Published by Jessica Weisman-Pitts
Posted on September 26, 2022
5 min readLast updated: February 4, 2026
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By Kam Star, V.P. Product Portfolio, SS&C Blue Prism
Globally, 70% of organizations have already piloted automation technologies, with those in the financial services industry most likely to scale such solutions across the business. Why is this a growing trend? Advanced technologies have become imperative to remaining competitive in the sector and securing a viable future in an increasingly digital-first world. Yet, only 13% of businesses do not plan to automate, and many cite a lack of opportunities and resources as the main reason.
Many organizations are not utilizing business process automation technologies to their fullest potential, with less than 20% scaling across the entire company. While robotic process automation (RPA) is the most widely used automation technology, artificial intelligence (AI) is estimated to have the potential to add $1 trillion in annual value to the global banking industry.
Semantic automation, as a component of AI, refers to:
How does semantic automation add value to the banking and finance sector?
When it comes to financial services, this sector tends to be interested in business process automation for the cost savings it can generate. The advanced technologies driving the most savings in this industry require high-level cognitive skills, such as thinking critically, assessing and providing insights on qualitative and quantitative information and managing projects.
While machine learning (ML), a sub-discipline of AI, can imitate human intelligence, semantic automation adds layers of understanding and thereby improves algorithms. This enables businesses to automate high-level, complex workflows.
Why is semantic automation the right fit for financial services?
The financial services industry has extensive analog processes, operations and data it needs to digitize. Manual processing is prone to errors, low job satisfaction and turnover. The issue is traditional automation technologies struggle with handwritten or visual data, requiring more human-in-the-loop and slower processing. Semantic automation allows disparate analog data to be classified, organized and validated. Any issues or concerns are flagged for human review.
For example, we find that global financial services firms may have to sift through over a million pages of data across various formats – handwritten, fax, low-res images, etc. – to comply with the latest know-your-customer (KYC) regulations. Without semantic automation, this task would be near impossible to complete within a reasonable timeframe. However, we know that this technology increases processing speeds and error rates. As a result, we see businesses save on the opportunity costs of having workers focused on limited-value tasks and any penalties for likely completion delays.
In addition to digitally transforming analog operations, semantic automation’s comprehension capabilities enhance human workers’ experience by analyzing and offering insights on relational and unstructured data. Such unstructured data may be drawn from derivatives restructuring, corporate lending and wealth management.
We’ve seen this in the reconciling of accounts too, where it takes approximately six hours for a human to complete – the worker has to read through and assess related materials, attain any additional information, offer analysis, etc. This is a complex process, but digital workers equipped with semantic cognitive capabilities can complete this in under a minute, freeing the human worker to focus on more complicated and intricate cases. The task is completed without error and gives 1.2 million minutes back to its human workers. They can now engage in high-value interactions, increasing business revenue and job satisfaction.
Leading financial service organizations and banking institutions recognize the need to utilize AI across all operations. Still, many use the technology inefficiently, applying it only for specific uses or verticals. As a result, the valuation gap between leading financial institutions and those falling behind is widening. Decisions around systemic automation adoption will be critical in determining which side of the gap companies land.
In the next ten years, 75-80% of transactional operations like general accounting and payments processing and up to 40% of strategic operations like financial controlling and reporting, financial planning and analysis and treasury are estimated to be automated. This trend highlights the need for financial services companies to begin systemically integrating advanced business process automation capabilities into their operations.
The future is already here
As writer William Gibson once said, “The future is already here. It’s just unevenly distributed.” AI’s cognitive capabilities are already being utilized to varying degrees throughout the financial sector, such as with expanding capabilities of intelligent document processing. The key differentiator among industry players will be the speed with which they invest in these technologies – and early adopters will be the conspicuous winners.
We’re finding that semantic automation is being increasingly used for its discerning capabilities: generating, processing and understanding data; engaging and creating connections among different data points in varying formats; and developing decision-enhancing analysis and insights. When used effectively, we see semantic automation deliver staggering results. Taking care of these tasks digitally allows human workers to drive value for financial organizations and focus on highly complex tasks requiring communication, negotiation, leadership, entrepreneurship and emotional and social understanding skills.
Automation refers to the use of technology to perform tasks without human intervention, improving efficiency and reducing costs in various industries, including financial services.
Robotic process automation (RPA) is a technology that uses software robots to automate repetitive tasks typically performed by humans, increasing efficiency and accuracy.
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems, enabling them to perform tasks that typically require human intelligence.
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how it operates and delivers value to customers.
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