Understanding compliance

0:28 Hello learners, welcome back to the course on AI in human resource management. Today, we move to the second lecture of Module 10. We'll be looking into AI in compliance. Now, this is one field which AI has—if I can use the word—gone into so much that we see a visible presence of AI in compliance. You look into any other domain specifically, be it recruitment, be it training, development, learning, or even what we have discussed—something like employee benefits, compensation, or maybe aspects of employee development over time.

0:55 Whatever it is, the involvement of AI is a bit latent. But in compliance, AI is visible; AI is present in a very holistic manner. And this is what we are going to discuss. We'll try to look into all possible dimensions of AI in compliance. We'll look into robotic process automation. We'll look into blockchain. We'll look into all possible technologies where AI is present in terms of compliance. I'm Dr. Abraham Cyril Issac. I'm an assistant professor at the School of Business, Indian Institute of Technology, Guwahati.

1:26 Now, when you look into the emerging trends of AI in HRM, this particular lecture will have its own importance because of the evolving nature that has been evident for some time now. Now, let's look into AI-driven compliance in greater detail. When you look into AI-driven compliance, we first have to understand what exactly we mean by compliance. Compliance here refers to an organization's adherence to laws, regulations, standards, and even ethical practices relevant to its operations. So, many times, most organizations tend to forget this ethical practices domain.

2:28 They'll go for adherence to laws, regulations, policies, etc. They will not give due importance to ethical practices. So, when you look into compliance from this holistic angle, it encompasses a wide range of requirements, including data protection laws, financial regulations, health and safety standards, and even industry-specific guidelines. So please note, organizations must implement compliance, robust compliance frameworks to mitigate risks associated with non-compliance, which can lead to legal penalties, reputational damage, and financial losses, as we have seen in the previous lecture too.

3:03 So, in highly regulated industries, let's take some examples, such as finance, healthcare, or telecommunications, for that matter. Compliance requirements are stringent and multifaceted. There is no doubt about it. Now, financial institutions, for example, must comply with anti-money laundering (AML) regulations, KYC (know your customer) laws, and various data protection standards. given the sensitivity of handling vast amounts of client data. So many a time, most of the spammers and most of the scams do happen around the aspects that we have just discussed now.

3:57 So it is prudent. It is just pragmatic from the organization's point of view that they should be actually looking into all these matters with greater concern. Now, when you look into the compliance, we have to especially AI driven compliance will start from the scratch. We look into what are the limitations of the traditional compliance methods. There has to be some limitation. That's why we are concluding that AI. AI