Up to 44% of work performed by the finance function can be automated. Automation offers a plethora of benefits including improved cycle time, better accuracy and compliance, as well as enhanced supplier experience. Not convinced? According to Gartner, RPA technology can cost as little as one-fifth the cost of an on-shore employee, or one-third the cost of an offshore employee. That’s a significant cost saving, in the region of 20% to 33%.

Robotic Process Automation (RPA) leverages scripts, or ‘bots’ that are governed by business logic and structured inputs to automate repetitive business processes. These ‘bots’ can be used to mimic or emulate traditionally human-performed tasks within an overall business or IT process. As CFOs look to reach a state of autonomous finance and step up the digital maturity of the finance function, RPA technologies offer executives a way to kickstart their journey. When coupled with machine learning and artificial intelligence (AI), the capabilities of RPA can be bolstered even further. This blog explores some of the most popular use cases of RPA for finance employed by companies across the globe.

A KPMG study conducted in Hong Kong revealed that 34% of companies surveyed aimed to achieve basic process automation across their finance functions within the next five years, and 26% were targeting enhanced process automation in the same time period. However, 30% of respondents stated that they didn’t know where they would go about it, showcasing a need for awareness generation on how RPAs can help bolster finance functions.

Where can I use RPA in my finance function?

Improve overall efficiency via lower payroll costs
Payroll is one of the most critical tasks of the finance function, and errors or non-compliance can have far-reaching effects. By using RPA to automate your payroll functions, your company can ensure that payroll submissions are 100% accurate, payments are made on time, ensuring full compliance, and minimizing areas for disputes.

Purchase order and invoice processing
Processing purchase orders and invoices is a repetitive and time-consuming task that can quickly eat up valuable staff time, particularly in organizations that see a lot of purchases and transactions every day. RPA bots can ensure perfect accuracy, cross-check with relevant supplier and material databases, and ensure perfect PO and invoice generation. These can be subjected to an approval mechanism by a human user, and automatically dispatched to the relevant parties upon approval.

Customer management
Customer management is another area that can significantly impact productivity, particularly for companies with a large number of small customers. Customer details and payment details may change periodically, and it would be tedious for a human to keep track of these. RPA technologies can be used to manage customers, provide reports on customer activity, and periodically reach out to customers and encourage them to update their details.

Optimized internal reporting
Finance functions are typically required to produce a myriad of reports for various internal purposes. With RPA, the most commonly used reports can be defined, and the system is automatically programmed to generate these at set intervals, or upon the request of a user. Since gathering data for financial reporting can be a particularly tedious task, especially across organisations where many financial function tasks are still manually performed, RPA can unlock significant staff time savings. Free of operational clutter, finance professionals can finally focus on their strategy.

Accurate external and tax reporting
External reports must be 100% accurate, particularly those used for tax purposes. RPA technologies can be leveraged to generate external reports such as those for investors and shareholders, as well as tax reports for taxation purposes. When programmed with the correct instructions and process guidelines, RPA can generate 100% accurate and compliant reports, ensuring that your company’s external and tax reporting is perfect.

Comprehensive asset management
Asset management is another area where asset-rich companies find their financial functions increasingly spending time on performing routine tasks. With RPA, the process of asset management, including registering new assets, tracking asset performance, depreciation and disposal can be fully automated. This ensures that your company’s asset values are as accurate as possible, and all assets are accounted for.

Budget planning and forecasting
Going beyond routine tasks, RPA can be used in conjunction with AI and ML technologies to tackle more complex budget planning and forecasting activities. Data can be gathered, analyzed and presented from various sources and angles, and trends identified.

How a leading Australian F&B manufacturer leveraged RPA to transform its finance function

The leading Australian food and beverage manufacturer had its sights set on ambitious growth. In order to achieve its goals, the manufacturer realized that it needed to rethink the way it managed its finance function. As finance executives were using multiple applications for the same operation, the lack of integration meant that employees had to spend a lot of time extracting data from disparate systems.

The manufacturer had to allocate dedicated staff to cleanse the data prior to uploading it to their ERP. Employees also had to manually compile and distribute reports. This meant that key knowledge workers spent a significant amount of their time on mundane and repetitive tasks. The lack of an integrated system also meant that response times for customer, vendor and other key stakeholder queries took over a day.

Fortude gathered a cross-functional team of technology and business stakeholders to determine which of the manufacturer’s financial workflows will be automated.

• Accounts Receivable: 5 processes including, Inventory Reconciliations, Claim Reconciliations, Invoice Processing, Collection, and Reporting
• Accounts Payable: 4 processes including, Inventory Reconciliations, 3-Way Matching, Payments, and Reporting
• Costing: 2 processes including, Inventory Reconciliations, and Reporting
• GL and Reporting: 3 processes including, Bank Reconciliation, Inventory Reconciliations, and Reporting
• Fixed Assets: 3 processes including, Creating, Managing and Depreciating Assets, Inventory Reconciliations, and Reporting

We recommended and implemented a UiPath-based RPA solution to increase the efficiency and accuracy of the finance function. The business achieved the following outcomes:

• Reduction in overall cycle time by 90% for all finance related processes
• Accuracy rate of 99%
• Enabled the customer to operate 24*7, whilst the bots worked during holidays and early mornings
• Freed up business users from repetitive work, resulting in happy people doing meaningful work
• Increased customer satisfaction in direct relation to the automated finance processes – achieved a Net Promoter Score of 10 (customer delight)

What are the benefits of leveraging RPA for the finance function?

