Giving your payroll the digital edge

Today we’re at the forefront of the next wave of technology change to impact payroll: the rise of the machines. Since the advent of the internet, there has been a myriad of technologies to digitise payroll effort and decentralise payroll data management to the wider workforce. The changes have kept coming at a rapid pace: from self-service portals to mobile apps and payroll robots for transactional processing and exception detection.

But beyond being the next ‘big thing’, technological advancements in machine learning (ML) and artificial intelligence (AI) offer more than just a changed way of working. ML and AI are a true opportunity to deliver the ultimate combination of compliance, risk reduction and efficiency while delivering increased service satisfaction and even competitive advantage.

With the feasible use cases for ML/AI increasing in parallel with ever-decreasing service costs from major infrastructure and cloud computing providers, we share potential opportunities for deploying ML and AI to achieve this combination in your business.

Tracking the trends

Emerging technologies like machine learning and artificial intelligence offer compelling opportunities to digitise everything in an environment with a ‘payroll expert over the shoulder of every service consumer, in real-time.’

CIOs and payroll managers alike need to build their understanding of payroll digitisation trends, the benefits that digitisation can deliver today, and how their peers may be planning to implement payroll digitisation to meet future service delivery challenges.

Where are you on the payroll digitisation journey?

• What payroll digitisation technologies and techniques have your implemented in your business to date?
• How have your payroll digitisation efforts delivered more value for your business?
• Which specific benefits of your digitisation program achieved the biggest gains in terms of actual and perceived value?
• What challenges are you looking to employ digitisation to address in the future?
• How are you hoping that digitisation will offer benefit in this space?

Making the case for Payroll Digitisation

Successfully implementing payroll digitisation is a business-wide effort. From seeking investment to effectively managing the shift from established traditional processes to more efficient payroll digitisation, challenges can emerge without a considered strategy that delivers whole of business value.

How will you engage business-wide stakeholders to secure buy-in and investment and successfully adopt payroll digitisation?

• Do you foresee challenges getting executive or organisation support for payroll digitisation efforts?
• Which risks/opportunities do executives perceive to be most compelling when making a case for investment in payroll digitisation?
• What strategies have you adopted before to make the case for digitisation and technology change to drive efficiency and improved results?
• Which strategies were successful (and which weren’t)?

Machine learning for payroll

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machines are typically cloud-based services that are trained to consume data and understand a number of processes by applying algorithms which are adapted automatically to operating environment changes.

Payroll processing relies on coordinating and managing massive internal and external data sets, all of which are subject to complicated and constantly shifting IR compliance rules. Unlike automation, which relies upon a cause-and-effect ‘trigger methodology, machine learning systems can interpret data and leverage it to make decisions, learn from previous mistakes, and adapt to strategically solve problems.

By managing data collection and automatically applying legislative rules, machine learning significantly reduces both the likelihood and severity of human errors while improving payroll efficiency.

Predictive analytics forecast what might happen in the future based on current and historical data from multiple sources, including sources outside your organisation. Predictive machines are often used to model budgets and forecast labour utilisation more accurately, enabling business leaders to pre-emptively find more efficient solutions.

Third-party data sharing can be accomplished using a combination of public APIs and data sources. Machines can collate data and automatically apply compliance rules much more quickly and accurately than human resources.

Machines are typically web-enabled and have access to data from sources outside your business systems landscape. Examples of data that can be used to drive efficiency include publicly available data from the ATO (STP data) or geographic and location data. As more data becomes available from organisations like Fair Work, machines will be able to automatically apply updated rules and build complex models for analysis.

Artificial Intelligence for smarter service delivery

Artificial intelligence (AI) takes the adaptive techniques implemented in a machine learning program and adds a layer of ‘smart’, human-centred capability. AI offers myriad opportunities for transforming frontline service delivery on top of back-office and administrative process improvements delivered by ML technology.

AI tools – from AI-driven service ‘staff’ to AI-powered integration services with external agencies – give you the ability to have a ‘payroll expert’ available for every service consumer, in real-time. These AI systems can substantially improve how data is collected and processed by supporting service consumers to understand which actions they should take and when to improve overall business outcomes and efficiency.

AI systems are able to collate and analyse significant volumes of your own business data, and unstructured data collected from external agencies and competitors in a way that allows you to make decisions informed by wider market trends and activity.

Get started on your journey

There’s never been a better time to continue your digital transformation journey with ML/AI. The first step to your journey is to consider each of these applications and how much efficiency they could deliver for your business so you can start building a compelling case.

Read on about the evolution of Aurion software: check out our releases for 2021 – Aurion Accelerates Employee Onboarding and  Aurion Prepares For STP 2.