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How to Execute Intelligent Automation in Banking: 4 Success Stories

Leslie Loges, Content Marketing Manager
June 13, 2024

If you’re looking for solutions to optimize business processes for improved results, artificial intelligence (AI) alone isn’t enough.

Intelligent automation in banking provides a holistic approach to automate complex processes and repetitive tasks so your financial institution can realize greater increases in performance, operational efficiency, and customer satisfaction.

In short, intelligent automation takes your processes to the next level. In this post, we’ll explain what intelligent automation is and break down several use cases for the financial services industry so you can see how it’s been put into practice for real-world success.

Further Reading

Is your company ready to use AI?

Read our AI Handbook for Financial Services Leaders for tips and tools on implementing AI while managing risk.

What is intelligent automation in financial services?

Intelligent automation delivers a proven way to combine digital technologies, such as generative AI (gen AI) and robotic process automation (RPA). This strategic approach streamlines your ability to automate manual tasks, giving employees the opportunity to spend more time on higher level priorities.

And it’s not a new or untested method for financial services companies. According to Forbes, by 2020 nearly 85% of financial institutions had already adopted intelligent automation to execute some core functions as part of their digital transformation. They cited examples, such as RPA and AI, as solutions to provide greater agility in a rapidly changing industry.

In the last few years, gen AI has emerged as a game changer for banking institutions. According to Gartner, 42% of banking CIOs have deployed gen AI or will in the next 12 months. More than 80% of banks will adopt gen AI by 2026, up from 5% in 2023.

How is AI used in banking?

In the past, the banking industry used AI to automate fraud detection, market projections, and mundane tasks like customer service (chatbots, for example).

Gen AI now allows financial institutions to leverage AI to automate complex processes, such as collateral management, anti-money laundering checks, credit assessments, personalized market insights and recommendations, risk management processes, and more. One key to making the best use of AI is combining it with other technologies for intelligent automation.

For example, using a data fabric will help you get the most accurate insights on what's happening in your organization. This will help you get a clearer picture of where you can make improvements in your processes with automation to achieve operational and cost efficiencies.  

Financial institutions employ these tools to simplify workflows for increased efficiency and productivity, including cost savings. But more than that, banks have found that user-friendly, less cumbersome manual tasks help boost employee engagement, improve customer service, and ultimately increase annual revenue.

4 intelligent automation examples in banking

If intelligent automation isn’t one process but a strategy to use several types of technology, like machine learning, intelligent document processing, and other automation solutions, where do you start?

Tailor your solutions to your unique challenges and goals to get the best value from your investment.

Some things to consider:

  •  What pain points are you trying to solve for your employees?
  •  How can you use intelligent automation to give customers the most intuitive experience?

Look for the bottlenecks in your current processes. Here are a few business use cases for well-executed intelligent automation in banking:

1. Governance
2. Customer experience
3. Risk management
4. Anti-money laundering (AML)

Intelligent automation to improve the governance cycle

For NatWest, the road to agility required new solutions for governance. They approached their process improvement initiative from a customer journey perspective, even for internal processes.

Looking at things in a new way was revelatory––previously, cumbersome approvals bogged down NatWest’s governance cycle so much that a single project might take 73 days.

NatWest developed their Change Risk Hub to standardize and automate 17 disjointed governance processes into a single approval process with a unified data model.

How did the initiative improve governance?

NatWest decreased the governance cycle from 73 days to as little as 73 minutes in some cases. In terms of time and money, the new process saved an estimated one week per project, for a savings of £4.5 million annually.

Case Study

NatWest Wins Celent's Model Risk Manager of the Year Award

Learn more about how NatWest overhauled their processes and earned the award. 

Simplifying workflows to decrease customer wait times

Addiko Bank faced difficulties in developing a seamless loan origination and management process. As an international financial group serving more than one million customers across several countries, Addiko originally relied on paper documents and complex manual processing, which bogged down communications and negatively impacted the customer experience.

Addiko overhauled the way they handle loan processing with intelligent process automation. By removing the long, complex workflow and developing a consistent standard process, Addiko cut customer wait times in half and freed up time for employees to focus on higher-value work.

Automating risk management for data-driven decision making

Rapid changes in the financial industry left Clayton Holdings, Inc, one of the leading providers of risk analysis and loss mitigation, in a difficult position. Productivity issues ranged from bottlenecks in task management and training timeframes to manual processes and workflows.

Clayton deployed a secure cloud architecture to manage critical processes and increase efficiency and visibility across functions, such as credit risk and transaction management. Since implementation, the company has seen a 30% improvement in process efficiency equaling a productivity increase of 66 hours per month and $10 million in client value-add savings.

Intelligent automation to evaluate and monitor account activity

FirstBank, the largest locally owned banking organization in Colorado, sought ways to improve its AML efforts to meet regulatory requirements and their responsibilities in assisting law enforcement with fraudulent activity.

As FirstBank grew, the SharePoint system they used became inadequate, causing issues with workflow visibility, scalability, and the ability to access data across disparate systems and databases.

FirstBank developed an intelligent case management approach to identify and resolve potential AML cases with ease. The concept for their new system went from ideation to deployment in 12 weeks and created 500,000 new processes within 12 months.

With centralized and easily accessible information, FirstBank saved more than 1,000 work hours annually.

Go beyond AI with automation and human intelligence

Gen AI may get all the hype, but to truly make the most of it and see the benefits of automation come to fruition, it needs to be combined with automation and human intelligence. 

AI relies on human intervention and assistance to evaluate nuances and make critical decisions. When AI and humans collaborate, robust process orchestration can be achieved to efficiently route tasks between AI, other automation technologies, and humans. This synergy enhances the effectiveness of both human and digital workers.

Worth the Watch

Webinar: De-Silo Your Data for Rapid Innovation with AI

Learn how AI and process automation can catapult your organization to the next level.