What Is the Meaning of Automation Within Finance?

When we think of the broader world of financial technology, especially in the wake of COVID, we usually think of fintech unicorns, challenger banks, and blockchain fuelling the perpetual rise of crypto and monetary security in a rapidly digitising world.

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This corner of fintech parallels other innovations in marketing technology, manufacturing, and logistics technology. The elevator pitch of automation is taking everyday, routine workflows that require repeated data input or simple uploading or forwarding of data, automating it, and giving your enterprise more time to focus efforts elsewhere. Like other industries, especially ones that are administrative-heavy, automation within finance has already been a brutally effective innovation, saving millions of person-hours and reducing the administrative burden on teams worldwide.

The move to machine-first administration is still strong, and innovation in our sector is still progressing. From AI chatbots automating (and improving) online customer relationships and troubleshooting to machine learning, which maps investment risks or security issues through rapid digital auditing, automation has shown its hand as easy to understand and quantify, quick to implement, value-driven and good for the bottom line.

But it has only been achievable through cultural change (the acceptance of both secure cloud data centres, challenger banks, and SaaS accounting software), technological accessibility (usable tech is cheaper and latency improved, SaaS more effective and more value-driven), and CFOs understanding what automotive processes they specifically need, and how they can derive value from them.

It’s also wise to see automation as one part of your organisation’s future – in a McKinsey report titled Harnessing automation for a future that works, “very few occupations – less than 5 per cent – are candidates for full automation”, but “we estimate that about half of all the activities people are paid to do in the world’s workforce could potentially be automated by adapting currently demonstrated technologies”. Interestingly, the report notes that macro-levels of automotive change may take decades to implement, but on a micro-level – within small organisations and individually – automation will make itself felt much more prominently and immediately.

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What Is the Definition of Finance Automation?

Finance automation can be described as the use of technology to execute financial processes and operations.

In the last 10 years significant step changes have been made in ERP systems design and functions that, whilst increasing their inherent complexity, enabled users to carry out financial and business processes and complete tasks in a more efficient manner.

New tools such as Robotic Process Automation (RPA), automated workflows, artificial intelligence applied to data analytics allow finance professionals and finance teams to significantly reduce the amount of manual tasks performed, save time in financial closing, as an example, as well as provide insights to execute sound business decisions and manage financial data.

It is essential to realise that automation in finance does not mean that there is no need for finance teams.

Instead, automation makes processes simpler, faster, more efficient, reduces the chances to commit fraud and allow finance teams to focus on “what’s important and relevant”.

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Automation Finance: Tools

Below are examples of finance automation technology readily available and in use today that have a significant impact on finance operations and finance departments:

Robotic Process Automation

Robotic process automation (RPA) is a software technology that makes it easy to build and deploy software robots that replicate human actions via interaction with digital systems and software.

Machine Learning Algorithms

Defined as the method by which an Artificial Intelligence system conducts its tasks, predicting output values from a given set of input data.

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Chatbots and Conversational Agents

Computer programme that allows people to obtain information from machines using text and voice.

Process Mining

Allow companies to analyse their processes and identify strengths and weaknesses (for example if the invoice process takes too long or costs more than it should).

Document Automation

Enables the generation and processing of electronic documents (OCR being an example).

What Finance Processes Are Most Suitable for Automation?

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This section will provide high level guidelines on the most common processes within a finance department that lend themselves to be migrated to automation:

Accounts Payable

Processing invoices has historically been one of the most time consuming and repetitive tasks that a finance team has to deal with in their careers.

Whilst OCR (Optical Character Recognition) of invoices has been around for over 20 years and has reduced human intervention and manual work, today’s “procure-to-pay” solutions (Ivalua being an example) also enable automatic matching with purchase orders. This includes other features such as “supplier statement reconciliation” and “portal for interchange of communications” between company and suppliers etc.

Machine learning algorithms can also be embedded in ERPs to generate “intelligent” payment proposals that would:

• Benefit cash flow management without the need to execute manual steps.
• Allow early payments without detriment to the financial position of the company.
• Enable integration with multiple systems (e.g., online banking) without human touch.
• Ultimately increase efficiency in Accounts Payable by allowing users to focus on critical decision making.

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Accounts Receivable

Accounts Receivable, part of “order to cash process” as we call it these days, usually encompasses amongst others the exchange of financial documents (such as invoices, credit notes) as well as credit control process.

