When the COVID-19 outbreak became a global pandemic, financial-markets volatility hit its highest level in more than a decade, amid pervasive uncertainty over the long-term economic impact. Calm has returned to markets in recent months, but volatility continues to trend above its long-term average. Amid persistent uncertainty, financial institutions are seeking to develop more advanced quantitative capabilities to support faster and more accurate decision making.
An imperative to act
As the volume of data generated is rising continuously, Machine learning, a subset of artificial intelligence, can process and analyze massive data through configuration. Similar to humans, machines can also process natural language and learn from experience. Machine learning has wide-ranging applications for the capital markets segment, which is characterized by labor intensive processes that generate low business value.
A New Opportunity for Intelligent Services
Machine learning focuses on developing algorithms to enable computers to independently adapt behaviors and make empirical data-driven decisions. Machine learning algorithms require trained data to capture the characteristics and relations between variables. As the research on machine learning advances, the focus will shift to self-learning and automatic recognition of complex patterns. By identifying patterns within the underlying data sets, algorithms enable intelligent decision making, guided by the given rules and conditions.
Machine learning is classified into two major types—Supervised and Unsupervised learning. Supervised learning uses historical datasets to tune and predict outcomes, and the user has to model the system by tagging the output and guiding the system with conditions and constraints. In Unsupervised learning, the system detects the clusters from untrained data sets by inferring from classifications and boundary conditions. Additionally, a third concept called Reinforcement learning is also emerging wherein the systems’ dynamic actions constantly measure the outcome and correct future behaviors.
Machine learning offers three broad solutions:
1.Robotics Process Automation (RPA) for automation at desktop and business process levels
2.Cognitive Computing for computers and systems that make decisions
3.Deep Learning for identifying patterns
RPA in Capital Markets
RPA is one of the early stage machine learning applications to benet business process automation. The exponential growth in computing power and its reducing cost has made RPA viable for deployment in business process services. RPA improves business efficiencies and effectiveness, while reducing manual errors by emulating and automating human actions. It is viewed as a potential replacement for the repetitive tasks performed by the operation teams. The RPA components extensively used for data extraction and preparation include optical character recognition (OCR), intelligent character recognition (ICR), document and image parsers, macros, scripts, and tools for recording and replaying. Depending on the data extracted, the logic and decision-making activities are performed using rule-based engines, BPM tools, or memory management.
RPA enables creation of three different types of bots—Task Bots, Medabots, and IQ Bots—depending on the complexities of the processes to be automated. Task Bots perform and enable completion of automated tasks by executing multiple steps using predefined rules and structured data. They are extensively used at the desktop level for completing the tasks that can replace any repetitive human actions performed during the processing lifecycle. Meta Bots are used mainly at the point of integration. APIs are used to perform system level automation and enable interaction with multiple systems to complete the task. Meta Bots enable orchestration of complex processes with dependent sub processes and actions to be performed or completed by multiple systems. IQ Bots are advanced RPA systems that apply machine learning, based on the trained data sets. They are self-learning systems that apply different models using both structured and unstructured datasets.
Applications of RPA include:
- Customer servicing,
- Know Your Customer (KYC) processes
- Customer profile creation,
- Derivative documentation,
- regulatory and compliance filings
- Automated portfolio rebalancing
Cognitive Computing in Capital Markets
Cognitive computing involves computer systems used for decision making by the processing computer, which is tuned to learn and think like humans. The methodology adopted is similar to the way the human mind works as it learns, contextualizes, and performs, based on past experiences and informed judgments. The underlying technology of any cognitive application involves natural language programming, which can understand the language, contextualize, and build relationships and neural networks. The system senses and predicts patterns and utilizes advanced self-learning algorithms to understand and enable complex decision making, computer vision, speech recognition, and data mining. It also offers statistical techniques to manage the data and content, and build runtime.
Cognitive computing enables organizations to build smart applications by employing dynamic learning techniques such as neural networks, which continue to train the model, based on the outcome obtained through iterations and interactions. Knowledge-intensive processes are well suited for replacement and automation using cognitive computing.
Applications of Cognitive Computing include
- Automated fraud detection,
- Smart forensic management
- Auto reconciliation.
Deep Learning in Capital Markets
Deep Learning is an advanced and nascent stream in machine learning and includes a collection of techniques for building multi-layered, non-linear artificial neural networks that can learn features from the input data. It can learn and recognize patterns but cannot solve problems. Deep learning algorithms are used for both supervised and unsupervised models. It is more prevalent in unsupervised learning and tasks, which can abstract or compose the information based on the layers of factors. Deep learning requires large volumes of data to learn and abstract the information; look for complex relationships, and refine the algorithms or models as they compose more data.
Deep learning can be employed in the financial markets to develop automated trading strategies using technical analysis. Deep learning models can be applied to identify patterns using different technical charts of each stock, perform predictions, and make trading decisions, based on the patterns recognized. The other potential applications of deep learning include developing credit rating mechanisms by identifying patterns of internal, external, and economic factors that affect the financial performance of companies. Similarly, deep learning techniques can also be used to provide appropriate automated investment advice to clients by utilizing multiple data from varied sources such as research reports, technical data, financial performance, news, and social media.
Machine learning has the potential to enable institutions to do more in capital markets, to move faster, and to move with greater accuracy. The working conditions created during the pandemic have accelerated reliance on digital access and the data-driven environment. Given these factors, machine learning could easily begin to migrate into mainstream operations. With this in mind, firms must not delay in building their capabilities.
Most banks consider algorithm-driven automated solutions to mitigate operational risks, improve efficiencies, reduce labor costs, and achieve faster compliance. They must experiment, develop use cases, and move quickly to the production of machine-learning-enhanced models. Those that create and execute a sensible implementation strategy are likely to emerge from the current crisis stronger, more assured of risk exposures, and better prepared for what lies ahead.
Knoldus simplifies this with a complete suite of tools to manage, deploy, and monitor your machine learning applications with expertise garnered over 10 years of developing ML enabled products.