Financial institutes are racing to become more digital as customer and regulatory demands heighten. Digital transformation can often seem daunting, and many groups fail due to poor planning or preparedness. Total digital transformation is about building an embedded infrastructure capable of adapting and improving.

Banking institutions are under pressure to increase revenue as new competitors emerge, and technology is becoming increasingly essential in how customers interact with their finances. Because of this, the financial industry needs to adapt, ensuring easier transaction processing and improved customer satisfaction. After all, without customers, the banking industry cannot survive.

Let’s look at the various automation use cases and benefits among core banking operations, customer services and support, and finance and accounting.

What Is RPA in the Finance and Banking Industry?

Robotic process automation (RPA) is increasingly popular in the banking industry due to heavily regulated and complex processes requiring too many resources.

RPA has use cases in many sectors along with finances because it’s a quick and efficient solution to bottlenecks and monotonous tasks. RPA deploys bots to take over simple, rules-driven or mundane tasks, mimicking human actions such as mouse clicks and keystrokes. This alleviates the slow and more error-prone manual processes from human employees so their talents can be redeployed elsewhere.

In the banking sector, RPA can deploy software bots to perform time-consuming processes such as customer onboarding or running through compliance checkpoints, freeing employees to focus on higher-value tasks such as responding to customer complaints and ensuring customer loyalty.

RPA is often used in core banking processes, especially for those on the back end. But you can expand your automation efforts to encompass your entire business processes at a larger scale to orchestrate work at an enterprise-wide scope.

How does artificial intelligence make RPA better? 

With intelligent automation (IA), RPA is combined with other artificial intelligence (AI) technologies such as machine learning (ML) to fully integrate into process workflows, optimizing, learning and implementing better pathways by using a digital workforce. And those digital workers collaborate with your team for a more unified and consistent workforce.

Danica Pension, a financial institution in Denmark, used SS&C Blue Prism IA to give 80% of their staff a digital colleague to help with anti-money laundering (AML) and due diligence efforts. They’re now growing their capabilities further with ML and generative AI.

Intelligent automation within financial services orchestrates your entire banking operations, monitoring and improving automations as they run to ensure the highest efficiency, cost saving, and time to value. IA helps reduce operational costs, but it also reduces operational risks, which are often caused by human errors.

Now let’s talk about where RPA and IA fit in perfect harmony within the finance and banking industry. We’ll discuss their benefits and some use cases to show you how they can be applied effectively to improve your financial operations.

The Benefits of RPA and IA in Banking and Financial Services

Many financial institutions are already running quick-win RPA solutions. But through RPA implementation with IA – which is the combined technology of artificial intelligence with RPA and ML – you can increase your benefits tenfold.

But what are the benefits of having automation in banking? We’ll go over the big six.

Cost-efficiency

RPA automates repetitive tasks, reducing manual effort and saving on operational costs. These cost savings help banks stay competitive in an increasingly crowded market.

Now, let’s consider AI-powered robotic process automation in loans.

Digitizing the loan process allows you to increase the number of loans done per day without sacrificing quality or accuracy. That means less time spent analyzing what went wrong or digging out mistakes caused by human errors – manual labor that would otherwise result in high costs for your organization.

It also means customers are served on their loan requests sooner. Plus, with lower overhead costs for automation and re-skilling existing staff rather than hiring more people, you’re increasing the level of your return on investment (ROI).

Other examples where financial institutions can apply IA for cost efficiency:

• Auto form filling
• Customer identification
• Elimination of redundant data fields
• Loan contract conversion to a machine-readable format

Digital and operational resiliency

Resiliency is essential to the finance industry. This work requires security and implementing automation into those systems may seem daunting at first glance. That’s why SS&C Blue Prism has developed pre-built automation solutions. They easily integrate with existing legacy systems using AI capabilities.

With intelligent automation, your digital workforce can scale to handle data securely, developing new and more efficient ways to work entire processes and adapt to constant change.

Risk, security and compliance

Incident management, fraud detection and risk management are critical in banking and require timely, accurate data analysis. Digital workers perform tasks such as data analysis, detecting fraudulent activities and risk scoring so banks can detect and mitigate risk quickly and efficiently.

IA can also help identify potential money laundering schemes by detecting suspicious transactions or patterns that might indicate fraudulent activities. This ensures compliance with regulatory standards and saves innumerable costs by avoiding legal or reputational risks. Digital workers can also reduce cyber threats by using machine learning technology to conduct near real-time threat analysis and alerts.

Higher compliance equals lower risk, and IA reduces your human employees’ workload by freeing them of these laborious tasks and ensuring everything is accurately reported in real time. Just look at how a financial services provider used SS&C Blue Prism IA to generate environmental, social and governance (ESG) reports 75% faster.

Here are a few more examples of how IA ensures security and compliance for financial institutions:

• 100% auditability and compliance on all loans processed
• Reduced risk of regulator fines and poor ratings
• Decreased risk of large, incorrect settlements
• Liquidity and treasury trade risk management
• Improved quality, storage and classification of data
• Screening, alerts and incident management

Employee satisfaction

IA helps free up employee time from routine tasks so you can reallocate your staff to strategic, higher revenue-generating and customer-focused responsibilities. Digital workers handle data entry and other work prone to errors, removing frustrating handoffs, delays and bottlenecks from workloads.

With IA, your digital workers can even help your financial staff with speedier decision-making, such as those in the asset management business covering numerous portfolios. Find out more about automatic portfolio reconciliation and rebalancing services.

Agents can employ a digital worker alongside them to help analyze data and make informed decisions. This shortens the lending process, using digitized documents and automated tasks from loan processing, insurance claims, funding, administration and monitoring, default management and so on.

