The Use Of Artificial Intelligence To Support Decision-Making In The Risk Areas

I began my career in the financial market working as a system developer and project manager. I worked in these roles for many years when I decided to start transitioning to roles in risk management. When making this career shift, I had in mind how the use of technology for mass data analysis, as well as the support of artificial intelligence, would be essential for delivering high-quality results, aiming at risk mitigation and prevention.

Recently, we pioneered the use of artificial intelligence (AI) in monitoring financial transactions and customer screening for anti-money laundering (AML) purposes in Brazil. I can affirm that this has resulted in a significant leap in terms of accuracy between alerts and communications. Currently, we have fewer cases to analyze and more cases to report due to the mapped and compared scenarios.

Of course, the use of these tools comes with both benefits and risks, as in any process. The challenge lies in weighing what brings the most added value.

Some immediate benefits include:

a) Efficiency Gain: By automating manual and repetitive tasks, we can significantly improve operational efficiency, saving time and costs and allowing us to dedicate more efforts to critical decision-making processes.

b) Real-time Monitoring: AI-based systems can monitor transactions and operations in real time, enabling quick detection and immediate responses to potential breaches.

c) Risk Assessment: AI can analyze large amounts of data from internal and external sources and historical behaviors to identify potential risks and predict future scenarios. This empowers professionals responsible for analysis and decision-making to take proactive measures to mitigate risks before they escalate or materialize.

Among several other benefits that could be mentioned.

 However, when we consider the risks that these automation present, we cannot ignore their proper mapping so that we can mitigate them with effective action plans, including:

a) Biased Decisions: AI algorithms can inadvertently perpetuate biases present in the data on which they are trained, potentially leading to discriminatory or inappropriate outcomes. In a compliance context, this can result in improper treatment or decisions. Ensuring the proper calibration of AI and reducing bias requires continuous monitoring and fine-tuning of algorithms, including comparative analysis with market data.

b) Privacy and Data Security: The increased use of AI requires access to large amounts of data, especially when confidential customer information is involved. Ensuring the privacy and security of this data through proper access management and data inventory is essential to prevent unauthorized access or breaches that could result in regulatory penalties and reputational damage.

c) Technical Complexity: Implementing AI systems requires resources with technical knowledge that may not be abundantly available in the job market, requiring many hours of investment in training. Maintenance and updates can pose challenges without adequate technical expertise.

That said, in my opinion, the use of AI in decision support for risk management areas offers notable benefits, including increased efficiency, more accurate real-time monitoring, and data-driven conclusions. However, these benefits come with inherent risks that must be managed and mitigated.

Thus, the significant challenge lies in finding a balance between automation and human action, adequate training for both the tool and decision-makers regarding impartiality and interpretation, and mainly keeping the tool updated with new scenarios and regulations requirements that come in order to ensure regulatory compliance. These are essential steps to harness the power of AI while protecting the organization against potential risks.

As the use of AI-based solutions continues to grow, it is indispensable for the areas utilizing them to approach AI integration thoughtfully and strategically. By doing so, we can observe and meet evolving regulatory requirements while leveraging AI resources to create a more efficient and effective compliance framework.

With the appropriate risk mitigations in place, it seems to me that the risk-return assessment in the use of AI has proven to be highly advantageous, bringing significant gains to organizations, especially in the financial market.

The beauty in all that lies in the enormous possibility that we have in front of us; inspite of the financial market or any other sector, we can contribute to designing and implementing an enormous range of conceptual scenarios, including those that even had been materialized. It is an infinite world of possibilities. Lastly, while AI has the potential to revolutionize many industries, it must be used in a responsible and ethical manner to ensure that the benefits are greater than the risks taken.