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Finance functions are under pressure to shrink costs, improve speed, deal with increasing volumes of complex data, and bring new value to business partners through actionable insights. RPA & AI are providing finance functions with new opportunities to achieve these goals.
RPA/AI is often associated with the pure automation of processes when in fact, this is only part of the story.
Robotic Process Automation is defined as: “the application of flexible tools to automate manual activity for the delivery of business processes or IT services”. In this context, RPA could be seen as a virtual worker, trained to perform any given repeatable and standardised work flow. This definition covers the traditional automation-related interpretation of RPA/AI.
Sia Partners believes that automation is only one of the outputs of RPA/AI, the other is the ability to deliver tangible value to the business through intelligent and self-improving systems and processes.
Rules based software robots are being used to undertake processes such as data gathering, reporting, matching and reconciliation, and complex multi-faceted processes such as period end closes.
Stemming from the types of processes that are candidates for automation, general finance function tasks that lend themselves to automation include:
Additionally, the latest advances in RPA can gather data from different sources including anything from company databases to images and videos; this greatly increases the scale and scope of available data.
While the examples above focus on how automation is being employed by finance functions, we believe there is extensive scope for Artificial Intelligence solutions to increase the impact that finance has on the overall business.
Due to its ability to benefit the finance function, AI will become commonplace within the finance function within the next five years. Sia Partners expects the following areas to be of high priority:
|Opportunity example||Practical example|
|Driver-based forecasting using historic learning||AI algorithm to adjust drivers used in planning budgeting and forecasting based on historic performance, trends and market variables|
|Within retail, live Price Volume Mix (PVM) analyses with sensitivity-based alerting and communications||Daily snapshots of revenue, volume, price, and product mix performance are automatically loaded to a program daily, from POS and ledger systems producing live PVM recommendations|
|Intelligent risk-based alerting and communications||High risk transactions types are flagged in real-time, as they occur, following a defined events path|
|Automated competitor product category and channel price-mining and analysis||Internet-based data-mining software could be used to provide live analysis against defined product categories|
|Automatic invoice processing, classification, and filing||Invoices are scanned automatically and matched to supplier and PO codes, after which it is electronically sent for approval to a defined based on value|
|Auto-approvals for transactions intelligently defined as low-risk with continuous learning functionality||Transactions are categorised by risk based on multiple parameters, which impact individual approval level and oversight. Exceptions are identified and the system "learns" a new definition of risk over time|
Robotics Process Automation (RPA) and Artificial Intelligence can deliver real benefit to finance functions that choose to implement them effectively. We have identified four distinct areas of benefit:
It is possible to install RPA systems on top of existing IT systems without replacing core line-of-business and ERP tools.
From a maintenance perspective, RPA is often considerably cheaper than employing IT systems or full-time employees (cost estimated to be of offshore FTE, or of onshore FTE)
Automating processes as an alternative to offshoring has several benefits:
By providing an alternative to inefficient and error-prone manual entries, RPA increases the overall quality of work produced and reduces one-off costs and wasted resources.
RPA can automate high-frequency tasks and move operations to downtime (i.e. overnight); this can reduce processing time (up to 50%-70%) and free employee capacity. Capacity can be re-employed on value adding activities for which human judgement is key.
Artificial Intelligence, layered on top of RPA and existing IT infrastructure, can uncover actionable insight that can improve the overall performance of a company through the interaction of finance and the organisation at large.
In contrast to the previous three areas, tied to process automation, few companies have explored how self-improving artificial intelligence approaches can digest increasing amounts of data to identify trends that inform decisions that range from pricing and marketing strategies to fraud detection. Sia Partners believes that the largest benefit of RPA/AI application to the role of a finance function stems from applications of Artificial Intelligence.
These are some examples of its impact on costs and speed:
RPA/AI offers broad possibilities of improvement and direction is dependent on functional objectives. Below are steps that can be taken to identify and subsequently implement RPA/AI opportunities:
A ‘data driven revolution’ will lead to significant changes in how finance functions operate in the coming years. Key items to consider: