Intelligent Automation in Banking & Financial Services [2025]

Companies in the financial services industry are aware of the potential benefits of AI and automation: 

• Banking will be one of the two industries spending the most on AI solutions by 2024, according to IDC.
• About 80% of finance leaders have implemented or are planning to implement RPA, according to Gartner’s RPA Stats.

Explore various use cases and case studies of intelligent automation in banking industry, as well as application steps of intelligent automation:

Use cases

By combining automation solutions, such as RPA, with AI technologies such as machine learning, NLP, OCR, or computer vision, financial services companies can move from automating specific tasks to end-to-end processes. 

This combination is commonly referred to as intelligent automation, cognitive automation, or hyperautomation.

1. Customer service

Know your customer (KYC)

By using AI-driven RPA bots, banks can accelerate their KYC automation processes. They can:

• Extract relevant data from customer documents faster with OCR and intelligent document processing, 
• Identify risk areas with ML models,
• Use RPA bots to forward cases that require human intervention to a staff member.

This can allow banks to:

• Improve the customer experience with rapid customer onboarding
• Increase employee productivity by reducing the need for manual intervention
• Improve security and compliance by reducing the error rates

Responding to customer requests

AI-powered chatbots integrated with RPA bots can:

• offer a 7/24 customer service
• answer FAQs
• enable automated onboarding

Intelligent automation bots can route more complex customer requests to call center staff.

2. Loan processing

Intelligent bots can radically improve the traditional paper-based lending processes by:

• Processing and extracting relevant information from customers’ documents with document capture technologies.
• Consolidating internal and external data to prepare due diligence for the loan decision.
• Leveraging ML-based credit scoring models for a final decision.

Leveraging intelligent automation can enable better loan decisions, boost operational efficiency, and improve the customer experience.

3. Regulatory compliance

Banks and other financial institutions operate in an ever-changing regulatory landscape. Intelligent bots can monitor regulatory announcements for upcoming changes and compare notifications to display what has changed. This reduces the time spent on tracking regulations and decreases the possibility of fines due to manual errors.

With NLP and OCR technologies, intelligent bots can also scan legal and regulatory reporting and documents rapidly to check non-compliant issues without any manual intervention.

You can also check our article on compliance automation.

4. Anti-money laundering

AI-enabled RPA bots can automate anti-money laundering tasks such as:

• Name screening: Bots can collect customer information from multiple watchlist databases that contain money launderers, fraudsters, or politically exposed persons (PEPs).
• Transaction monitoring: AI-powered bots can monitor transactions, detect suspicious activities, and alert staff for further investigation. Feel free to check our article on how AI/ML models improve fraud prevention.
• Offboarding: Bots can check clients’ account status and automate other manual tasks involved in customer offboarding.

Feel free to check our article on how AI is used in anti-money laundering (AML).

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in financial services organizations include:

1. Credigy Solutions

Sponsored:

Problem: Credigy, a global speciality finance company, had many back-office processes that were handled manually, such as analyzing thousands of incoming data files.

Solution: The company implemented IBM Robotic Process Automation to automate over 25 business processes.¹

Result: By automating time-consuming tasks, Credigy has continued to grow its business at a compound annual growth rate of 15%+ and the company plans to deploy hundreds of RPA robots over the next two years.²

2. Heritage Bank

Problem: Founded in 1875, Heritage Bank is Australia’s one of the longest-standing financial institutions. The company faced the challenge of increasing competition from fintechs and other digitally savvy financial institutions. 

Solution: The company implemented an intelligent automation solution to automate customer-facing, back-office, and middle-office processes related to operations, payments, financial crimes, and contact center services.

Result: The company automated around 80 processes and in some of these processes, the level of automation is 90%. Moreover, the accuracy of their most recent machine learning model is 98%. ³

3. Bancolombia

Problem: Bancolombia, the 10^th largest financial group in Latin America, wanted to develop a workforce that consists of human and robotic workers to enhance banking customer experiences, automate repetitive tasks, and increase efficiency.

Solution: The company adopted an intelligent automation solution to process structured, semi-structured, and unstructured customer data to transform their BPM.

Result: Bots automated hundreds of processes related to customer services, credit review, clearance and settlement, and capital markets. The company saved more than 127 thousand hours and achieved a 50% increase in customer service efficiency.⁴

Applying intelligent automation in banking industry

To fully realize the benefits of intelligent automation in banking sector, institutions must effectively balance human and machine capabilities. Here are key recommendations for implementing IA to enhance efficiency:

1. Streamline the customer journey

Customers now expect a modern, digital-first banking industry experience, which prioritizes immediate and exceptional service. Utilizing virtual agents, such as automated chat or voice bots, can address over 90% of customer inquiries.⁵ These agents can assist with online searches and provide direct answers, escalating complex issues to human agents as needed. This approach allows internal staff to focus on delivering high-quality, personalized service.

2. Future-proof services

As Gen Z’s purchasing power grows, financial institutions must adapt to their preferences for instant assistance and comfort with automation technologies. Offering self-service options that can seamlessly transition to live agents when necessary is essential. The aim is not to replace customer service teams but to enable them to concentrate on more complex inquiries, enhancing overall customer experience.

3. Leverage ready-made digital strategies

Many organizations struggle with fear of innovation,⁶ yet support is available to facilitate the realization of digital strategies. Partnering with experienced firms can provide access to proven processes and solutions tailored for finance automation. It’s crucial to ensure that new software integrates well with existing systems to expedite deployment.

4. Establish robust IA governance

Implementing strong governance frameworks is vital to managing risks, addressing biases, and effectively utilizing data. Institutions should develop policies and procedures to guide the design, testing, and operational phases of IA systems.

5. Test and upskill

Creating and testing workflows is essential to ensure alignment with organizational goals. Additionally, upskilling employees to work confidently with data-driven insights and automated processes is important. Designating select staff as AI trainers can help maintain the system’s flexibility and adaptability over time.

By partnering with experienced providers, financial institutions can ensure a smooth transition to IA, resulting in superior customer experiences through readily accessible and personalized service, while alleviating employees from mundane tasks and high-volume pressures.

If you want to implement intelligent automation in your business but don’t know where to start, feel free to check our comprehensive article on intelligent automation examples.

External Links

• 1. IBM Robotic Process Automation. IBM. Accessed: 10/25/2024.
• 2. Intelligent automation for consumer finance solutions. IBM. Accessed: 10/25/2024.
• 3. “Heritage Bank: Driving Automation With RPA and AI.” UiPath. Accessed: 10/19/2024.
• 4. “Leveraging intelligent process automation: 1300% ROI delivers increased CSAT and $7m in new revenue streams.” Automation Anywhere. Accessed: 10/29/2024.
• 5. Furthesthal, L. et al. Fear factor: Overcoming human barriers to innovation. McKinsey. Accessed:10/29/2024.
• 6. Furthesthal, L. et al. Fear factor: Overcoming human barriers to innovation. McKinsey. Accessed:10/29/2024.