It is one of the (many) customer-facing roles in all organisations, and it is also the most time-consuming, if it is not helped by the technology that is available today.

Whilst EDI, the auto-sending of invoices and customer statements, are features that are expected now from any financial software, many organisations have also invested in customer portals (provided for example by Microsoft and other top tier software houses) to give customers more visibility of their financial position, order fulfilment, and credit status, without the need for human interaction.

RPA and machine learning tools can also be utilised to manage the credit control process, allowing businesses to automatically block shipments or orders from overdue accounts, and execute the dunning strategy without the need for finance teams to send constant reminders.

Chat bots can be set up on portals so that customers can access their information or request assistance without the need for human intervention; this cannot be discounted especially for international organisations that operate across multiple time zones and do not (and should not) want to wait for the next business day to deal with queries.

Record To Report

Activities usually associated with general ledger accounting processes, financial close, financial analysis and financial reports.

Finance automation software (such as Blackline) provides automation solutions that can help immensely in account reconciliation as well as financial close; the latter being one of the most time-consuming tasks involving manual processes that a finance team has to execute.

Data Lakes and data mining tools can also be invaluable to gather data from the most disparate systems to provide business users with a complete overview of financial data and generate complex financial and statutory reports at the press of a button.

Expense Management

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Concur, Zoho, and Expensify are examples of finance automation platforms that have revolutionised expense management by automating processes (including validation and audit) so that business users do not have to rely on finance teams entirely but effectively utilise “self-serving tools”.

Such tools also generate expense reports that provide valuable insights and trends that the business can use to its own advantage by for example entering partnership with hotels and airlines based on expense patterns.

Procure To Pay

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A “procure to pay” business process involves the interaction of multiple departments in an organisation (Supply Chain, Inventory Management, Finance amongst others).

Coupa BSM, SAP Ariba, Oracle Fusion Procurement, and Ivalua represent some of the most common platforms that create a virtual ecosystem between suppliers, business users and finance teams improving the flow of data, transparency, and the efficiency of processes.

Tax

Software platforms such as Thomson Reuters can manage easily routine tasks undertaken by finance users, whether it’s periodic VAT filings and VAT reports, Corporate Tax or even tax planning.

Financial Planning and Analysis

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An FP&A process usually involves combining data from potentially multiple applications to build insights, plans and reports.

Anaplan, One Stream, Wolters Kluwer and others are examples of platforms that combine scenario modelling, workflow automation, data integration and configurable models simplifying critical decision-making and core processes.

So How Is Automation Changing Finance and What Do Companies Need To Be Aware Of?

The Weight of Your Legacy

• Legacy systems and legacy thinking will stymie any digital transformation.
• Legacy systems are the bane of CTOs and CFOs all over the world and will continue to limit innovation, encourage security risks and slow down business.
• Legacy thinking, however, is a more subtle barrier to change – finance managers need to understand that traditional, human-centric models of workflow management, assessment and change cannot be applied to machines – digital tools are expressly designed to speed up the process of data transfer between departments, reconcile accounts immediately, and even prepare journal entries and reports and map future spends through algorithms. This, therefore, to a degree comes down to trust.

Change management and the impact that it has on users and the wider workforce should not be underestimated; whilst software implementations has become much simpler in the 22nd century, and agile methodologies improve the speed, the benefits of such platforms need to be clearly understood and users need to embark on the journey with enthusiasm.

The Threat of a Machine Takeover

In another McKinsey report, Bots, algorithms, and the future of the finance function, the changes to workforces have not been understated – in one example, one global financial firm is looking to release a quarter of their 20,000 strong workforce over the next 24 months due to the rapid automation of tasks. It cannot be ignored that automation will cause labour flux.

Their Advice Was Simple:

• Slow down the rollout of automotive tasks,
• Focus on how automation works alongside new forms of staff and task governance,
• Be thoughtful in your redeployment and understand that the human and machine can and should work together,
• Revamp your retention tools on retraining and quantifying what automation does.

Conclusion

Automation in finance is invaluable in data-driven organisations that focus on “looking forward” rather than “backwards”; the amount of data that each business collects (structured or unstructured) daily is immense and needs to be nurtured, enhanced and used for critical decision making.

It will not eliminate the need for finance teams, but instead require a significantly different skillset than traditional transaction processing.

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