by similarity or function. Originally published by Jason Brownlee in 2013 the following chart offers a helpful overview of the most common algorithms. The list is not exhaustive, but representative and helpful to get an idea of the landscape of machine learning algorithms. A longer and more detailed version can be found on machinelearningmastery.





A common misconception about Artificial Neural Networks is that they achieved remarkable commercial success only in recent years

Machine learning has been around for decades already, and despite a commonly-held belief, there have been very successful applications of artificial neural networks since the late 80's, especially in the field of finance, such as in stock-market prediction in 1990, for ATM communications network control in 1990, in the prediction of bank bankruptcy in 1991, in the prediction of stock-price performance in 1993, or the commercial deployement of ConvNet-based systems for reading the amount on bank check automatically. In contrast to today, the amount of digital data was much smaller, as well as computing power has been slower and more expensive.

Fortunately, both has changed significantly during the last years (and the field of deep learning is able to fulfill some of the promises made in the 80's about neural networks). First, the amount of digitally available data is growing exponentially and will reach 44 ZB in 2020 up from 4.4 in 2014. In this context, many experts use the term 'data is the new oil'. Clive Humby, UK Mathematician, has coined the term in 2006. Interestingly, AI researcher Neil Lawrence, a professor of machine learning and computational biology at the University of Sheffield who recently joined Amazon as leading machine learning scientist, diagrees. He explained at the Re-Work conference on Deep Learning in London that instead data is the new coal, because machine learning is most successfully applied where market participants of machine learning techniques already have much data, such as Google, Facebook, Amazon, and Microsoft. (As an economist I feel obliged to point out that from a strictly economic point of view, the analogy is incorrect. Data is neither coal nor oil: Data is non-rival, because its consumption by one consumer is does not prevent simultaneous consumption by other consumers, but it is excludable by those who own data. Hence data is actually a so-called 'club good'. Coal and oil on the other side are 'private goods': they cannot be consumed by other consumers at the same time. Data is more like a non-monetary currency.)

The second factor that pushed machine learning was technology advancement which led to powerful and efficient parallel computing especially provided by GPU computing. Those GPUs allow that extraordinarily large data sets are being generated faster than ever with less datacenter infrastructure. They are especially used to train deep neural networks for classification and prediction in the cloud. And we can expect that machine learning will become even deeper and cheaper due to supercharged hardware. A very important market player is NVIDIA with their GPUs. Previously, AI researchers used CPUs to process large data with machine learning models. However, CPUs were expensive and researchers needed lots of them for machine learning applications with large data sets. In 2011 Andrew Ng, who is one of the leading deep learning researchers and was at that time working on the 'Google Brain' project, teamed with Bryan Catanzaro from NVIDIA Research in Stanford in order to find a cheaper and faster alternative. They found that 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs. AI researchers worldwide, such as the Swiss AI Lab, the University of Toronto and the NYU, quickly followed which particularly boosted the market for deep learning applications.

Furthermore, machine learning researchers nowadays can apply certain 'tricks' that weren't available in the past, such as different types of spatial pooling, 'Dropout' as a form of regularization for fully connected neural network layers, rectifying non-linearity for the units, and more. Additionally, open source is in machine learning a very important driver that helps to increase the diffusion of AI.

Speaking of neural networks, another common misconception about neural networks is that they would be models of the human brain. They are at most inspired by the human brain. Andrew Ng points out that a neuron in a human brain is a very complex machine that scientists today still don't understand, meanwhile 'a neuron in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron'.

Why is Deep Learning so popular?

Within machine learning, the field of deep learning has attracted particular attention in recent years. From AlphaGo by Google DeepMind, that defeats a human in the first game of Go contest, over DeepText from Facebook, that analyses the text you post on the social media network, to DeepBach, a platform that uses deep learning to create new classical music in the style of Bach. Everything around us seems to go 'deep'. While there is a certain level of hype going around at the moment, there are also lots of successful commercial applications of deep learning, such as Youtube in their recommendation system, or Spotify, who is using deep learning because those convolutional neural network overcome content-agnostic traditional collaborative filtering methods and provide much better music recommendations, or in Google translate, as well as in cancer detection, and in autonomous cars. But what actually makes deep learning so special?

