5 Tips to Prevent Dreadful Situations in AI-Powered RegTech

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Category: RegTech
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Regulatory and compliance problems are some of the most important, complex, and resource-consuming issues faced by any business, especially for startups with limited resources. Over the decades of development, regulatory requirements and documentation have grown into a matter of special expertise and skills to work out.

The growing world of artificial intelligence is answering concerns to meet the requirements of regulatory compliance. Like any powerful tool, with no excuse, using powerful tools inappropriately can lead to a great dilemma.

Organizations are not always successful in solving particular challenges even they spend fortunes on a technology to solve. Often, issues that businesses are trying to address are related. They only intend to resolve a certain range using a sophisticated technology but failed to understand the technology first and its impact that may lead to more issues.

Introducing AI to organizations should be done methodically within the context of regulatory stipulations. Artificial intelligence is no magic bullet that will simply do its thing. As new technology like this, it is crucial to deploy it with precautionary measures to reduce further problems.

AI provides opportunities for efficiencies, automation, and big data analyses with forensic tools that bank are needed to review and produce due to the examination process. That is why AI is a focus of compliance departments.

Here are 5 tips to prevent dreadful situations when integrating AI-powered solutions in RegTech.

  1.    Analyze data for pre-existing biases

Artificial intelligence is based on statistical analysis generating outputs based on that data. If AI can recognize bias in the original data AI might emphasize it and will surely flaw disgorged it. It should be done with caution by also integrating human intelligence to analyze and pick out pre-existing errors and biases in the data sets. Biases may be such as the programmer’s own bias of race, gender, and economic groups. Errors may include may be incomplete or inaccurate data.

  1.    Guard against legal and compliance risk

The common cliché “faulty coding will create faulty algorithms, which will create faulty results” is still true in the context of AI as it is merely a computer program.  A way to prevent risks such as this includes installing quality checks and balances in the program to eradicate anomalies. It is also essential for the board of directors to understand, learn and be trained by the capability of artificial intelligence.

  1.    Know how your AI makes decisions

It is vital to know how your AI-powered system is making decisions. Deep learning algorithms involved in AI requires many mathematical calculations that customers cannot simply understand.

With that being said, glass box AI is generating a buzz. It originally rises from the United States Department of Defense that aimed to ensure that AI machines are making the right decisions.

Its opposite, Black box AI comprises of unsupervised machine learning capabilities based on statistical analysis. This type of AI develops on its own and generates decisions without an explanation.  This may be risky as it could lead to unpredictable situations like predicting wrong health conditions.

A circumstance like this can be prevented by explainable or glass box AI as it is programmed with an interface engine that explains how an AI makes a decision.

Algorithmic auditing is a good practice that will allow AI to be more transparent in its decision-making process.

  1.    Build controls to avoid infiltration of malicious code

Cybersecurity is a major concern in banking. More and more hackers are targeting AI systems. It is important to safeguard AI-powered systems by posing security measures to prevent these threats.

Recognizing probable misuse and vulnerabilities is crucial in risk management strategies. To ensure to address this, training AI developers to follow security protocols should be done.

  1.    Controlled experimentation approach

Data and code should be built gradually and methodically. Even though AI provides a significant step in the speed of analysis, it still needs a slow-paced approach.

Building AI should be done stage by stage and by doing the simple aspects first and proceed into more complex functions that have been reviewed and fine-tuned.

Developers need to continuously support the maintenance of the system as a change in regulations is constant. Your AI machine should be powered with updated data to fight hackers.