AI in payroll - some practical use case examples

Introduction: A new year is underway, and we have invited members of the Payslip leadership team to give their views on some trending topics in their respective fields. This is one of a series of blogs from Payslip leaders.

Here, Gus Legge , Chief Technology Officer at Payslip shares some insight on AI and how it can be applied in the global payroll space.

AI dominated the tech landscape in 2023 and the noise around it shows no signs of slowing down in 2024. As with a lot of trending topics, opinions vary wildly as to what exactly it is and what the implications are. A case of what is real and what is not and sifting through the hype to find something tangible with real world applications. The search for clarity is not helped by sensationalized content, misinformed generalizations and flat-out disinformation circulating around the web!

So, what I would like to do in this blog is briefly touch upon some areas that artificial intelligence and data analytics can impact global payroll delivery and management and explain how they could provide practical value for global payroll professionals.

How Data Analytics and AI can help in Payroll Automation

I like to think of AI in terms of how it can provide value in the field of data analytics . And I always bring it back to the data because payroll is a function that both relies on data and produces large amounts of it at the same time. If you can access and extract intelligent data that is highly relevant to your business needs, an artificial intelligence technology can be leveraged to maximise the value of this data.

Data analytics is the science of drawing insights from raw data. In Payslip we use data analytics to help identify potential anomalies in a payroll cycle. These analytics help us to identify patterns and highlight deviations to those patterns. The deviation will often indicate a problem, which requires further checking and the value here is the potential to address possible problems and rectify them before they impact the payrun.

This could be an employee with a markedly different bonus to others with the same role or an employee with an exceptionally high tax to gross pay ratio. Payslip present graphical reports ?highlighting outliers which the payroll team can check and determine if there is an issue or not. This is visibility and control in action and represent a level of analysis that many global payroll professionals would like to be able to do but lack of technology and time prevents them.

Payslip is now developing capabilities based on machine learning to enhance this analytics capability. Our system learns what is normal and to be expected and what is not. This capability will move anomaly checking beyond pointing to anomalies to interpreting the cause and making suggestions to the payrun owner. E.g. “Should John, Employee ID 2035, receive a bonus as the data indicates that all other employees with his role have ”

The key to successful AI is having large amounts of data. And categorized or structured data is far better than uncategorized data. Payslip have large amounts of data and it is structured because we have mapped it to our global data model. This volume of structured data underpins our ability to provide real AI capabilities using machine learning in the platform .

Rapid Onboarding

Another area we are applying machine learning to is on rapid onboarding of local country payroll providers. Payslip is provider independent and is the only provider that allows you to choose any local country vendor ?or change at any time – this is really important to global organizations seeking to have full choice and control over who delivers their payroll in each of their countries.

We have developed capabilities to integrate with local payroll providers and with ERP systems, Benefits, and other applications within the technology ecosystems ?inside an organization. These capabilities are no-code and configuration based. The task of onboarding a customer is defining this configuration. As we repeat onboarding with different customers, we see re-occurring patterns. We will use machine learning to spot and learn from these patterns and to automate the configuration setup. This will enable us to set up new local payroll providers on our platform in a faster way so that our clients can get up and running with payroll in new countries in a quicker and more efficient way.

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This article originally appeared on payslip.com