As Artificial intelligence (AI) develops we asked Ferhat Sarikaya a few questions on how it can be used in finance and banking sector and the future.
Q: First, could you introduce yourself to us? What’s your education and career path?
A: I am Ferhat Sarikaya. With over 20 years in technology and the last decade focused on artificial intelligence and big data, I hold a Master’s degree in Artificial Intelligence and Adaptive Systems from the University of Sussex. Currently, as a Master AI Researcher and Principal Big Data Architect, I develop sophisticated AI solutions in areas including fraud detection, anti-money laundering systems, and advanced computer vision applications. Throughout my career, I’ve maintained a balance between technical expertise and leadership, building MLOps architectures and optimizing big data infrastructures across major cloud platforms. My experience spans from theoretical research to practical implementation, delivering scalable AI initiatives across various industries.
Q: What significant changes have you noticed in the industry after ChatGPT, and what key issues are experts discussing?
A: ChatGPT has revolutionized AI, making it accessible through natural language interaction rather than complex coding. Key discussions among experts center on ethics, particularly in areas like hiring where AI systems can inherit biases from training data. The debate also extends to whether AI truly “understands” or merely excels at pattern matching. While AI is transforming various industries, it’s becoming clear that it’s not about replacing jobs but augmenting human capabilities. Technical challenges remain significant, particularly regarding AI “hallucinations” – where systems confidently provide incorrect information. This becomes especially concerning in critical areas like medical diagnosis or legal research. The balance between innovation and responsible AI development remains a crucial consideration for the industry’s future.
Q: Having worked extensively in banking and finance, how is AI being used in these sectors?
A: At Denizbank, we implemented AI in several crucial areas. Our anti-money laundering systems analyze transaction patterns in real-time, identifying suspicious activities that might take days to detect manually. Our fraud detection systems adapt to individual customer behavior patterns, reducing false positives while catching genuine fraud attempts more effectively. We developed deepfake detection systems for secure remote banking, especially valuable during the pandemic, ensuring authentic video interactions during digital onboarding.
AI has also streamlined document processing, reducing processing times from hours to minutes for loan applications and KYC documents. In risk assessment, we’ve developed models that enable more inclusive lending practices by considering alternative data sources. Our AI systems analyze not just traditional financial data but also other relevant indicators to create a more comprehensive risk assessment profile. While AI chatbots handle routine queries, we maintain human oversight for complex decisions, demonstrating how AI augments rather than replaces human judgment. The integration of these systems has significantly improved operational efficiency while maintaining high security standards.
Q: What practical measures would you recommend for protecting against AI-related threats like deepfakes and sophisticated scams?
A: Key protection measures include always verifying through official channels when receiving unexpected requests, especially financial ones. For digital safety, implement multi-factor authentication, be wary of unusual social media friend requests, and maintain good “digital hygiene” with regular password updates. The “pause and verify” rule is crucial, particularly for financial requests. With AI-generated content becoming more sophisticated, it’s essential to verify unexpected communications, especially those creating urgency or requesting sensitive information.
Be particularly cautious of personalized scams that utilize social media information to create convincing messages. Regular security audits and updates to protection measures are essential as AI-driven threats evolve. Education and awareness about current scam techniques are crucial for both individuals and organizations. Implementing strong verification protocols and maintaining updated security systems can significantly reduce vulnerability to AI-related threats.
Q: Looking ahead, how do you think AI will transform banking and financial services?
A: We’re moving toward “invisible banking” where financial services seamlessly integrate into daily life. Real-time risk assessment will become more sophisticated, using alternative data sources to continuously evaluate creditworthiness. Fraud prevention will become more proactive, predicting and preventing incidents before they occur. Customer service will transform with AI assistants capable of complex financial advisory roles, while maintaining human advisors for critical decisions.
Investment management will see major changes, with AI creating hyper-personalized strategies that adapt to market conditions and personal circumstances in real-time. Financial inclusion will improve through alternative data evaluation methods, helping serve previously underbanked populations. The integration of AI will enable more sophisticated product recommendations and personalized financial planning services. However, challenges remain in balancing automation with personal touch, and addressing privacy and data security concerns. The future of banking will likely see a hybrid model where AI enhances human expertise rather than replacing it entirely.
Q: How does the UK’s approach to AI governance differ from Turkey’s?
A: The UK takes a more structured, policy-driven approach with established research ecosystems like the Alan Turing Institute and proactive regulatory development. Turkey adopts a more business-driven, entrepreneurial approach, particularly in banking and fintech. While the UK has a mature ecosystem for AI startups with strong government backing and academic partnerships, Turkey focuses more on practical applications in manufacturing, retail, and customer service.
The UK’s emphasis lies in healthcare, financial services, and public sector efficiency, while Turkey excels in practical implementations, particularly in banking innovations. Both countries face the challenge of bridging the AI skills gap, though the UK has a more developed talent pipeline. The regulatory environment differs significantly, with the UK maintaining comprehensive governance frameworks while Turkey’s more liberal approach allows faster implementation but less standardization. This has created distinct but complementary environments for AI development and innovation, each with its own strengths and opportunities for growth.
