Machine learning and artificial intelligence, more commonly called AI, have been carving a niche regarding regulatory compliance because their applications address common systematic issues and challenges that many need to deal with daily. But, more importantly, it does so as effectively and efficiently as possible.
Enterprise solution applications integrating AI, like SaaS insurance software, can effectively enhance efficacy and efficiency. Some are even able to do so with compliance across various industries. While the sky’s the limit with the technological breakthroughs artificial intelligence and machine learning offer, many of their current applications regarding compliance systems demonstrated three advantages: the reduction in false positives, addressing errors, and minimizing costs.
1. Lowering false positives.
Many financial service providers have been experiencing false positives with their systems of compliance at a concerningly high rate. Unfortunately, the alert systems of compliance that are based on current, standard technologies have been triggering thousands of these false positives daily. Since every alarm needs to be carefully reviewed, it invites more human error and inefficiency opportunities. Fortunately, with ML and AI, this doesn’t have to be the case.
Machine learning and artificial intelligence can capture, analyze, and filter massive amounts of data, and address the false positives that waste providers’ money and time. One good example is that they can significantly improve the workflow by categorizing any compliance-related activity autonomously and ensuring that the right people are notified with any essential updates, activities, and events. In other words, they can streamline the alert systems of compliance.
2. Reducing costs.
Many modern financial service providers are forced to make changes and adjustments so they can adhere to regulatory compliance requirements revolving around the analysis and management of data. As a result, RegTech developers have now begun to use artificial intelligence to improve efficiency while lowering compliance costs by automating processes that would have otherwise needed tedious manual work.
When partnered with machine learning, artificial intelligence can automate the workflow. This means less human capital and time are needed to support your compliance operations. And when used in tandem with accuracy gains that are possible because of the integration of both technologies, you can save a considerable amounts of financial resources in compliance costs annually.
3. Addresses human errors.
Whether it’s due to a lack of due diligence, ineffective processes, or outdated technology, the expenses associated with human error can cost regulated industries a lot every year. For example, the financial regulations that passed following the global recession require the tracking, management, and analysis of detailed data regarding transactions, operational activities, and customers in financial institutions. The massive volume of information can raise many opportunities that could potentially lead to expensive human error.
Much like using calculators over manual computation, ML and AI can potentially shed some light on certain areas that people would have missed otherwise. Furthermore, they can spot patterns and trends to reach out to their customer base easier.
Conclusion.
No one can deny the challenges that come from regulatory compliance. While it’s certainly true that there’s no such thing as a solution that can meet every need, artificial intelligence and machine learning can go a long way in improving compliance systems.
