Cognitive technologies are artificial intelligence (AI)-based machines and applications, including robotic process automation (RPA), natural language processing (NLP), and machine learning, that take over functionality that previously needed human operation. Cognitive Technologies in Capital Markets offer enhanced accuracy and productivity, as employees previously responsible for repetitive tasks that can be automated can focus their bandwidth on higher-value tasks.
Cognitive technologies can be used alone or in conjunction with other applications. RPA and machine learning are more often used for tasks with common instructions, while NPL, virtual agents, and smart workflows are used for more client-facing services.
Cognitive technologies help financial institutions stay competitive in the current digital revolution. Intelligent automation can be used to facilitate clients managing their own portfolios. Financial advisors utilize cognitive computing to strengthen confidence in their decisions and ultimately improve customer experience with quicker and more appropriate investment options. Relationship managers use it to more often engage with customers and increase customer loyalty.
Digital technologies that contribute to cognitive computing further include intelligent process automation (IPA) that organizes and automates routine reporting and reconciliation, virtual agents that address basic customer needs rapidly, and pattern recognition that can automate fraud detection.
Cognitive technologies have transformative potential across various business lines and functions. Machine learning algorithms can detect patterns in transaction data, offering an advanced understanding of customer behaviors, and informing more useful digital sales and marketing. Smart workflows accommodate functions performed by both humans and machines that offer end-to-end, real-time tracking. These tools can automate activities from margin payments to trade lifecycle management. When combined, applications such as machine learning and natural language generation can build virtual workforces, able to execute tasks, communicate, learn from data sets, and even make decisions based on predetermined parameters.