RGP » Top Use Cases for AI ML in Finance and Accounting

The bank account opening process requires a manual data entry or even a client’s physical presence at the bank branch. If clients make a mistake during data entry, the client support specialists have to reach back to them to clear things up. This action takes time from your employees and slows down the overall process of registration. RPA for finance and accounting offers an alternative that can eliminate the chances of a mistake. Invoices, for example, require employees to spend a lot of time gathering data from different sources.

One of the best benefits of RPA for finance is budget planning and forecasting. Fetching details with the help of RPA bots from various reports and systems with accuracy will help create the variance reports, providing different angles to view and analyze data. Based on historical data and current information, comparison and trends can be drawn upon that are the proven successful ways to forecast and plan your business. RPA bots make the task quick and consistent by auditing and reconciling the data at every step and process with minimal human intervention in incorporating the essential elements of these tasks. The technology has evolved from performing simple individual tasks of automation to processing full-fledged automated reports, data analysis, and forecasting while interacting with other technologies. According to Grand View Research, the global RPA market size was valued at $2,322.9 million in 2022 and is expected to grow at a CAGR of 39.9% from 2023 to 2030.

Accounts Receivable Processing

When the software notices suspicious activity, it automatically downloads checks for a predefined period of time. This results in an increased number of solved fraud cases and increases the productivity of investigators. Moreover, this system works round the clock so that auditors can work on new cases right from the start of a new working day. Their task is to monitor the transactions on high-risk accounts and detect suspicious activity. Investigators have to manually check every domestic and international transaction made with this account.

As a result, businesses would maximize revenue, save time, and receive payments more quickly. Additionally, we will discuss real-world RPA use cases and shed light on the evolving role of RPA in shaping the future of finance and accounting practices. Automate quarterly cashflow forecasting for ongoing and new projects, improve collections, and reduce day sales outstanding by using RPA bots. Alternatively, read the purchase order number or service received number from the email and fetch the digitized document from the core system.

Generative AI Use Cases and Benefits for Enterprises

Improve efficiency and productivity by limiting manual intervention only for eyeball verification. RPA technology is relatively simple to implement and offers a lot of benefits. The implementation can start with applying RPA to simple rules-based finance and accounting tasks, and can be scaled up to take over more complex functions as the need arises. There’s a lot intelligent automation can do for finance and accounting departments. Namely, accuracy, compliance, speed of productivity and enabling higher-value work to get done. Luckily, while accounts payable and accounts receivable processes tend to be time-consuming, they’re also structured – making them a perfect candidate for intelligent automation.

• Digital workers are the software robots deployed within IA, designed with decision-making capabilities to mimic human actions.
• At the same time finance robotics must be scaled out of shared services and into other finance subfunctions such as procurement and tax.
• Besides, RPA software places the finale file version on the server automatically.
• We’ll assist you with processing cash, managing debts, and help with your accounts receivable (A/R) and collections strategy and policy to reduce overdue payments and DSO.

Without adequate controls, the situation could quickly get out of hand — to a point where instituting any control may be difficult. Pattern recognition helps to level the playing field by continuously monitoring transactions and looking for suspicious patterns that may not be visible by traditional human analysis. Identifying and preventing fraud is important to every financial professional, but this is easier said than done.

Manage cash

Once the team member approves the change, the bot makes the change in the appropriate system. As to fears that the robots are coming for the finance teams’ jobs, it’s important to include those teams on RPA projects both to allay fears and to find new opportunities, Gannon said. Project leaders can start by inviting a few people from a finance team into an automation lab for a few days a month to practice putting new bots into a production environment. Over the course of the rest of the month they will notice how the bot worked and can identify any in-use problems or limitations.

Digital workers also close aged orders and help resolve emailed invoice disputes from over 6,000 global requestors. Executing 98% of vendor, employee and intercompany payments, digital workers have enabled the finance payables team to focus on issue resolution and strategic initiatives instead of manual work. Overall, the combination of AI and ML with RPA enhances the potential of RPA in financial services, leading to improved efficiency, reduced errors, enhanced customer experiences, and data-driven decision-making. Performing the tedious tasks of timesheet validations, deductions calculations, tax calculations, overtime payouts, etc., can be managed by RPA bots with zero errors and delays. Also, bots can do tasks for hours in just a matter of minutes without getting tired.

Improved fraud detection with RPA

One of the leading commercial banks, Keybank, adapted RPA at an early stage to improve efficiency in a highly realistic manner. Account receivable that involves multiple steps of repetitive tasks of generating invoices and POs has been automated. Although the bank’s key focus is typically the payments, the automation of accounts receivable makes the payments process smooth and error-free from the first step to the last stage. Intelligent automation (IA) has the potential to automate work previously performed by humans while maintaining strict security standards in finance and accounting (F&A).

With RPA you can bill customers for your goods and services not only faster but in a way that is more reliable and efficient. With Blueprint, you can optimize the accounts receivable process regardless of the source – structured or unstructured. With more optimized processes, your team will be able to maximize revenue, save employees valuable work time, and bill customers faster, and receive payments sooner. Even if your invoicing processes are already automated, Blueprint can easily ingest existing automations for further, critical optimization.

Ways in Which Artificial Intelligence Is Revolutionizing the Financial Services Sector

Bots mimic some functions humans typically do, such as reading a screen in one application, copying the appropriate text, and then pasting it into another application. IT teams can use RPA platforms to create, monitor, manage, reuse and secure bots and their activities. RPA in finance can manage transactions, display bank account info, and provide interest rates for customers. This software can produce invoices, perform gross-to-net processing, and more. As the tasks involved are repetitive in nature, it is time-consuming and error-prone. RPA integrated with ML and AI can take up the tedious and monotonous task of performing repetitive tasks of generating invoices and POs.

To achieve the full benefit of finance robotics, corporate controllers need to restructure their workforce to enable automated work, free from human interference. Gartner studied how a global television company restructured its team to support finance robotics. 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. For example, Dean worked on one project with a logistics company that used RPA to identify discrepancies between the ERP system and the company’s reporting tool. The bot evaluates the discrepancy and uses various rules to determine if the issue comes from an error with the source data or the reporting repository.

Navigating Uncertainty: How Enterprise Risk Management Is Changing Healthcare

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 rpa use cases in finance and accounting to review, finding it and storing it in a convenient location for faster decision-making. RPA solutions allow businesses to collect customer information by accessing databases, gathering data from documents, and social media. Analysts spend a lot of time searching for information on complex government resources, the FBI, Interpol, and more.