Customer experience

By automating the manual processes in customer service, IA can help banks provide faster and more efficient service. This helps avoid costly mistakes and improves the bottom line. Plus, with better service, banks can earn customer engagement and loyalty, ensuring long-term success.

IA can also help identify useful products and services for customers, so they only see what’s relevant to them without being bombarded by impersonal product dumps. It also enables new customers to open a bank account and apply for additional products in minutes with automated know-your-customer (KYC) checks and affordability calculators.

RPA adoption with other AI technologies in customer experience banking can even allow your customers to interact with AI chatbots for faster response times. If their request is simple, the bot can handle their queries without human intervention. If the issue is more nuanced, the chatbot simply escalates the procedure, notifying a staff member of the customer’s needs.

In loan servicing, RPA can help reduce the processing and review time for loan applications from days to hours. The documents are processed faster and with 100% processing accuracy, covering risk management, reducing customer churn and further improving customer experiences.

Discover how ABANCA used SS&C Blue Prism IA with generative AI to respond to customer inquiries 60% faster and enhance the customer and employee experience.

Other examples in which RPA and IA together can improve the delivery of experiences to customers:

• Reduce customer onboarding time
• Increase credit card application processing
• Support lost or stolen card replacement, charge reversals, billing or card blocking
• Complete customer KYC processes and risk assessment ten times faster
• Faster and more detailed anti-money laundering (AML) client reporting
• Improved customer accounts maintenance, fee documenting, ID verification, cash withdrawals and deposits, and the closing accounts process

Connected ecosystems

Finally, IA unifies your human and digital workforce, improving operational efficiencies and creating better communication between systems and departments. That means less information gets lost, fewer errors bog down workflows and customers receive the high-quality services they deserve.

These are just some of the benefits IA can provide to the financial services industry. IA helps banks remove repetitive tasks, improve efficiency, reduce costs and enhance customer service, among other things. And as the financial sector evolves, IA will play an increasingly crucial role in helping banks stay competitive and meet their customers’ changing needs. 

AI and RPA Use Cases in Banking

The banking sector and any financial services require accuracy, resiliency, security and quality, but in the era of quickly evolving technology and expectations, many are also looking to improve efficiency, customer experience, employee satisfaction and connectivity.

We’ve mentioned the numerous benefits of IA in financial services. Now, let’s talk about how you can make that happen in more detail.

Account opening and onboarding

As we briefly mentioned before, adding a new customer to the system can be complex and time-consuming when completed manually.

But with RPA, these manual banking processes can be automated to reduce the time and resources required to onboard new customers. RPA bots work through process steps, fulfilling mundane tasks such as data entry, background checks and document verification.

Trade finance

Trade finance involves the ensured delivery of goods and payments between multiple international parties. Banks use letters of credit and bank guarantees, among other documents, to communicate with companies on these trades, all of which need to be processed. Digital workers can process this information quickly and accurately for smoother communication between the multiple parties involved in trade finance.

SS&C GlobeOp increased their trade break solution by 20% with SS&C Blue Prism intelligent automation, returning 3,680 hours back to the business each month.

Private equity

Most equity analysts still create research reports manually, logging into multiple systems to extract customer data. With a digital equity research assistant augmenting their work, they could automatically create a draft report and synthesize the data by evaluating the organization’s products, strategies, competitors, etc., to make financial projections.

Read about how you could streamline equity research with generative AI and LangChain.

Loan applications

Loan operations and credit operations both require significant resources and expertise. However, manually audited loan servicing can take several days to process. The processes used to determine loan eligibility can be extensive and include performing credit checks, income verification, direct debit cancellation, account closures, CHAPS payments, foreign payments, audit reports, internet banking application processing and PIN pulls.

Now with IA, those manual banking processes can all be automated, improving accuracy and reducing the resources and time required to complete them.

Examples of automated loan application operations:

• Online: A customer’s online loan application is fully validated by a digital worker and then passed into the mainframe for further processing.
• By phone: When a customer calls to apply for a loan, a customer service agent can fill in the electronic form while speaking with the customer. This form is then sent to a digital worker to be processed without manual intervention from a staff member.

Accounting

Accounting requires reporting accuracy and audit trails for regulatory standard compliance. With RPA, record keeping and reporting of all financial events and transactions are done through an automated system and the data is stored securely.

Automation can help with accounts receivable, ensuring timely payment collection is also done with accurate recording and accounting. On the accounts payable side, RPA bots can create entries and manage vendor data so your employees don’t have to.

Some other accounting roles an RPA bot can take:

• Employee payroll
• Overtime
• Paid leave
• Pensions
• Taxes

Forecasting and budgeting

RPA bots can conduct financial data extraction from multiple sources in their processes including databases and spreadsheets, then consolidate them into a single report for analysis. They can also be programmed for trend analysis and scenario modeling to provide insights into financial health and forecast future expenses and revenue.

How to Implement RPA and AI Into Your Bank

RPA and AI have the potential to empower your customers and employees and grow your revenue through their ability to adapt and scale. They can work across banking, financial services and insurance, among others. As this technology continues to evolve, it’s important that the financial sector stays involved and looks at its automation with an enterprise-wide scope.

Just look at how our customer Old Mutual upgraded and migrated their systems to allow a digital workforce to reduce 70% of their manual transactions without any system downtime.

The world is increasingly more reliant on digital services. We expect everything to be available at our fingertips, from information accessibility to immediate responses to questions. And that’s not likely to change.

The AI and RPA use cases across financial services will continue to grow with the emergence of gen AI, predictive AI and other forthcoming cognitive technologies. All you need for adapting these automation technologies is to think strategically and innovatively about the future of your organization and people.