Building Smart Capital Market with Artificial Intelligence and Machine Learning

Preliminaries

In capital and securities markets, the stakes have been raised for participants to establish value, win loyalty, and expand their share of wallet. Data analytics capabilities, combined with artificial intelligence (AI) and machine learning (ML), can open new opportunities in these areas. Without the ability to accurately predict business outcomes with the help of AI, market makers are left to rely on hunches and educated decision-making when predicting the unknown. Firms are increasingly recognizing the benefits of technology, and partnering with modern tech providers is key to realizing those benefits. But challenges still exist for capital markets looking to deploy ML at scale. The global AI in the Fintech market was estimated at USD 7.91 billion in 2020, and it is expected to reach USD 26.67 billion by 2026. The market is also expected to witness a CAGR of 23.17% over the forecast period (2021 - 2026). Artificial intelligence improves results by applying methods derived from the aspects of human intelligence but beyond the human scale.

The Securities and Exchange Board of India (SEBI) is planning to invest in a major way to upgrade its technology in order to boost its capability for market surveillance, investigations and policy-making. In the forthcoming years, SEBI will be implementing major information technology (IT) projects, which are critical to its day-to-day operations and its mandate. SEBI is planning to implement various analytical models based on artificial intelligence and machine learning as well as rule-based algorithms. The regulator is planning on implementation of analytics based on unstructured data on its Data Lake platform.

How Artificial Intelligence (AI) / Machine Learning Works

How Artificial Intelligence (AI) Works

Artificial intelligence (AI) is a concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector. Building an AI system is a process of reverse-engineering human traits and capabilities in a machine and using its computational expertise to surpass what humans are capable of.

To understand How AI works, one needs to deep dive into its various sub domains and understand how those domains could be applied into the various fields of the industry.

Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data.
Deep Learning: Deep Learning is a ML technique. It teaches a machine to process inputs through layers to classify, infer and predict the outcome. Few of Deep Learning Algorithms are Convolutional Neural Network (CNN), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Stacked Auto-Encoders. Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN).
Neural Networks: Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does. Most frequently used Neural Network Algorithms are Perceptron, Multilayer Perceptrons (MLP), Back-Propagation, Gradient Descent (Stochastic), Hopfield Network
Natural Language Processing (NLP): NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly. Key NLP Algorithms are Support Vector Machines, Bayesian Networks, Maximum Entropy, Conditional Random Field.
Computer Vision: Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations. Few of Computer Vision Algorithms are
SIFT and SURF for feature-point extraction. Used for object recognition, Image registration.
Viola-Jones algorithm, for object (especially face) detection in real time.
'Eigenfaces' approach, using PCA for dimension reduction. Used in face recognition.
Lucas-Kanade algorithm for optical flow calculation. Used for tracking, stereo registration.
Mean-shift algorithm for fast tracking of object.
Kalman filter, again for object tracking, using point features for tracking.
Machine learning algorithms like SVM's, KNN, Naive Bayes, etc. are also important in computer vision.
Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analysing text/speech/images/objects in a manner that a human does and tries to give the desired output. Few popular Cognitive Computing Tools are SparkCognition (SparkPredict, SparkSecure and MindFabric etc), DeepMind. IBM Watson. Expert System. Microsoft Cognitive Services. Cisco Cognitive Threat Analytics.

Arising from these approaches, artificial intelligence most frequently gets categorized into the following concepts:

Artificial Narrow Intelligence (ANI): A branch of AI that excels in performing singular tasks by replicating human intelligence, and AI’s basic concept. This type of knowledge is found in speech recognition systems and voice assistants.
Artificial General Intelligence (AGI): AI whose purpose is general and whose efficiency can be applied to diverse tasks. This type of artificial intelligence can improve itself by learning and is the closest to the human brain in terms of capacities.
Artificial Super Intelligence (ASI): Exceeding human intelligence, this AI concept is way more sophisticated than any other artificial intelligence system or even a human brain. The main trait of ASI is that it can contemplate about abstractions of which humans are unable to think.

