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Regulatory compliance

Introduction

The insurance industry which has always been considered as one of the industries that are least likely to adapt to changes is undergoing a change process currently. This has mainly been attributed to the need to meet compliance requirements, manage risks and improve on the efficiency of the organization. Regulatory Technology (RegTech) especially Regulatory Algorithms (RAG) with the use of Artificial Intelligence (AI) is at the epicenter of this transformation. RAG AI is a growing branch that involves the use of AI methods including machine learning, natural language processing and predictive analytics for regulatory compliance. This technology has the ability to change the face of how regulatory compliance is handled in the insurance industry, by increasing efficiency, reducing the costs and improving on the accuracy.

Challenges

There are several challenges that the insurance industry faces in regulatory compliance. Due to the fact that the regulations are constantly changing and are quite vast, it is hard for insurers to be aware of all the modifications and meet all the requirements. The current methods of compliance are mainly manual and therefore slow, prone to mistakes and expensive. There is also the issue of variation in the regulations and the regulations of different jurisdictions which makes compliance even more difficult. Also, the penalties for non-compliance are very severe such as fines, bad reputation, and loss of customers’ trust. Last but not the least, the COVID-19 has also created another level of challenge in the regulatory compliance where the regulators have been enforcing new set of rules and regulations to protect the policy holders.

AI Solutions

This is where RAG AI comes into the picture as the solution to these challenges. It is an innovative way of managing compliance that makes the process easier, more efficient, and less expensive. RAG AI uses machine learning to read and analyse regulatory texts, learn about the regulations that apply, and dynamically maintain compliance policies. It also has the capability to understand and analyse complex regulatory language through the use of natural language processing, thus minimizing the need for manual analysis. Predictive analytics assist firms to forecast the changes in the regulations and be in a position to address them before they take effect. Advanced AI solutions for regulatory compliance are being provided by companies such as IBM and Ayasdi. For example, IBM’s Watson Financial Services provides regulatory compliance solutions through the use of artificial intelligence while Ayasdi an AI platform which enables insurance companies to identify and avoid regulatory issues.

Benefits

There are numerous advantages of applying RAG AI in the area of regulatory compliance. It helps to reduce the time and efforts required for meeting the compliance standards thereby reducing the costs. It also improves the precision and reduces the chances of non-compliance and the fines that come with it. It also offers an overall perspective of the compliance within the organization and hence improves on decision making. It also free the compliance officers to engage in other important activities since it automates routine tasks. Besides, it promotes a strong compliance culture since instead of waiting for changes to be imposed, they are addressed before they become a problem.

Return on Investment

The ROI from RAG AI, however, will depend on several factors such as the size of the insurer, the complexity of the regulations to be followed, and the current compliance structure of the company. There have been many studies that demonstrate that it can be very high. An article by Accenture states that AI has the potential of cutting down compliance expenses by fifty percent. A research by McKinsey also revealed that RAG AI can minimize the effort made on compliance by 10-20%. When added to the avoidance of the costs of non-compliance and the potential loss in reputation, this is a strong argument to implement RAG AI.

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