Perhaps no other function of the enterprise has as many repetitive, routine tasks as the finance function. Whether it’s inventory and claims reconciliations, invoice processing or managing depreciating assets, most finance processes are high cost and of very little interest to employees. Automation holds the key to transforming the role of the CFO and the finance team, thus placing the function at the strategic heart of the enterprise. The benefits of RPA are numerous, and a few of them are listed below:

Improved accuracy through reduced human errors
Human errors are unavoidable, particularly when mundane and repetitive tasks are performed in large volumes by the same person. However, these processing errors can have costly consequences, especially when they are related to the finance function. RPA technologies can minimize some of this risk and ensure the accuracy of repetitive tasks. This reduction in errors also means that finance teams are able to ensure complete compliance.

Better use of resources
Team members will also be better equipped to participate in strategic activities if the proportion of their workload comprising of routine and repetitive tasks is greatly reduced.

Enhanced access to data and insights
Data is power, and businesses tend to generate colossal amounts of data, across various formats. A human operator would find it almost impossible to crunch thousands of numbers, collate data from different sources, and derive actionable insights from this raw data. This is where RPA can shine, performing intelligent data analysis and unearthing hidden opportunities.

Smoothing peak workloads and increasing efficiency
Your peak workloads may not occur at your employees’ best times. Employees work on a clock after all, and have their own peak performance windows throughout the day. RPA bots don’t have this limitation, and can work round the clock, at peak efficiency.

Why is transforming finance with RPA so difficult?

While there has been a significant leap in automation adoption over the years, organisations are far from reaching their digital ideal. Deloitte’s Global Intelligent Automation survey results reveal that executives believe that enterprises are yet to tap into the full potential of automation. The technology is readily available, and skilled resources with extensive multi-industry deployment experience exist. However, barriers such as process fragmentation, lack of a clear vision, lack of IT readiness and resistance to change tend to persist as outlined below.

Lack of awareness
Lack of awareness continues to be a major stumbling block in RPA adoption – most of which stems from a lack of understanding as to what RPA can accomplish. Employees and managers tend to view RPA as yet another complex and fearsome IT system. A proper understanding of what RPA is, as well as how it can be tailored to a specific functional need can alleviate this.

On the flip side, if an enterprise has already embarked on its RPA journey in another function and is encountering difficulty with it, it might put off executives from deploying it in the finance function. Finance heads must understand that there is a learning curve involved for all users of an RPA, and certain functions may appear more complicated at the outset, until a modicum of adjustment and re-engineering is performed. Once RPA is properly implemented, and users have been comprehensively trained, the benefits far outweigh any shortcomings.

Insufficient budget
As RPA implementation requires a sizable investment, organisations may not be keen on embarking on their automation journey. However, as one financial services executive interviewed during Deloitte’s Global Intelligent Automation survey explains, radically re-engineering processes and using multiple intelligent automation tools resulted in the organisation achieving a cost reduction of over 70% in the targeted area. Budgeting for an RPA system is, therefore, an exercise in medium- to long-term cost optimization, rather than short-term spending. Finding a commercial model that will suit your demand profile and testing can help overcome this barrier.

Employee resistance
Employee resistance is a prevalent issue in any technology implementation. Employees tend to feel threatened, as they perceive a risk of job losses. While this can be a legitimate concern, companies will usually implement an RPA to streamline workloads and optimize employee engagement by automating repetitive tasks. This enables employees directly involved with the RPA to realize lower workloads, and creates more time for more challenging, cognitive work. Workforces within organisations that are implementing and scaling automation are more supportive than those at organisations piloting automation (52 per cent versus 34 per cent), which suggests that workforce buy-in is possible by proactively communicating the ‘what,’ ‘why’ and ‘how’ of automation, the benefits and reskilling opportunities that come with it.

Governance concerns
While regulated sectors such as banking and financial services have some legal requirements and constraints for process automation, RPA is generally seen in a positive light by regulators at the moment. This is partly attributed to the fact that the technology is fairly young and legal regulations have yet to be introduced in many countries. As adoption grows and RPA technologies mature, it is likely that regulatory complexity will grow. At this point in time, however, RPA technologies enable organisations to improve oversight, and ensure compliance and high levels of auditability of all business processes.

Rising to the challenge of implementing RPA for the finance function

As you assess your company’s readiness to embark upon the RPA route, the first indicator is to critically evaluate your company’s willingness to adopt new technologies and automate processes. Companies that are dynamic and evolving to stay ahead of the rest will find this much easier than the laggards who prefer to wait it out and see what everyone else is doing before making a move.

Qualifying factors for automation are critical to evaluate the best starting point for an RPA system implementation. The best processes to be automated are those that are routine, monotonous, and highly regularized. When evaluating the finance function as a whole, the relatively routine and fixed nature of the majority of tasks performed by it means that there is enormous scope for automating functions, as well as their sub-functions. This massively optimizes departmental workloads, and realizes the benefits outlined earlier in this article.

The full potential of RPA to transform the finance function is achieved when AI and cognitive technologies are integrated. Intelligent automation presents a range of possibilities to integrate AI and machine learning capabilities to stretch and break down the limitations of robotic process automation. While this stretches as far as AI-powered decision making, finance executives can start off by exploring customized optical character recognition (OCR) and AI technologies to replace the manual entry of data from images of checks or receipts.

Finance teams deal with documents in varied formats, as customers and suppliers often present data in their preferred layouts, ranging from papers and PDFs, to email and spreadsheets. AI-powered document processing learns to locate and read the various formats, while the machine-learning component improves accuracy over time. This helps overcome the limitations of traditional RPA bots such as its inability to process unstructured data.