5 Pre-Made AI Tech Tools, Frameworks and Templates

TensorFlow - TensorFlow is an open-source software library that was developed by Google Brain Team. It has a flexible architecture allowing developers to “deploy computation to one or more CPUs in a desktop, server or mobile device with a single API,” although this library itself provides multiple APIs.
Caffe - Caffe is a framework for creating deep learning systems. It was developed by Berkeley AI research team and its primary focus are networks applied to computer vision. By design, this tool maintains innovation and application because its models are configured without hard coding. Caffe also fosters active development, quick research experiments and industry deployment (Caffee can process 60 million pictures at a daily level).
Neuroph - This open-source tool is used for creating artificial neural networks. It contains Java’s class library and with its easyNeurons tool, it can facilitate the creation and training of neural networks. Its GUI neural network editor is very convenient, and developers can use it to create their own neural network components. The neural networks conceived through Neuroph have artificial neuron layers, neuron connections, transfer function, input function, learning rule and more. This tool also has its own support for image recognition.
Apache SystemML - This framework develops systems that are capable of machine learning using Big Data. SystemML was created by IBM and renowned for its flexibility and scalability. It allows multiple execution modes, customization of algorithms and optimization based on data and cluster characteristics. The additional levels of deep learning deployed include GPU capabilities, importing and running neural networks and more.
Torch - Torch is an open-source machine learning library based on LuaJIT programming language. It boasts many algorithms and flexible tensors for indexing, resizing, cloning, and sharing storage, other features. With a top-notch interface, linear algebra routines, neural network models, efficient GPU support and embeddable nature, Torch is used by Facebook AI Group, IBM and Yandex, among others. Its subset, PyTorch, is an open-source machine learning library for Python and can be used for natural language processing.

How does Machine Learning work?

ML is an application of AI that can automatically learn and improve from experience without being explicitly programmed to do so. ML occurs as a result of analysing ever increasing amounts of data, so the basic algorithms don’t change, but the code’s internal weights and biases used to select a particular answer do.

The three major building blocks of a Machine Learning system are the model, the parameters, and the learner.

Model is the system which makes predictions.
The parameters are the factors which are considered by the model to make predictions.
The learner makes the adjustments in the parameters and the model to align the predictions with the actual results.

Machine learning uses two main techniques:

Supervised learning allows to collect data or produce a data output from a previous ML deployment. Supervised learning is exciting because it works in much the same way humans learn. In supervised tasks, the computer is fed with a collection of labelled data points called a training set. The most widely used learning algorithms are Support-vector machines. Linear regression. Logistic regression. Naive Bayes. Linear discriminant analysis. Decision trees. K-Nearest Neighbour algorithm, Multilayer perceptron
Unsupervised machine learning helps one find all kinds of unknown patterns in data. In unsupervised learning, the algorithm tries to learn some inherent structure to the data with only unlabelled examples. Below is the list of some popular unsupervised learning algorithms: K-means clustering, Hierarchal clustering, Anomaly detection, Principal Component Analysis, Independent Component Analysis and Apriori algorithm. Two common unsupervised learning tasks are clustering and dimensionality reduction.
In clustering, data points are grouped into meaningful clusters such that elements within a given cluster are similar to each other but dissimilar to those from other clusters. Clustering is useful for tasks such as market segmentation.
Dimension reduction models reduce the number of variables in a dataset by grouping similar or correlated attributes for better interpretation (and more effective model training).

Python is the best programming language for ML Learning applications as it has rich in-built libraries. Other programming languages that could use for Machine Learning Applications are R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala. There are different packages for a different type of applications, as mentioned below:

NumPy (Scientific Computing in Python), OpenCV (Real-time optimized Computer Vision library, tools, and hardware), and Scikit (Machine Learning Library for the Python) are used when working with Images.
NLTK (Natural Language Toolkit) along with NumPy and Scikit again when working with text.
Librosa (Python package for Music and Audio Analysis) for audio applications
Matplotlib (Visualization with Python), Seaborn (Python data visualization library based on Matplotlib), and Scikit for data representation.
TensorFlow (Open-source machine learning platform) and Pytorch (Open-source machine learning library for Computer Vision) for Deep Learning applications
Scipy (Open-source Python library) used for scientific computing and technical computing
Django (Python Web framework) for integrating web applications
Pandas (Python programming language for data manipulation and analysis) for high-level data structures and analysis

Artificial Intelligence (AI) in Capital Market and Securities Industry

Artificial Intelligence brings a plethora of possibilities for all industries and as technology continues to advance rapidly, it is driving innovation in exciting ways. Within capital markets, the application of artificial intelligence is gaining broader acceptance, and the value it brings to organizations is beginning to be realized. A number of common use cases for Artificial Intelligence have arisen in the capital markets sector. Here are some:

Portfolio Management: Broker-dealers are using AI applications within their portfolio management and trading functions.

