Since the Coronavirus pandemic surfaced, the volatility of the financial market across the globe hit the highest level in more than ten years as the persuasive uncertainty prevails and shows long-term economic impact. However, in recent months, markets have started to gain stability, but still, the volatility is trending above what is known as its long-term average. During times of uncertainty, financial institutions are putting effort into developing quantitative capabilities to support accurate and faster decision-making.
The Urgency Explained
The generated data volume is constantly increasing. Machine Learning, which is widely known as artificial intelligence’s subset, aids in the processing and analyzing a huge amount of data via configuration. Just like us humans, machines are capable of processing natural language as well as learning from experience. Moreover, Machine Learning offers applications for the capital market that range widely. There are numerous Machine Learning use cases that we see today. It is characterized by processes that are labor-intensive and usually generate low business value.
Presenting Opportunities for Intelligent Services
Machine Learning is about developing algorithms for computers to adapt behaviors independently in order to make data-driven empirical decisions. Moreover, algorithms of Machine Learning need trained data in order to capture the relations and characteristics between variables.
With the advancement of research in Machine Learning, self-learning is becoming the ultimate focus and automatic recognition of complex patterns. By identifying patterns through underlying data sets, decision-making is enabled by algorithms that are ultimately guided by predefined conditions and rules.
Machine Learning is further divided into two main types are Unsupervised and Supervised Learning. To be precise, supervised learning depends on historical datasets to tune and predict the outcomes. Furthermore, the user is required to model the system through output tagging and guiding the system by highlighting constraints and conditions. Whereas, in supervised learning, clusters are detected by the system via untrained datasets, which are inferred from boundary conditions and classifications. In addition to that, another concept that is known as Reinforcement learning is emerging. In this type of Machine Learning, dynamic actions of learning are constantly measuring the outcome and correcting future behaviors.
There are three broad solutions offered by Machine Learning:
- Deep Learning for identifying patterns such as text mining applications.
- Cognitive Computing for computers and systems that make decisions
- Robotics Process Automation (RPA) for automation at desktop and business process levels
RPA & Capital Markets
RPA is mainly known as the Machine Learning of the initial stage relating to the automation of business processes. The growth in computing power and its decreasing cost has further made RPA applicable in business process services. RPA enhances business efficiencies and its effectiveness while reducing manual errors through emulation and automation of human actions.
It is considered as an ideal replacement for tasks performed by the operation teams repeatedly. The components of RPA that are extensively utilized for extracting and preparing data include scripts, macros, document and image parsers, tools for replaying and recording, intelligent character recognition, optical character recognition.
Decision-making and logic activities are performed through memory management, BPM tools, and rule-based engines.
Here are some applications of RPA:
- Automated portfolio rebalancing
- Customer profile creation,
- Derivative documentation,
- Applications of RPA include:
- Customer servicing,
- Know Your Customer (KYC) processes
- regulatory and compliance filings
Cognitive Computing & Capital Markets
Cognitive Computing includes using computer systems for decision-making with the help of processing computers tuned to think and learn like humans. The adopted methodology is similar to how our (humans) mind works, learns, performs, and contextualizes as per our past experiences and judgments.
The technology underlying any cognitive application includes language programming that understands the language, can contextualize and develop neural networks and relationships. The system utilizes the patterns sensed and predicted, and it uses advanced self-learning algorithms to understand and enable complex decision making, speech recognition, data mining, and computer visions. Moreover, it offers statistical techniques for managing the data and content as well as building the runtime.
Cognitive Computing makes an organization capable of building smart applications with the employment of dynamic learning applications, including neural networks that continue training the model based on the obtained outcome via interactions and iterations. Processes that are knowledge-intensive are suited for automation and replacement using Cognitive Computing.
Some applications of Cognitive Computing are:
- Smart forensic management
- Automated fraud detection
- Auto reconciliation
Deep Learning & Capital Markets
Deep learning is a developing and advanced stream in Machine Learning that includes a bunch of techniques used for building nonlinear, multi-layered artificial neural networks that are capable of learning features from the fed data. It is able to learn and recognize patterns but unable to solve issues.
Deep learning algorithms can be used for both unsupervised and supervised models. However, it is more prevalent in unsupervised tasks and learning that can compose or abstract the information in accordance with factor layers. Deep learning needs huge volumes of data in order to search for complex relationships, learn and abstract information, and refine models or algorithms as te continue to gather more data.
In the financial market, deep learning is employed in order to develop automated trading strategies with the help of analysis. Deep learning models are applied for identifying patterns with the help of various technical charts of every stock, make predictions, and finalize trading decisions as per recognized patterns.
Other potential applications of deep learning include the development of credit rating mechanisms with the help of identifying patterns of external and internal, and economic factors that affect an organization’s performance. In the same way, deep learning techniques are used to offers automated investment advice to clients by using multiple data collected from different sources, including research reports, news, financial performance, social media, and technical data.
Machine Learning enables organizations to perform better and do more in capital markets while moving faster and more accurately. The conditions developed during the COVID 19 pandemic have improved the dependence on the data-driven environment and digital access. Because of these factors, Machine Learning can be easily migrated into mainstream operations.