Intelligent Automation in Financial Services & Banking in 2024

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.

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. In this research, we’ll explore various use cases and case studies of intelligent automation in the financial services industry.

Use cases

Customer service

Know your customer (KYC)

By using AI-driven RPA bots, banks can accelerate their KYC 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 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.

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.

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 documents rapidly to check non-compliant issues without any manual intervention.

You can also check our article on compliance automation.

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:

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.

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.

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%. ¹

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.²

For more on banking automation

You can also check our article on RPA in banking.

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.

We also have a data-driven list of intelligent automation solution providers. If you have questions, we can help:

External Links

• 1. “Heritage Bank: Driving Automation With RPA and AI.” UiPath. Revisited January 19, 2023.
• 2. “Leveraging intelligent process automation: 1300% ROI delivers increased CSAT and $7m in new revenue streams.” Automation Anywhere. Revisited January 19, 2023.