AI applications identify new patterns and predict potential price movements of specific products or asset classes within portfolio. These applications tap into vast amounts of data available from internal and external sources, social media and satellite imagery etc., to identify insights that may signal price movement. Broker-dealers incorporate these predictions into their investment strategies to generate alpha for the portfolio.
AI tools make their trading functions more efficient by maximizing speed and price performance -using ML for smart order routing, price optimization, and optimal allocations of block trades. Circumstances which are not covered in AI model training – such as unusual market volatility, natural disasters, pandemics, or geopolitical changes – may not produces reliable predictions, and could trigger undesired trading behavior.

Compliance and Risk Management: Broker-dealers have to keep pace with complex and evolving domestic and international regulations, as well as a rapidly changing risk landscape (e.g., cybersecurity, internal threats, and financial risks). AI technologies present opportunities for them to develop automated compliance and risk-management tools.

AI tools offer the ability to move beyond “traditional rule-based systems to a predictive, risk-based surveillance model that identifies and exploits patterns in data and facilitate informed decision-making. AI technology offers the ability to capture and surveil large amounts of structured and unstructured data in various forms (e.g., text, speech, voice, image, and video) from all sources in order to identify patterns and anomalies. This enables firms to holistically surveil and monitor various functions across the enterprise, as well as monitor conduct across various individuals (e.g., traders, registered representatives, employees, and customers), in a more efficient, effective, and risk-based manner. These AI tools could significantly reduce the number of false positives, free up compliance and supervisory staff time to conduct more thorough reviews of the remaining alerts, resulting in higher escalation rates.
AI-based tools are developed for customer identification (KYC) and financial crime monitoring- to detect potential money laundering, terrorist financing, bribery, tax evasion, insider trading, market manipulation, and other fraudulent or illegal activities. AI technologies, such as ML, NLP, and biometrics, are deployed to make the programs more effective and risk based. Now, these tools identify and track customer activity with greater accuracy and efficiency, and to conduct more holistic and detailed analysis of customer transactions.
Broker-dealers use a variety of regulatory intelligence management programs and processes to identify, interpret, and comply with new and changing rules and regulations across jurisdictions. Earlier it was a manual process, now AI tools can digitize, review, and interpret new and existing regulatory intelligence (including rules, regulations, enforcement actions, and no-action letters) and incorporate appropriate changes into their compliance programs.
ML applications optimizes financial liquidity and cash management by analyzing substantial historical data along with current market data to identify trends, note anomalies, and make predictions related to intra-day liquidity needs, peak liquidity demands, working capital requirements, and securities lending demand.
AI-based models assess credit worthiness of their counterparties, which speeds up the credit review process. These models analyze traditional credit evaluation criteria, such as current financial standing and historical credit history; identify demographic factors as deterministic criteria, which may lead to unfair and discriminatory credit scoring based on biases present in the underlying historical data.
Cybersecurity sustains to be a top challenge for Capital Market. Regulators are demanding financial institutions to develop comprehensive cybersecurity controls. In response, broker-dealers are incorporating AI in their cybersecurity programs. Incorporating AI into cybersecurity programs allows to predict potential attacks, detect threats in real-time, and respond to them faster and at lower costs.

Collateral Management: Natural language processing (NLP) has offered significant automation to a variety of manual tasks:

