Abstract
The financial services industry is on the verge of a revolutionary period, propelled by the emergence of hyperautomation. This study explores the significant influence of hyperautomation technologies, including artificial intelligence (AI), machine learning, robotic process automation (RPA), and other advanced digital tools, on banking and investment procedures. This analysis explores the ways in which these technologies are changing and improving business operations, enhancing consumer satisfaction, and modifying the competitive environment. The chapter begins by providing an overview of the present condition of financial services, emphasizing conventional approaches and the growing requirement for innovation in order to address changing market demands and rising client expectations. Subsequently, it offers a comprehensive examination of the integration of hyperautomation technologies into many facets of financial operations, encompassing algorithmic trading, personalized banking services, risk assessment, compliance monitoring, and customer service improvement.
Top1. Introduction
The COVID-19 pandemic has significantly accelerated consumer payment pattern towards digital instruments that has been progressing slowly progressing for years (Akana, 2021).. In US, the cash in circulation reached a decade high due to a surge in demand for high valued notes for storage rather than payment purpose (BIS, 2021). Jamie Dimon, JPMorgan Chase chairman and CEO, in his annual letter to the shareholders mentioned that “Banks will face enroumos competitive threat from the Fintechs and Big techs” to stay relevant and profitable in future. Banks got to leverage emerging technologies and readjust their products and processes to stay in the business. Hyperautomation is critical for survival. The financial services sector is on the verge of a significant transformation, marked by the emergence of hyperautomation. The current technological revolution, which incorporates sophisticated technologies like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), Chatbots and cognitive automation, is not only changing the way banking and investment processes are carried out, but also transforming the customer experience in the financial sector.
Hyperautomation in financial services surpasses the conventional limits of automation. It integrates several technology applications to automate intricate business processes, including those that formerly relied on advanced human intervention and decision-making abilities. This integration enables an unparalleled level of efficiency, precision, and swiftness in financial transactions and processes. The banking industry, which has traditionally relied on manual procedures and outdated technologies, is currently undergoing a significant transformation through the use of hyperautomation. Banks are enhancing their services by revolutionizing processes such as loan origination, risk assessment, compliance checks, and customer support. This transformation enables them to provide more customized, secure, and streamlined services. Hyperautomation in investment management facilitates instantaneous market analysis, algorithmic trading, customized portfolio management, and anticipatory risk evaluation. This process expands accessibility to advanced investing tactics that were previously exclusive to privileged individuals.
Hyperautomation is an advanced approach that combines multiple technologies to automate, streamline, and optimize business processes. Here's a table describing key technologies needed for hyperautomation, along with their descriptions and applications: These technologies(Table 1) work in tandem to enable hyperautomation, allowing businesses to automate complex processes, gain deeper insights, and significantly enhance efficiency and accuracy across various domains.
Key Terms in this Chapter
Hyperautomation: An end-to-end automation strategy that uses modern technologies like AI, ML, RPA, and others to automate as many business activities as feasible.
Process Mining: Using event logs to analyze and visualize business operations. Process mining helps companies understand and optimize and automate processes.
Natural Language Processing (NLP): A branch of AI that uses natural language to help computers understand, interpret, and respond to human speech.
Compliance Automation: Automation to ensure financial services firms meet regulations. Compliance automation enhances audit readiness and decreases non-compliance.
Digital Transformation: Integration of digital technology into all aspects of a business, transforming operations and customer value. Cultural transformation is often needed to adopt new technologies and processes.
Financial Technology (FinTech): Innovative technologies to automate and improve financial services. Mobile banking, investment apps, and cryptocurrency are FinTech applications.
Intelligent Process Automation (IPA): Automation of complicated business processes using AI and RPA. IPA uses cognitive technologies to manage unstructured data and make judgments beyond RPA.
Artificial Intelligence (AI): A discipline of computer science that creates systems that can learn, reason, solve problems, and interpret language.
Robotic Process Automation (RPA): A technology that automates repetitive, rule-based processes with software robots or 'bots' Financial process efficiency and mistake reduction can be greatly improved with RPA.
Workflow Automation: The design, execution, and automation of processes where employees deliver tasks, information, or documents for action according to procedural standards.
Machine Learning (ML): Algorithms and statistical models allow computers to learn and improve without being programmed.
Cognitive Automation: Utilizing AI technologies like ML and NLP to mimic human thought processes in automated business processes. Cognitive automation lets systems comprehend, interpret, and act on complicated data.