Adherence to regulation is the bedrock of progressive growth and advancement of the complex sectors of artificial intelligence (AI) and machine learning (ML). Among the leaders of these initiatives is Rajiv Avacharmal, who has more than 12 years of experience in managing risks related to AI/ML quantitative models. Rajiv is a Corporate Vice-President at one of the largest financial services companies in the world and has consistently demonstrated a passion for making sure that the implementations of AI systems within this company are both moral and feasible.
Rajiv’s specialization is in the validation, application and governance of AI/ML/Gen AI models. He has been instrumental in helping these top firms address the technical and business problems related to AI, including issues related to bias and compliance with core laws like the BSA/AML, fair lending, sanctions, and fraud. The same skills make it possible to turn data analysis into business solutions, which in turn promotes the integration of innovative solutions with compliance requirements.
Staying Ahead in a Dynamic Regulatory Environment
Rajiv encourages the key approach to keep up to date and agile in the face of changes to the regulations. He engages in associations in the industrial field and meets with professionals in forums, conferences and workshops to learn on emerging challenges and developments.
Rajiv emphasizes, “I regularly review scholarly publications, industry reports, and regulatory guidance documents from key institutions such as the NAIC, Federal Reserve, and other regulators to gain a comprehensive understanding of the evolving landscape.” This equal level of interaction with formal research and industry discussion helps him to keep abreast of the challenges within the regulatory environment.
To Rajiv’s mind, the further, the closer integration of AI/ML technologies will be with the regulation process and the integration process will be cyclic. He notes, “As AI/ML models become more sophisticated and pervasive, regulators will need to adapt their approaches to keep pace with the changing landscape.” His strategy includes investing more in research and development to align with enhancing risk management measures so as to maintain organizational adaptability in the face of new regulation.
Additionally, Rajiv underscores the importance of “deepening our engagement with regulators and industry stakeholders” to shape effective governance standards for the future of AI.
Navigating Complex Regulations with Strategic Foresight
Rajiv’s commitment to compliance is evident in how he has addressed some of the most difficult regulatory issues within the financial services industry and these include SR 11-7 and FIL-22-2017. He reflects, “The challenges posed by SR 11-7 and FIL-22-2017 required a comprehensive understanding of both the spirit and letter of these regulations.”
Supervisory guidelines have been maintained by Rajiv through leading extensive assessments of AI/ML models across his organizations. The model risk management strategies in various departments have been improved through the cooperation with model developers, validation teams and business units.
During his tenure the application of stringent test procedures, constant monitoring and documentation compliance with regulation not only have been achieved, but enhanced new standards in model compliance have been set.
Educating Teams for Compliance and Innovation
Rajiv also points out the need to educate the employees so that they can be on the lookout for compliance while the organization encourages creativity. He explains, “We need to take a multi-faceted approach, combining regular training sessions, interactive workshops, and hands-on case studies.” Using this method, team members gain a broad understanding of requirements and regulations and how they are put to practice.
Further, Rajiv promotes a learning culture where employees are encouraged to “stay abreast of regulatory updates through self-study and participation in industry events,” ensuring ongoing professional development. His approach guarantees that his teams are informed and ready to provide the best regulatory compliance across operations.
Addressing the Challenges of Tomorrow’s AI Regulations
This is particularly a challenge when it comes to the ever-changing technology since this is what makes it very difficult for AI systems to meet current and future regulations. Rajiv explains, “The most challenging aspect of ensuring AI systems meet current legal standards and are prepared for future regulatory developments is the pace of technological change.”
To this end, Rajiv underlines the concept of anticipatory and adaptive governance. He states, “To address this challenge, we need to implement a forward-looking approach to AI governance.” Rajiv makes it possible to achieve long-term compliance since he factors in the likelihood of future changes and integrates flexibility into the governance structures. They also have the element of strategic vision that allows them to keep all teams responsive to new regulatory horizons.
Striking a Balance
Overcoming the conflict between innovation and compliance is not an easy thing to achieve. Rajiv elaborates, “Balancing the drive for innovation with the need for stringent compliance is indeed a challenging task, but one that is essential for the responsible development and deployment of AI/ML technologies.”
He gives an example of creating a new AI-based AML system that provided improved detection while reducing the number of false positives. However, the problem was to make the output of the system comprehensible and understandable for the end-user.
Rajiv engaged data scientists, legal advisors, and compliance teams to design a good governance model around the system. He explains, “To address these concerns while still pursuing innovation, I collaborated closely with our data science, legal, and compliance teams to implement a robust governance framework for the AI-powered AML system.
The outcome was the successful launch that retained full compliance with regulations while providing the maximum level of transparency and interpretability. This example shows how Rajiv leads the organization to achieve the right mix of technology and compliance.
Crafting Effective AI Governance Strategies
According to Rajiv, governance is central to the development of innovation and compliance to the AI/ML frameworks. According to him, the essential elements of a robust governance strategy include:
- An effective governance structure, which can outline specific contours of risk associated with AI/ML models, and address them.
- Geometric and algorithmic ranging validation to guarantee that models work without prejudice to given directions and prohibiting unfair outcomes.
- The latter long-term systems monitor the situation continuously and offer real-time data resulting in the immediate elimination of problems.
- Semi-autonomous documentation that helps stakeholders to comprehend how models work and make choices.
- Strengthening of cooperation between data science and legal and compliance departments and business to achieve regulatory objectives in the framework of innovation.
- The fees provided should include the opportunity to maintain frequent meetings with regulators to be aware of the new prerequisites for changes in standards and to participate in the formation of the new legislation.
Through these elements, Rajiv is of the opinion that organizations can strike a perfect balance between innovation and compliance in their AI/ML projects.
More than anything else, Rajiv’s expertise in the legal frameworks governing the use of AI/ML technologies in business is unprecedented. The actions have increased the bar for compliance, as well as promoted the growth of innovation accountability. With such a vision in mind, Rajiv is still working to ensure that innovative initiatives do not overshadow proper regulation of the technological processes. His commitment is enlightening to the successor, most particularly the new entrant in the world of AI/ML, with the realization that the future is made not by anticipating, sharing and being truthful.