AI has ability to automate the mapping of key terms from ISDA CSA (International Swaps and Derivatives Association’s Credit Support Annex), GMRA (Global Master Repurchase Agreement) and GMSLA (Global Master Securities Lending Agreement). Structured and unstructured data processing capability of AI can interpret the content held within legal agreements and convert this unstructured data into structured data more quickly, accurately and cost effectively than a human.
AI can be deployed for its NLP capability to analyze huge unstructured data in new regulation facing the industry. IBM estimates that financial services firms spend $99 billion per year addressing compliance, which by 2020 will be 300 million pages of regulation. As a result, IBM is exploring Watson supercomputer to help financial institutions deal with this burden.
ML from the past datasets, how disputes have been reconciled in the past, enables process automation with major cost reductions and increased speed in resolving disputes. BNY Mellon achieved success with a robotic process automation solution for trade reconciliation. The system can reconcile a failed trade in a quarter of a second vs 5-10 minutes for a human reconciliation with minimal human intervention. The machines can also work overnight.
NLP processes of text contained in an email from a client or counterpart to determine intent. From there the AI solution automatically provides a standard response for more mundane tasks, converts them into structured data using NLP, feeds them into a machine learning model to categorize emails and then acts on them. This allows the escalation of more complex queries to humans, freeing up time to focus on more difficult requests.

Customer Service: AI-enabled customer service applications involve NLP- and ML-based tools in Securities Industry that automate and customize customer communications

AI enabled Virtual assistant interacts with humans using voice recognition and is programmed to perform certain tasks in the delivery of digital customer service. Digital customer services with their virtual assistants provide responses to basic customer inquiries, such as those related to account balances, portfolio holdings, market data, address changes, and password resets. Virtual assistants accept and process trade orders within certain thresholds through their websites and mobile apps; through third-party platforms, such as Amazon’s Alexa, Google’s Home Assistant, and Apple’s Siri; or through AI-based interactive voice response (IVR) systems. These virtual assistants employ NLP (including speech-to-text/text-to-speech conversion, tone recognition, and text generation), ML, and sophisticated customer authentication tools, including the use of facial recognition, fingerprints, and voice biometrics. The applications are trained with large sets of historical and current data, including customer account information, trading history, and past inquiries, as well as market data, to formulate appropriate responses to incoming customer inquiries.

AI-based applications screen and classify incoming client emails based on key attributes, such as the sender’s identity, the email’s subject line, and an automated review of the email message itself. Such applications may also automatically respond to emails containing routine inquiries, while routing emails with complex inquiries to the appropriate personnel or relevant subject matter experts.

Sales Management: Broker-dealers explore AI tools to reach and analyze their potential, past and existing customers’ investing behaviors, website and mobile app footprints, and inquiries, and then proactively provide customized content to them. This includes curated educational information, news, and research reports on specific investment products or asset classes. This content could be delivered to customers by email or directly through the firm’s website or mobile app. AI tools determine whether individuals would be interested in certain services based on their customer profile and browsing history within the firms’ websites. These applications may pose certain risks, such as customer authentication, data privacy, cybersecurity, and recordkeeping

Marketing Management: AI-based tools provide individual dealers/brokers with customized, real-time customer information and better insights into customer preferences and trading behaviors. Registered representatives use such information to enhance customer relationships, to provide better service and recommendations, and to potentially minimize customer attrition.

AI-based applications create real-time, holistic customer profiles, which incorporate information from a broad range of sources, such as customer assets (based on internal and external data), spending patterns, and debt balances via data aggregation tools; social media and public websites; browsing history on the firm’s website and mobile apps; and past communications (e.g., from emails, chat messages, and meeting notes). All this information is analyzed using AI tools to provide the registered representative with a broader picture of customer needs, along with tailored suggestions of what investment products the customer may be interested in.

AI tools provide curated market research directly to brokers/ dealers to share relevant information on various investment opportunities. AI-based tools may offer customers social media data and related sentiment analysis on investment products and asset classes.

Back Office Functions: Security industries are incorporating AI technologies (e.g., ML, NLP, and CV) to automate high-volume, less complex, repetitive, and manual tasks that traditionally involve significant human staff time and related administrative functions. Automating such tasks with AI tools have the potential of high returns in terms of cost savings and efficiency increases.

AI tools automates functions that involve manual review of documents, such as processing faxed trade orders, searching, ranking, and retrieving documents. These applications incorporate CV and NLP to identify customers, review signatures, read orders, and digitize documents. Such applications increase productivity, accelerate important processes, such as trade and payment processing.

NLP provides review of significant volumes of documents (e.g., legal contracts, custody documents, and loan agreements) at a fraction of the time it takes for human analysis. In addition to time and cost savings, these automated reviews yield results with higher accuracy.

AI tools help to improve the accuracy and efficiency of existing processes, such as reporting and invoice reconciliation.