Deep learning models, such as convolutional neural networks (CNN) can handle extraordinary large data sets, including unlabeled data, and - if necessary - by applying the so-called unsupervised learning style on deep neural networks. Consequently, deep learning is particularly useful in areas, where large amounts of unstructured data need to be processed. Deep learning really shines in complex fields like image classification, natural language processing, and speech recognition. It is much more powerful than other machine learning algorithms which is why deep learning is currently electrifying the entire computer industry and also attracting more attention in other industry such as automobile, medicine and even finance.

'The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms' - Andrew Ng

From an economic perspective, what makes deep learning so interesting is its more advantageous economies of scale in contrast to other machine learning algorithms, as shown in the chart below. The marginal utility of additional data is still positive where it already diminishes with other machine learning algorithms. Though it seems reasonable that the marginal utility decreases with more data, it is unclear at which point it equals to the marginal cost and wouldn't be efficient anymore. In any case, deep learning still produces a marginal utility with more data where other machine learning algorithms are already unprofitable. This makes it so interesting for academic research and for commercial applications.

How many hidden layers do you need to call it Deep Learning?

In this context, people often ask at which depth shallow learning ends, and deep learning begins. Shortly, there is no common number of layers to declare the beginning of deep neural nets. Yann LeCun, director of AI research at Facebook, considers any learning method that can train a system with more than 2 or 3 non-linear hidden layers. In contrast, Jürgen Schmidhuber, co-director of the 'Dalle Molle Institute for Artificial Intelligence Research' (IDSIA) in Switzerland, does not commit himself to a precise number, and instead defines problems of depth with more than 10 layers as 'very deep learning'. Furthermore, he underlines that the depth of a neural network is not necessarily related to the difficulty of a problem.

Wherever the limit is, its number is growing strongly: the current maximum is 152 layers, as Kaiming He, Research Scientist from Facebook AI Research (FAIR) points out in his recent paper 'Deep Learning Gets Way Deeper. Recent Advances of Deep Learning for Computer Vision'. This is however an exceptional use case. Today, most artificial neural network include six or seven layers, and a few might extend to 20 or 30. But the depth of 152 layers demonstrates how large and powerful deep learning architecture can become.

Moreover, the number of parameters increases as well. In a recent study, Geoff Hinton, professor at the computer science department at the University of Toronto, and his collegues were able to build a neural network with a billion parameters. A new kind of layer, the so-called 'Sparsely-Gated Mixture-of-Experts' (MoE) enables training extremely large models without increasing computation to deliver high quality language modeling and translation results.

If this sounds to abstract for you, just tinker yourself with a neural network on this amazing example from Google to get an impression of how they work. It runs on the open source software library TensorFlow.

Machine Learning Is More Than Just Deep Learning

The records in number of parameters and hidden layers, that deep convolutional neural nets and recurrent nets are setting, can sometimes outshine the massive amounts of data and computational power the rely on. There are many problems that can be solved with either fewer data or simpler machine learning methods. Deep learning is not an efficient approach to all problems, not even in finance.

For example, product recommendations or personalized feeds use collaborative filtering and can be efficiently built on smaller data sets of tens of thousands of data and not necessarily millions. Neil Lawrence points out, that in these cases more conventional computational statistical methodologies, such as logistic regression, decision trees, support vector machines, or k-nearest neighbor are perfectly fine or will even lead to better results (consider overfitting, that can arise with larger data sets and poses a major problem in predictive analytics). Machine learning methods should be chosen wisely, and deep learning is - despite its current hype - only suitable for very specific use cases where extraordinarily large data sets are necessary.

As you can see, machine learning methods are providing a very rich and powerful set of techniques for pattern analysis, classification and prediction. That makes them so interesting for many different industries. Now let's turn to some commercially successful applications of artificial intelligence technologies in finance.

The Hype & Hope of Chatbots & Virtual Assistants in Finance

One of the most popular applications of AI in 2016 have been chatbots and virtual assistants. Google announced their new chatbot Google Allo, Facebook presented a messenger platform (beta) for chatbots, Samsung acquired Viv, and Amazon announced a 900 percent year-over-year increase in Echo sales this holiday season with more than five million Alexa-enabled smart speakers sold.

Financial institutions joined this trend as well. Banks already bet on financial chatbots as the next big thing. Certainly, the most attraction gained Kasisto, whose conversational AI platform KAI had been implemented into the Indian digibank and already takes care of 95 % of all queries. It has been previously trained on millions of investing and trading interactions. Royal Bank of Scotland recently published RBS Luvo, an AI chatbot to help customers. The Swedish SEB introduced Amelia (in cooperation with IPsoft), a bot for their 1 million customers that has been internally applied among 15,000 employees in order to ensure a solid implementation. Bank of America launched the chatbot Erica for digital banking. Mastercard announced at Money2020 its plans to launch an entire AI bot platform where customers are able to transact, manage their finance, and shop via messaging platform.

