Learn the latest ways to use robotic process automation (RPA) to drive efficiencies and generate value for your financial institution.
For years, finance teams have used robotic process automation (RPA) to improve the speed, efficiency and accuracy of specific tasks. Now, they’re taking RPA to the next level by combining it with machine learning (ML). In fact, recent Gartner research shows that around 80% of finance leaders have already implemented or are planning to implement RPA.
Finance automation got a kick-start in the 1990s, when MIT researchers developed the optical character recognition (OCR) technology for reading the handwritten parts of checks with high speed and accuracy. Beyond check processing, today’s banks and financial services firms use RPA tools to interact with a wide range of critical applications, such as enterprise resource planning (ERP) and customer relationship management (CRM) platforms. The tools can manipulate data, trigger responses and communicate with other systems in a way that previously required human interaction.
The latest RPA solutions use the integrated capabilities of artificial intelligence (AI) and ML models to “review” reports, flag potential issues and learn from experience. The RPA solutions have a high level of security for finance functions, and they work without interruption for substantial cost savings.
What’s the best way to consider implementing RPA in your own financial institution? This article outlines five RPA use cases that are worth a closer look.
The evolution of RPA in finance
Used in a variety of industries, robotic process automation (RPA) refers to the use of low-code software “bots” to handle the repetitive, time-consuming tasks of human workers — such as invoice processing, data entry, compliance reporting, etc. RPA is part of the greater trend of hyperautomation, enabling organizations to move from automation that mimics human actions toward automation that uses data to optimize end-to-end finance processes.
The robots used in RPA are ideal for handling a high volume of recurring tasks without human intervention. This frees up employees to focus on more meaningful work, from building strong relationships with customers, to analyzing data to gain a competitive advantage, to turning great ideas into new financial products.
How RPA works with AI and ML
AI and ML technologies boost the power of RPA by doing the following:
- Preventing RPA bots from breaking down if any underlying rules change
- Finding patterns in historical data to identify the most relevant information for decision-makers
- Analyzing data and predicting outcomes that help in contextual and informed decision-making
For example, let’s say you’re using RPA software to help consolidate data from different sources about customer payments that are scheduled to come in, along with invoices that will need to be paid. ML can predict the likelihood that each customer will make their payment on time. This prediction informs actions like reducing administrative costs, extending supplier payment terms or investing in new equipment. 
Five ways to use RPA in finance
When getting started with RPA technology, many finance leaders look for tasks that are the most prone to human error, cause the greatest workflow bottlenecks or create inefficiencies that lead to poor customer service and employee engagement.
Here are five areas to consider using an RPA platform, powered by AI and ML, to transform your financial institution.
1. Drive sustainable growth
The competition for banks and financial services firms is fierce, particularly in a world of low interest rates and costly digital transformation initiatives. One way to increase revenue is by identifying cross-selling opportunities for new financial planning products. Enter RPA.
With an RPA implementation, your financial institution can have customer behavior data automatically sent to specific people in the organization. ML models help group customers into categories based on their behavior, so the most appealing products or services can be recommended to them. For example, banks know which customers might be most interested in opening a new line of credit.
2. Boost operational efficiency
RPA technology drives down operational costs by automating the transaction-heavy, manually intensive tasks that require reconciliation. Digital workers can retrieve and compile data from multiple back-office systems, reconcile amounts (say, for invoice payments or billed amounts) and take action to resolve breaks in real-time. For example, using natural language processing, digital workers can analyze the text that comes in with invoices and automatically route issues to the correct team.
3. Revitalize the customer experience
Today’s consumers have more options than ever for financial services, and they have high expectations for personalized services, fast processing times and responsive support. RPA tools can improve all aspects of the customer experience, from initial onboarding to account updates. New customers can open new accounts and apply for additional products in minutes with automated Know Your Customer (KYC) validation.
RPA also helps notify stakeholders about specific events, such as customer complaints about a new mobile banking feature. With ML, data about similar past complaints can be filtered to predict the most impactful improvement opportunities.
4. Fight financial crime
To help detect and prevent fraud, financial institutions need the right cybersecurity technology for due-diligence checks, sanctions screening and transaction monitoring and investigation. RPA improves the speed and accuracy of fraud detection. First, RPA bots confirm whether data adheres to federal anti-money laundering (AML) guidelines. ML helps by analyzing variances to infer why they may have happened and to flag any instances of potential fraud.
5. Manage regulatory compliance
To limit the risks of regulatory fines and reputational damage, financial institutions can use RPA to strengthen governance of financial processes. RPA helps consolidate data from specific systems or documents to reduce the manual business processes involved with compliance reporting. ML goes further by deciding what data an auditor might need to review, finding it and storing it in a convenient location for faster decision-making.
RPA in finance and IBM
IBM is building the industry’s most comprehensive suite of AI-powered Automation capabilities. With IBM Robotic Process Automation, financial services firms like Credigy Solutions can automate more business and IT tasks at scale with the ease and speed of traditional RPA. Software robots, or bots, can act on AI insights to complete tasks with no lag time and accelerate digital transformation.
 Gartner, “When and How to Use Machine Learning with RPA in Finance,” October 30, 2020.