Machine Learning (ML) and Predictive Analytics in Capital Market and Securities Industry

ML is a specific type of artificial intelligence AI, with subsets for supervised learning (regressions, decision trees and random forests), unsupervised learning (clustering, principal component analysis…various forms of neural networks fall into both supervised and unsupervised) and reinforcement learning (Monte Carlo algorithms and the Markov decision process). It extends the ability of computers to learn without being explicitly programmed. Predictive analytics make predictions about unknown future events. This can be applied to financial markets, including securities finance and collateral management in the following ways:

Digital Trading Strategy and Simulations: The ability to guide digital trading strategy is one of the challenging areas of AI. Note that high frequency algorithmic trading are not the same as the replication of an analyst. One recent development leverages evolutionary computation, involves creating a large group of digital stock traders and then testing their performance against past stock performance data. The top performers are selected and their genes utilized to create the next generation of superior traders. This process is repeated over many thousands of generations, until the system produces an AI trader that can operate independently. Other developments include start-up Kensho, offers a solution that can scan documents on a wide range of topics from economic reports to politics. It then uses this data to provide answers to “more than 65 million question combinations on where markets are headed.” “Which cement stocks go up the most when a Category 3 hurricane hits Florida?”

Security Financing: In the Securities Finance business, firms could also deploy AI against the data gathered from Securities Financing Transactions Regulation (SFTR). By feeding in examples of trends it could then become possible to second guess moves by counterparties, clients and regulators. Regulators may react to certain trends in the SFTR data that signal a build-up of risk by raising haircut floors or increasing capital requirements. If market participants can use AI to better predict when this type of activity will occur, along with other key events such as bond market squeezes, then this can inform strategic decision making.

Collateral Optimization and Liquidity Management: Collateral optimization and liquidity management is one area where AI has the potential to support decision making. There are a large number of parameters influencing optimization decisions (collateral costs, operational and settlement costs, counterparty efficiency etc.). Feeding historic data around performance of optimization runs and then using AI to suggest more optimal collateral allocations in the future could result in major cost benefits.

Counterparty Credit Risk: The ability to assess whether a counterpart is likely to default is a key use case for AI. This could leverage huge amounts of data from multiple sources from news and analyst reports to social media sentiment around the company on platforms such as Facebook and Twitter to look for indicators of default. The trend towards Peer to Peer/All to All networks in securities finance could also leverage AI to increase disintermediation.

Applied AI and ML Practices by Leaders in Capital Market and Securities Industry

ING: ING uses Katana, a machine-learning algorithms, to scan the European and UK bond market for possible pairs that have out-of-the-ordinary spreads or behave abnormally. It aims to help bond traders and asset managers find investment opportunities they otherwise could have missed out on. In a pool of even just 2,000 bonds, there can be almost two million potential pairs. Katana, a data analytics & machine-led platform, finds these opportunities much faster than a human can by comparing historical prices of the bond with others in the portfolio. Once it identifies a potential investments, the trader gets an alert and it’s up to them to decide to pursue that trade.

Northern Trust: The custodian has developed a pricing engine that uses machine learning and statistical analysis techniques to forecast loan rates in the securities lending markets. For this project, data scientists applied a time-series algorithm to the problem of securities lending. Northern Trust’s algorithm uses market data from various asset classes and regions to project the demand for equities in the securities lending market. The firm’s global securities lending traders can combine these projections with their own market intelligence to automatically broadcast lending rates for 34 markets to borrowers.

NASDAQ: NASDAQ has implemented a new market surveillance tool for finding patterns of abusive behavior. Underpinning the tech are three subsets of machine learning: (i) deep learning, analyzes and understands hidden relationship to get insights; (ii) transfer learning, involves training new models based on older ones; and (iii) human-in-the-loop learning, requires human interaction to weed out noise from signal. Currently, the tool covers only US equities, but NASDAQ has already run some trials on fixed income and with other exchanges, which have shown promise.

JP Morgan: JP Morgan has dropped conventional modeling techniques such as Black-Scholes and replication, and adopted data-driven approach that is underpinned by machine learning. The bank has applied new technology in 2018 to hedge its vanilla index options books and rolled it out in 2020 for single stocks, baskets, and light exotics.

