Artificial Intelligence in the Financial Sector: Applications, Challenges, and Future Trends – Interview with Li Jinlong

Introduction

Driven by rapid advances in data, models, and computing power, artificial intelligence (AI) has become a key engine of the technological and industrial revolutions, reshaping industries worldwide. In finance, AI is a pivotal technology as banks undergo digital and intelligent transformation.

Expert Introduction

Li Jinlong, Senior Manager of the AI Lab at China Merchants Bank, has led projects in capital agreements, data standards, big data, blockchain, and AI. He focuses on intelligent finance, leading large‑model research and applications, and has contributed to numerous academic papers and patents.

Background and Significance of AI in Finance

Banking is highly digital and complex, making it an ideal arena for AI empowerment. Digitalization creates data foundations for intelligence, enabling feedback loops where models continuously learn from business data, improving system efficiency and customer experience.

Financial institutions possess massive application data; AI unlocks its value, reduces information asymmetry, lowers service entry barriers, promotes inclusive finance, and drives innovation in management and strategy, ultimately enhancing the efficiency of technology‑driven banks.

Current Practice of AI in the Financial Sector

Intelligent finance leverages AI to replace or augment human capabilities, becoming a core competitive edge. Banks apply AI across marketing, risk control, operations, and customer service. The rise of large language models (LLMs) brings conversational, writing, reasoning, coding, and general intelligence capabilities, opening new competitive and industrial opportunities.

Potential LLM use cases include wealth‑management report generation and analysis, knowledge‑base enrichment for personalized service, sales script creation, intelligent audit and quality inspection, multimodal RPA, code generation and quality monitoring, as well as automated meeting minutes, reports, and presentations.

China Merchants Bank has deployed over a hundred AI‑driven scenarios since establishing its AI Lab in 2017, such as AI assistants, text‑based chatbots, intelligent outbound calls, and collaborative human‑machine services, improving service efficiency and reducing costs.

Challenges Facing AI Adoption in Finance

Key challenges include incomplete data ecosystems (single‑source data, heterogeneous unstructured data, and privacy concerns), technical and compliance risks of models (low error tolerance, lack of interpretability, regulatory oversight), and the massive compute resources required for large models, which strain banks’ budgets compared to tech firms.

Future Trends of AI in Finance

AI is expected to deepen its impact, delivering more personalized customer services, automating operational management, and enabling smarter risk control through knowledge graphs and big‑data analytics. While opportunities abound, attention to privacy, algorithmic bias, and sustainable governance remains essential.