Wage underpayment continues to be a pervasive issue in Australia that disproportionately affects vulnerable workers, including casual and migrant employees. In the 2021-22 fiscal year alone, the Fair Work Ombudsman recovered a record $532 million in unpaid wages and entitlements for more than 384,000 workers. This was a fivefold increase in the number of workers from the previous year. This issue has implications beyond financial hardship, including decreased consumer spending, unfair advantages for businesses, reduced quality of goods and services, increased reliance on government assistance, and potential conflict between workers and employers. Prioritising wage regulations not only ensures legal compliance but also helps in preventing and remediating these issues. While the root causes of wage underpayment vary and can be complex, common factors like inadequate understanding of legal requirements, lack of transparency in pay systems, poor record-keeping, and intentional exploitation by employers, contribute to the issue. This is where artificial intelligence can help.
While compliance with wage regulations is essential for Australian businesses, it can be challenging and time-consuming, especially for those with complex payroll systems and large numbers of employees. AI technologies, such as form recognition, can help businesses meet their obligations more efficiently and avoid non-compliance risks. While there are potential limitations and considerations to keep in mind, the benefits of using these technologies to ensure regulatory compliance are clear and significant.
Form recognition technologies use machine learning algorithms to understand content in forms such as scanned or handwritten timesheets. These technologies can detect patterns, data, and text within unstructured documents, which can then be extracted and analysed. The application of these technologies can assist in identifying wage underpayment by identifying anomalies in payment records, flagging areas of concern, and facilitating further investigation.
Implementing form recognition technologies in wage remediation programs can offer significant benefits. For example, Deloitte helped an Australian university address wage underpayments by analysing more than 3.2 million payslip records. This work was supported by Deloitte’s Discovery and Data Management practice, who applied form recognition to wrangle data from handwritten timesheets for over 15,000 current and former casual workers.
By leveraging DDM’s form recognition capabilities, the university could identify wage underpayment issues more accurately and efficiently, reducing the time and resources required to rectify the issue and resulting in cost savings for the university. The use of this technology demonstrated the university's commitment to fair and ethical employment practice, robust internal governance structures, and ensured compliance with legal obligations. As a result, the university can maintain its reputation as a socially responsible institution and set an example for other organisations, exemplifying how innovative technologies can be used to address significant social issues such as wage underpayment.
While form recognition technologies can be a valuable tool in identifying wage underpayment, they are not infallible. Human oversight is still necessary to ensure data extracted from documents is accurate and anomalies missed by algorithms are identified appropriately. Human involvement can also address legal or ethical considerations, such as sensitive or confidential information in documents that should not be extracted or analysed by algorithms.
By ensuring compliance with wage regulations, businesses can create a culture of fairness and trust that boosts their reputation as an employer and attracts and retains top talent. One way to improve compliance and reduce risk is to move away from paper-based payroll information. By digitising payroll records, businesses can eliminate the need for form recognition technology, progress Environmental, Social & Governance (ESG) goals, and reduce the likelihood of underpayments occurring in the first place. However, if underpayments do occur, AI technologies, particularly form recognition, can play a significant role in both proactively detecting issues and supporting wage remediation activities retrospectively. This allows businesses to address potential underpayment problems before they escalate, while also providing a means to correct past mistakes efficiently. By automating the identification and remediation of issues, form recognition can reduce costs, and enhance accuracy and consistency in the remediation of wages. Although there are potential limitations to consider, the benefits of using these technologies make them a promising solution to the ongoing issue of wage underpayment in Australia.