However, not everyone is convinced that chatbots will impact finance anytime soon, since most of them simply don't hold their (admittedly high) expectations. A common criticism is that bots could barely keep up their side of a conversation. The problem behind is that many chatbots have actually very little or no artificial intelligence, at least in the way consumers would expect it. Bots need to understand complex requests, learn from error and improve over time. Even though many financial chatbots still don't meet those minimum requirements, they are rapidly catching up. And deep learning is in the center of making chatbots intelligent. Instead of writing time-consuming rules for each human request, machines with deep neural networks learn how to understand language. The creators of Siri impressed earlier in 2016 when their new AI assistant Viv was able to handle complex queries.

The technical progress is particularly relevant in speech recognition, because voice is much more difficult than text. E-commerce platforms invest heavily in its development, because they consider the benefits of voice assistance much higher than its technical obstacles. Back in 2013, Google reached an accuracy of 80 % in word recognition. Two years later, Baidu, the Chinese platform where Andrew Ng is currently leading researcher, reached an accuracy rate of 96.3 % with its deep learning platform 'Deep Speech 2'. The pace of improvement in accuracy, followed by latency is highly relevant for the commercial success of voice assistants. Or in other words: no one wants to wait 10 seconds for an answer, or repeat the same question five times.

Voice assistants, such as Amazon's Alexa, will be nothing less than the next major operating system, like Android is for mobile phones, or Windows was for desktop PCs. Even though voice commerce is orders of magnitude more complex then web commerce, voice-first technology is expected to completely change advertising as we known it. A reason why Google, Amazon and Baidu are leading in this field.

Millennials prefer texting over voice. But that doesn't mean that voice assistants are less relevant.

Technological progress, especially based on deep learning methods, will solve the current problems of chatbots. But managers in financial institutions often criticise voice assistants on a very profound level: the younger generation wouldn't use them. Voice assistance would be more of a gimmick to show off innovation than to actually solve any customer need. And research seems to support this: A recent study revealed that a whopping 75% of millennials prefer texting. In contrast to previous generations, phone contacts among millennials are the least preferred option, as shown in the chart below.

But that doesn't necessarily contradict: it depends on the use case and the environment. There are many situations in which voice is much more convenient than other means of communication. And people will weigh up the pros and cons in different situations for different tasks. For example, making a bank transaction or checking your account state by voice that uses biometric recognition is far more convenient and faster than typing and generating mTAN numbers. But customers will differentiate between home, car, or sitting in the train. This is supported by market analysis: A KPCB study revealed that in the U.S., 43% use voice assistants at home, 36% in the car and in contrast only 19% on the go and 3% at work. Furthermore it can also make sense to differentiate into pre-sales and aftersales services, and test use cases against each other.

My guess for 2017 is that natural language processing for general-purpose assistants will still disappoint due to the lack of an understanding the actual meaning of language. Businesses should instead focus on developing applications that help customers with narrow and niche artificial intelligent chatbots based on machine learning and complex lexicons. Like verticalized AI in form highly specialized agents in their field. The era of an 'Gandalf for finance' that guides customers through their whole personal finances covering several fields including smart wallets, investing, loans, and insurances will come, but in 2018 or maybe even later. However, people will definitely become more comfortable using voice assistants, even the millennials. Alexa or Siri are a good start to easily test those virtual assistants on the own client base.

Artificial Intelligence in Wealth Management

Portfolio optimization is another area where machine learning technologies are expected to revolutionize the current processes. Quantitative hedge-funds, such as Renaissance Technologies, or Two Sigma, want to outsmart the market with AI-powered trading decisions. Aidyia, a hedge fund based in Hong Kong, makes all stock trades using artificial intelligence and without any human intervention. The engine analyzes large amounts of data, including market prices, volumes to macroeconomic data, and corporate accounting documents to create predictions and take a decision on trading and investment strategy.

'If we all die, it would keep trading' - Ben Goertzel, Chief Science Officer at Aidyia

Sentient Technologies, a San-Francisco based start-up, is combining deep learning and large-scale distributed computing to identify the most successful investment strategies. Euclidean Technologies, a money manager from New York, is combining deep learning and value investing to uncover hidden patterns in large-scale unstructured data for investment decisions. Investment management platforms, such as Walnut Algorithms, are focused on applying advanced machine learning methods for absolute return quantitative investment strategies.

