How to increase the scale and business value of automation in financial services
Robotic process automation (RPA), cognitive automation, and artificial intelligence (AI) are transforming how financial services organizations operate. Today, many organizations are still in the early stages of incorporating robotics and cognitive automation (R&CA) into their businesses. Find out how you can maximize the value and benefits from R&CA investments.
Robotics and cognitive automation (R&CA) is transforming how financial services firms operate, and over the next few years widespread adoption is expected across the industry. Today, however, many firms are still in the early stages of incorporating R&CA into their businesses and are unclear on how to achieve the scale necessary to produce substantial business value.
This article examines the most common challenges FSI firms encounter when pursuing R&CA strategies, and offers practical advice to help you maximize value and benefits from your R&CA investments.
What is R&CA?
The term “robotics & cognitive automation” refers to a broad continuum of technological capabilities, ranging from robotics that mimic human action to cognitive automation and artificial intelligence that mimic human intelligence and judgment.
Robotic Process Automation (RPA). Early on some referred to RPA as ‘spreadsheet macros on steroids.’ But the power of RPA has evolved significantly and is increasingly being used for rules-based, high-volume tasks and business processes that do not require human judgment. Examples include accounts payable, finance processes, elements of client onboarding processes, and elements of mortgage loan origination processes. Processes that are prime candidates for RPA should have a welldefined set of rules and instructions to train the automation, just as a new employee should be trained. The more complex the rule set, the harder it can be to make RPA work.
Cognitive Automation. Moving up the continuum, we are seeing increased adoption of cognitive technologies that go beyond rule-based systems, with significant movement towards parameter-based systems that can learn and adjust over time. For example, chatbots are now being used by banks and insurance companies to handle customer inquiries; machine learning is helping with anti-money laundering alerts and fraud detection; and internal helpdesk chatbots are being used to handle financial queries.
Artificial Intelligence (AI). AI is making major strides in many process areas outside of FSI, including highly complex activities such as autonomous driving. But within financial services, AI is still largely in the experimental stage. For example, some asset managers are exploring the potential of AI to generate higher alpha by using asymmetrical data (such as unstructured data sets and sentiment analysis) to drive better investment decisions.