By: Dim Watson

What are the common challenges in payroll?

Suresh Dodda: The common challenges in automating the payroll process are compliance and regulation changes, which are frequent changes in tax laws, labor regulations, and compliance requirements that pose a challenge for payroll professionals. Staying updated and ensuring compliance with these changes can be time-consuming and complex.  

How did you solve this complex payroll issue?

Suresh Dodda: Many payroll systems need to integrate with other HR and financial systems. Compatibility issues and the need for seamless integration can be a challenge, especially when organizations are using legacy systems. Data Security and Privacy: With the increasing amount of sensitive employee data handled by payroll systems, maintaining robust data security measures is crucial. Protecting against data breaches and ensuring compliance with privacy regulations is an ongoing concern.  

Impressive. How did you achieve one solution for world payroll?

Suresh Dodda: For multinational companies, managing payroll across different countries with diverse tax laws, currencies, and employment regulations can be highly complex. Achieving standardization and efficiency in a global payroll system is a significant challenge. Manual Processes and Human Error: Reliance on manual processes can lead to errors in payroll calculations, which can result in compliance issues and employee dissatisfaction. Automating processes where possible can help mitigate this risk.

That’s great and your hard work is paid off. Can you talk bit about AI role in your project?

Suresh Dodda: Artificial Intelligence (AI) played a significant role in enhancing the integration of payroll systems with other HR and financial systems. Here are several ways AI can be leveraged for improved integration. 

Data Mapping and Transformation: Data mapping and transformation play a crucial role in integrating payroll systems with other HR and financial systems. 

Here’s a breakdown of these concepts: 

• Definition: Data mapping is the process of establishing a relationship between the data elements in one system and the corresponding elements in another system. It defines how data is transferred and transformed from source to target. 
• Purpose: The primary goal of data mapping is to ensure that information is accurately and appropriately shared between different systems. It involves identifying the source and target data fields, understanding their formats, and creating a mapping schema.

Also, you mentioned Natural Language Processing. Can you go over how you use that?

Suresh Dodda: Natural Language Processing: NLP can be applied to understand and interpret unstructured data, such as employee documents and contracts. This can help automate the extraction of relevant payroll information from various sources. Machine Learning for Compliance Monitoring: Machine learning algorithms can continuously monitor changes in regulations and compliance requirements. This enables payroll systems to adapt and ensure that all processes remain compliant with the latest laws. 

Last, what are the benefits of your product?

Suresh Dodda: The benefit of using the above techniques is that we can shorten the Payroll Processing Window. Enabling the payroll administrators to validate the payroll faster means employees can receive their paychecks ahead of time, and payroll teams can meet deadlines more efficiently. 

We can perform more accurate payment calculations. Eliminating errors in payroll calculations, such as incorrect deductions, human data-entry errors, or missed payments, ensures that employees are paid accurately, improving employee satisfaction and retention. 

We can remove legal issues. It is daunting to be updated with the labor laws of one country; imagine the plight when the business is spread over many! AI gives you all the necessary updates to ensure your payroll processing is aligned with the respective laws and saves you from any legal troubles. 

Provides more transparency. Employees have the right to know how their pay is being calculated. AI not only does the calculations but also offers step-by-step details to the employees as to how their pay rates are determined.

Provides more employee empowerment. Irrespective of the employee’s position in the hierarchy, AI treats him/her equally and offers answers even in the middle of the night! No longer waiting for HR professionals to respond to tickets/queries. 

Suresh Dodda has expertise in Java, AWS, Microservices, statistical learning, and data mining, leading to innovative work in telecom billing, real-time credit scoring, and payment system integrations. Suresh was instrumental in creating critical products like risk scoring products in Mastercard, Lifion for World HR, and Payroll solutions.

Published By: Aize Perez