The wealth management platform Quantenstein from Acatis Investment in Germany is using deep learning solutions for long-term value investing. Quantenstein applies deep learning to assemble and optimize client-specific investment portfolios, based on large quantities of data for a given investment universe, such as regions, industries, or market cap categories, and set of constraints, like portfolio size, dividend yield, holding period, transaction costs, ESG criteria. Given the complexity of deep learning in portfolio optimization, Quantenstein is collaborating with nnaisense, a company that was founded by Jürgen Schmidhuber, one of the leading AI researchers in the world. (He invented in 1997 along with Sepp Hochreiter the Long-Short-Term-Memory (LSTM) for neural networks, which practically gave AI the ability to 'remember', and is nowadays used for speech recognition and other complex tasks.)

Algorithmic trading is nothing new for investment managers and hedge funds - roughly 9 percent of all funds with an asset under management of about $197 billion are using machines to create large statistical models build by 'quants'. Moreover, around 40 percent of hedge funds that went public in 2015 rely on computer models for their decisions. However, those models are often static, and don't perform as well anymore when market changes. Human intervention is necessary. This is why quants are moving further towards more complex machine learning models with larger data sets. Others argue that the line is blurred between the algorithms, and as pointed out earlier, many machine learning techniques are already applied in finance since decades, which is why most of those AI-based systems are maybe more conventional than they seem, and an AI revolution in finance isn't imminent.

Call it a revolution or not, more complex machine learning methods will be applied in wealth management and take over repetitive tasks. And even though hedge funds and wealth management platforms embrace machine learning, there is still a role for humans. But this role, especially of portfolio managers and quants, will change significantly in the next years. On the one side, their tasks are shifting more towards a data scientist and engineer role, maintaining the system, feeding it and stepping in when unexpected scenarios occur the machine is not able to cope with. Everything in between will be automatized by machine learning algorithms. On the other side, humans will also play a greater role in explaining and selling the decisions the machine has taken, because to some degree the finance industry is just like the entertainment industry: bad decisions can be made in investment banking and portfolio management, but there is a great difference if a human can explain it and reason about alternatives, or just point to a machine.

Artificial Neural Networks for Automated Fraud Detection

Another popular branch in finance, where machine learning algorithms are heavily applied, is fraud detection. As previously mentioned, PayPal was able to keep fraud losses below the industry average by applying AI. Given its size - PayPal generates $10,900 in payments every second, and handled 4.9 billion payments in 2015 for 188 million customers worldwide - the company has a strong reason and the scale to use deep learning for scanning transactions on artificial neural networks. In contrast to conventional linear models, that consume 20-30 variables, deep learning can process thousands of data points, which provides the possibility to analyze much more data in size and in sophistication.

'There's a magnitude of difference — you'll be able to analyze a lot more information and identify patterns that are a lot more sophisticated' - Hui Wang, senior director of global risk and data sciences at PayPal

Traditional algorithms are rule-based, and they do work at first. But when fraudsters change their tactics, you need to add rules, and interaction mayhem ensues. With learning algorithms, such as deep learning, you just add features and retrain the system, which will be able to follow the changed tactics by fraudsters. However, that doesn't mean applying deep learning for fraud detection is simple: sample features for fraud detection is hard to determine. It can include unusual regions for a card, unusual time-of-day, the charge size relative to previous average of merchant or card, or an address verification system mismatch.

The underlying technique is also used in other industries that operate with payments: Orange, a French telecom operator is testing deep learning software from Skymind to help it identify fraud. Skymind provides an open source enterprise distribution to build production-grade deep learning solutions. It is the company behind Deeplearning4j, the only commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Open source plays a great role in fraud and anomalies detection. DeepDetect, an open source deep learning server is used by Microsoft, Airbus, and others for the analysis of bulks of unlabeled data to detect anomalies. Interestingly, DeepDetect has taken inspiration from the paper on character-based ConvNets for text classification by Yann LeCun and his collegues.

Legal problems still represents one of the most difficult challenges for financial institutions. But another common misconception is that neural networks are black boxes.

The headline isn't actually 100% accurate. But it is important to differentiate. Neural networks are black-boxes by themself. This constitutes a problem for companies, that use neural networks and want to explain how individual decisions have been made. Especially in case of suboptimal or even bad decisions (imagine a car company with an autonomous driving system that cannot explain why the car hit a human).

