The implementation of Artificial Intelligence (AI) is similarly self-evident as its recognition. However, the latter part remains somewhat in question. The European Union passed the EU AI Act as the comprehensive law on the said technology. This means there are now guidelines for AI to follow to live effectively in the region.
What brings regulations on AI under the regulatory umbrella is the transparency around them. The financial sector, for one, has been looking to leverage the potential of AI for a long time. The objective is to bypass the complexities of several processes. As a result, customers benefit from saving time and increasing efficiency.
Regulations cover requirements for documentation, testing, reporting, and transparency. When AI enters sectors that have never implemented the technology, risks arise. The development and use of AI models necessitate constant monitoring due to the obvious ethical risks.
Another way to look at it is by knowing that the implementation of AI in a sector for the first time comes with the risk of multiple bad decisions and ethical breaches. The financial sector must remain cautious, as it directly deals with customers’ data and finances. A single mistake can have an impact on customers, resulting in their loss of trust in the sector’s technology and infrastructure.
A core focus is required on keeping the process transparent and taking proactive measures to avoid any negative impact that AI may otherwise have.
The financial sector is dedicated to having AI by its side, but not without a committee and ethical standards. nterpretability of AI models comes second, emphasizing sufficient knowledge about the decision-making process and parameters.
Users are more inclined to place their trust in a mechanism that exhibits qualities of transparency and trustworthiness.
At this time, it is advised that financial institutions replace their legacy systems in preparation for a likely upgrade. Legacy systems may contain insufficient techniques to demonstrate explainability. Upgrading to a technique that improves the interpretability of AI models makes it easier to grasp the relationship between decisions and the data used to provide those decisions.
There are many ways for the financial sector to reap benefits from AI. These include scalable standards, blockchain technology applications, and comprehensible machine learning architectures.
For starters, scalable standards have to be defined ahead of integration so that the model can consider accuracy, performance, robustness, transparency, and fairness. Furthermore, they should align with FI industry standards. Next, the use of blockchain technology can facilitate transparency and immutability in the decision-making process. Blockchain can further help to prevent any unauthorized changes to the model.
The ML architecture prioritizes explainability and transparency by making it easier to understand the decision-making process behind algorithms.
The financial sector continues to explore the implementation of AI or integrating a reliable model into its legacy system. The goal is to build trust as the industry advances in the digital age.
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