Morgan Stanley: The bank is experimenting with machine learning algorithms and other forms of AI to figure out which algorithms to use to trade equities in a particular market condition and predict whether last night’s stock price, which jumped up, is going to continue or whether it’s going to fade away in the morning. It also looks at trade-volume curves to better understand when to trade with the least friction. They want to use algorithms to understand what is going to happen to the price over the next event, the next second, the next minute, the next half hour. Morgan Stanley is also using machine-learning forms to intelligently suggest indications-of-interest (IOI) to clients, based on their expected investment behavior.

UBS: The Swiss bank uses machine learning to match information and find anomalies in customer information for KYC and anti-money-laundering (AML) reporting. UBS is coupling machine learning with natural language processing (NLP) that takes data from public sources and automatically connects it to customer information to find anomalies.

Brown Brothers Harriman: BBH brings greater efficiency to its net asset value (NAV) review process through the use of supervised machine learning. Securities pricing is reconciled each day at market close to make sure that the NAV figure is accurate, and the prices are reviewed to discover any significant variation day to day. With traditional methods, the process resulted in a high proportion of exceptions, most of which were not true anomalies, but that nonetheless had to be reviewed by analysts. To address the issue, BBH created a tool that uses supervised machine learning and predictive analysis to generate an exception when the price truly moves and eliminated 90% of the false-positives.

Universal Investment: The Frankfurt-based fund administrator is using machine learning as part of a broader, ambitious project to build a solution that will allow clients to purchase funds as conveniently as readers buy books on Amazon. Right now, the process of buying a fund is slow: different intermediaries, such as banks, transfer agents and custodians, are involved; their processes are still manual and paper-based; and settlement cycles can take up to two days. The service is going to be based on the Ethereum blockchain and will use predictive analytics to identify clients’ interests and allow sales personnel to recommend them better options.

Franklin Templeton: Franklin Templeton’s fixed-income (mutual fund) team along with vendor H2O.ai builds machine learning models using its Driverless AI product and estimate the default risk of underlying loans in fixed-income assets, like mortgage-backed securities. Franklin Templeton wants to use the tool to predict bond defaults and model cash flows on other types of loans.

The Federal Reserve Bank of New York: The New York Fed and those companies go back and forth to hammer out misreporting that needs to be corrected. The regulator use machine learning that takes into account historical reporting from banks, as well as peer-to-peer comparisons to “triangulate and predict” instances of misreporting and to streamline the correction process. They are exploring machine learning’s strength to look at a massive amount of data and find connections like— “So where do they focus their attention? Which data is potentially erroneous?

The Financial Industry Regulatory Authority (Finra): Finra uses machine learning for market surveillance as it continues to refine its algorithms to trace manipulation. The regulator uses machine-learning algorithms to detect spoofing and layering activities. Machine learning makes sure that the handling and disposition of the alerts has a higher level of certainty in that judgment. Machines are trained to do what the humans do in terms of their initial judgment and intuition and let the algorithm do that.

Linedata: Linedata is working on a project that will automatically fix system fragmentation using machine learning. The application will monitor fragmentation—or the level in which memory allocation is broken up within a system that causes slower performance, which presents security and performance risks—within a firm’s technology infrastructure, and predict security failures. Monitoring toolsets, under research, uses machine learning to learn behaviors of changes in the technical environment and apply fixes automatically without the intervention of an engineer. Linedata also identifies patterns in trade pricing and position amendments, others ML algorithms for cybersecurity services when it comes to monitoring access to end-user devices.

Conclusion

Capital Market and Securities Industry are at a unique place and time in their evolution with biggest challenges in their history: full digitization of markets, technology-driven disruption, fee compression, and lower client switching costs, to name just a few. At the same time, they are embracing some of their biggest opportunities, many made possible by technology: the convergence of data ubiquity, high-speed processing, and the advanced technologies that comprise enterprise AI. As Capital Market and Securities Industry work to digitize and transform for growth and operational efficiencies, they are aggressively innovating and differentiating, as they compete to secure a larger share of assets and create a next generation client experience. From AI trading to AI fraud detection to the benefits of a machine learning stock market— artificial intelligence helps firms to reimagine their operations. By accelerating initiatives, leveraging their own data, and delivering bottom-line results, companies using machine learning for trading cannot just compete, but also win.