E-commerce and entertainment companies can deal with mistakes in their neural-network-based recommendation system. If Netflix recommends one movie that doesn't really match the customer's preference, or Spotify one 'wrong' song, or Youtube one 'wrong' videoclip, the consequences are quite small and the company isn't liable. But just like in the automotive industry, it is a total different case for financial institutions. Imagine fund managers that aren't able to assess the risk of trading strategies, or cannot understand the reason for certain trading decisions executed by neural networks, or banks that aren't able to explain individual credit ratings or portfolio recommendations. The finance industry might sometimes act like an entertainment industry, but before the law it isn't. There are strong regulatory requirements for financial institutions to justify their assessments, recommendations and actions.

However, that doesn't mean that there are no solutions. Data scientists have developed so-called rule-extraction algorithms that can extract knowledge from the neural networks. These are mathematical expressions, symbolic logic, fuzzy logic, and decision trees. So-called neuro-fuzzy systems are applied in fields where probability and propositional logic meet. Decision trees can be extracted from neural networks with decision tree induction. Mathematical rules extract multiple linear regression lines from neural networks. And propositional logic is for operations on discrete valued variables.

A final appeal to reason: The lack of diversification in AI research represents a more urgent issue in AI than the (hypothetical) threat of an extinction of humanity by machines

With increasing computer power and technological progress, machine learning experts discuss if we reach anytime soon the 'singularity'. Vernor Vinge, a mathematician, is thought to have coined the term 'the singularity' that describes the inflection point when machines will outsmart humans. He stated in the 1993 essay 'The Coming Technological Singularity: How to Survive in the Post-Human Era' that the physical extinction of the human race is one possibility. Stephen Hawking warns that full artificial intelligence would spell the end of human race, because AI would take-off and redesign itself, and humans would be superseded. Elon Musk added that full AI is the human race’s biggest existential threat, and called for the establishment of national or international regulations on the development of AI. But how probable is this all, and when will we reach the singularity?

'In 50 years, you will have a small device for the same price that can compute as much as all 10 billion human brains of all human kind together – and there won't be just one small device but many of them.' - Jürgen Schmidhuber

First of all, futurists and AI experts have a very different idea about the point of technological singularity. The time ranges between the next 30 to 1,000 years, with a median value of 2040, when artificial general intelligence (AGI) will rise.

Second, some researchers, such as Danko Nikolic, a neuroscientist at the Max Planck Institute for Brain Research in Frankfurt, generally doubt that an AI will ever exceed human intelligence but at most asymptotically approach it.

Third, and most important, some AI experts scrutinize the whole assumption that hyperintelligent machines would want to wipe out the human race. Jürgen Schmidhuber underlines that humans are more intelligent than their pets, but don't erase them either. On the contrary, machines and humans would be living in complete different ecosystems: for machines the outer space is a much more suitable place than earth, whereas for humans it is the other way around. Machines will most likely colonize outer space, according to Schmidhuber.

Given the current challenges in AI development, it seems a much more urgent question to discuss which role a lack of diversity in AI research is playing. Machines are learning from data, and the data sets are created by computer scientists. What happens when most of those researchers are men? If the input data are biased, the out-coming results are biased as well. Machines based on artificial intelligence can pick up bias from human creators, and learns prejudice. This is very relevant, because machine learning and AI are conquering more and more aspects of human life, and hence are being deployed on every aspect of industrial and personal needs. In a recent study, Xiaolin Wu and Xi Zhang, researchers at China’s Shanghai Jiao Tong University, trained a machine-learning algorithm to identify convicted criminals based on specific facial characteristics. The paper got highly criticized, because 'it relies on this system as the ground truth for labeling criminals, then concludes that the resulting is unbiased by human judgment'.

Consequently, more and more AI scientists, such as Fei-Fei Li, professor for computer science at Stanford University, make the case for an emphasis on ethnic and gender diversity in artificial intelligence. If businesses create AI-based products for their customers, they need to look for diversity in their machine learning and data science teams. A cultural change is in many companies still one of the most important requirement to be commercially successful with machine learning solutions.

For further information about an successful application of artificial intelligence technologies, the 'Harvard Business Review' recently published an article about 'How to make your company machine learning ready'. A must-read, especially considering the pace of innovation and time to market of machine-learning-based services, that will accelerate in 2017. In particular, the finance industry will see many more amazing new innovations in the upcoming year.