ML aims to create algorithms to allow computers to adapt behaviors and make empirical data-driven decisions independently. The algorithms need trained data to identify the characteristics and relations between variables.
Fremont, CA: The vast amount of data generated is gradually increasing, and machine learning (ML) helps process and analyzes these data through configuration. This technology has a broad range of uses for the capital markets segment, characterized by labor-intensive processes that produce low business value.
Here are three ML solutions in capital markets:
Cognitive computing includes computer systems used for decision making by the processing computer, tuned to learn and think like humans. The cognitive application involves natural language programming that understands the language, contextualizes, and develops relationships and neural networks. This application helps organizations create smart applications by implementing dynamic learning techniques like neural networks that train the model according to the result gained through iterations and interactions.
Cognitive computing applications include automated fraud detection, smart forensic management, and auto reconciliation.
Deep Learning includes a collection of techniques for creating multi-layered, non-linear artificial neural networks that can learn features from the input data. It needs a vast amount of data to learn and obtain the information, identify complex relationships, and refine the algorithms or models as they produce more data.
Deep learning can be used in financial markets to create automated trading strategies using technical analysis. Deep learning models can be implemented to recognize patterns using various technical charts of each stock, conduct forecasts, and make trading decisions according to the patterns identified. It can also be applied in building credit rating mechanisms by recognizing patterns of internal, external, and economic factors that influence companies' financial performance.
Robotics Process Automation
Robotics Process Automation (RPA), a machine learning application, enhances business efficiencies and effectiveness, minimizes manual errors by following and automating human actions. Because of the increasing growth in computing power and its low cost, RPA can be successfully integrated into business process services and a potential replacement for repetitive tasks carried out by operation teams.
RPA applications include customer servicing, KYC processes, customer profile creation, derivative documentation, regulatory and compliance filings.