TLDR: Dr. Ming Jin, International CTO of OneConnect Financial Technology, emphasized at the Fortune Brainstorm AI conference in Singapore that a hybrid AI approach, combining generative models with traditional AI, is essential for ensuring trust and control in the financial sector, particularly given its handling of sensitive data.
SINGAPORE – Dr. Ming Jin, International CTO of OneConnect Financial Technology (NYSE: OCFT; HKEX: 6638), addressed the Fortune Brainstorm AI conference in Singapore on July 28, 2025, outlining a critical vision for the future of artificial intelligence in finance. Dr. Jin asserted that a ‘hybrid AI approach’ is indispensable, integrating the expansive generative capabilities of large models with the precise, reliable nature of traditional AI. This strategy, he explained, is paramount for maintaining trust and control within the financial industry, a sector inherently dealing with highly sensitive data.
Dr. Jin highlighted the prevalent challenges faced by the banking industry, including complex legacy systems and outdated architectures. He presented AI, particularly large language models, as a transformative solution for modernizing these systems. By leveraging AI large models to analyze program code and extract functional specifications, financial institutions can rapidly comprehend and reconstruct their underlying architectures, effectively overcoming modernization hurdles.
As a testament to the practical application of this approach, Dr. Jin cited the success of OneConnect’s parent company, Ping An Group (HKEX: 2318; SSE: 601318). Ping An has extensively deployed AI across various core scenarios, including sales, customer service, operations, and management. In 2024, Ping An’s AI call center managed an impressive 1.84 billion customer interactions, accounting for 80% of all inquiries. Furthermore, in risk management, large model-powered anti-fraud technologies have yielded significant results in crucial areas such as credit and fraud prevention. These algorithms provide real-time analysis for identifying forged facial information and tampered identity data, thereby establishing robust security defenses and substantially reducing operational risks and costs.
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Discussing the global landscape of AI deployment, Dr. Jin observed distinct differences, noting that financial institutions in mainland China typically favor private deployments and concentrate on developing financial-specific AI applications.
