As part of Global Fintech celebrations, I interviewed Cedrick Agorbia-Atta, a finance and technology expert with close to a decade of intersectional experience between leading industry fore-front financial transactions as an Investment Banker and currently working across AI, Security and Cloud at Microsoft, one of the world’s biggest technology companies. We discussed how AI and cloud computing is shaping the future of global financial risk management, cybersecurity, and decentralized finance, and the key challenges in building secure and accurate global financial monitoring systems.
1. Can you elaborate on how your experience has shaped your understanding of traditional and emerging finance?
My experience has provided a comprehensive understanding of traditional and emerging finance.
Leading high-profile mergers, capital raising, and investment strategies deepened my expertise in navigating complex regulatory landscapes and executing significant financial transactions.
Simultaneously, I gained insight into innovative finance through projects like raising capital for multiple intelligence technology companies pioneering Africa’s Fintech and digital currency ecosystem. This blend of traditional finance and forward-thinking innovation shaped my approach to creating financial solutions that address both current and future challenges.
2. What do you think are the key considerations when developing financial systems that are global, innovative and secure against financial fraud and digital asset loss?
When developing global, innovative, and secure financial systems, several key considerations must be prioritized to combat financial fraud and safeguard digital assets. These include ensuring regulatory compliance with international standards, implementing advanced security measures such as encryption and multi-factor authentication, and leveraging blockchain technology for transparency and resilience. Additionally, user education on security practices, system scalability, and real-time monitoring through AI and machine learning are essential for minimizing risks. Finally, robust data privacy protocols are crucial to protect sensitive financial information from breaches. By focusing on these elements, financial systems can foster a secure environment for users worldwide.
3. In your opinion, how can financial institutions better leverage AI and ML to enhance their anti-money laundering (AML) capabilities?
Financial institutions can enhance their anti-money laundering (AML) capabilities by leveraging AI and machine learning (ML) to detect and prevent illicit activities more effectively. Drawing from my experience from managing high-stakes financial transactions, and contributing to AI-driven innovations, AI can automate data analysis, identify suspicious patterns in real-time, and improve compliance monitoring. For instance, in the digital finance and currency space, AI could be used to track user or investor behavior and flag potential risks, ensuring regulatory adherence. By continuously learning from data, AI systems can adapt to evolving threats, making them essential for mitigating sophisticated money laundering schemes.
4. What role do you see AI playing in the future of financial risk management, particularly in detecting and preventing fraudulent activities, adapting to rapidly evolving cyber threats and improve cybersecurity outcomes particularly in risk-based access management?
AI will play a transformative role in financial risk management by detecting and preventing fraud, adapting to evolving cyber threats, and improving cybersecurity, particularly in risk-based access management. Drawing from my experience in finance and AI, I’ve seen how AI can analyze vast data in real-time, identifying suspicious activities and reducing false positives. As cyber threats grow more sophisticated, AI systems will dynamically learn from each threat, enhancing their predictive capabilities and enabling financial institutions to proactively manage risks and secure digital assets in an increasingly complex financial landscape.
5. What are the biggest challenges in building AI systems for cybersecurity, and how do you overcome issues like false positives and threat detection accuracy and how do you foresee the ethical implications of AI in this domain?
The biggest challenges in building AI systems for cybersecurity include ensuring high threat detection accuracy and addressing ethical concerns like bias and privacy. From my experience driving AI-powered security solutions, improving accuracy involves using advanced machine learning algorithms that adapt to new and evolving threats, combined with continuous real-time data analysis.
To address ethical implications, it’s crucial to ensure AI systems are transparent, unbiased, and respect user privacy, especially when handling sensitive financial data. Properly balancing innovation with ethical considerations will be key to maintaining trust and effectiveness in AI-driven cybersecurity.
6. How do you envision the role of cloud computing in financial products, especially as digital and decentralized finance grows and balancing such innovation with the need for robust security in cloud environments?
Cloud computing will play a pivotal role in the future of financial products, particularly as digital and decentralized finance (DeFi) grows. I see cloud platforms enabling faster, scalable, and more efficient financial services, facilitating seamless transactions and smart contract execution in DeFi.
However, as these innovations evolve, maintaining robust security is paramount. By leveraging AI-powered cloud security solutions and risk-based access management, financial institutions can safeguard against cyber threats while supporting innovation. Balancing scalability with strong security measures will be crucial for trust and adoption in decentralized financial systems.
7. Throughout your career, you’ve demonstrated strong leadership in both finance and technology. How do you measure the long-term societal impact of financial products or technologies, particularly in terms of protecting citizens from financial fraud?
I measure the long-term societal impact of financial products and technologies by evaluating how effectively they promote financial inclusion and protect citizens from financial fraud and enhance security. Drawing from my experience in both finance and technology, I believe solutions should not only meet immediate market needs but also contribute to long-term financial stability. Success is measured by reduced fraud incidents, improved trust in financial systems, and accessibility of secure financial services to underserved populations, ensuring sustainable, positive societal outcomes.
8. Looking ahead, what do you believe will be the most transformative trends in AI, cybersecurity, and finance over the next five years, and how are you preparing to lead in these areas? And How do you plan to continue using your expertise in this area to contribute to safeguarding digital financial infrastructure?
Over the next five years, I believe the most transformative trends in AI, cybersecurity, and finance will be the integration of AI in real-time fraud detection, advancements in decentralized finance (DeFi), and the rise of AI-powered cybersecurity systems that adapt autonomously to new threats. Drawing from my experience in AI, cloud innovation and finance, I am confident in continuous efforts to refine AI models for better accuracy, promote secure blockchain adoption in DeFi, and collaborate on advanced cybersecurity measures. My focus will remain on leveraging AI and cloud technologies to safeguard digital financial infrastructure, ensuring resilient and secure financial ecosystems.