AI Interviewer Takes the Stage: Banks Diving Deep into "Human-Machine Collaboration" Digital Intelligence Transformation

Stone Shi Yu China Securities Journal

In 2026, the spring campus recruitment season for banks kicks off, with many banks offering positions in artificial intelligence, data mining, and other tech fields. The hiring standards are clearly shifting toward technical and versatile talents. Interestingly, the first hurdle for these future AI professionals is to undergo an “AI interviewer” assessment and screening.

Industry insiders believe that banks accelerating the recruitment of tech talents reflects the industry’s urgent need to promote technological finance and digital financial transformation. However, amidst the enthusiasm, challenges such as talent gaps and increasing segmentation still need to be addressed by banks. The industry is undergoing a deep dive into “human-machine collaboration” and intelligent digitalization.

AI Interviewers Go Live

A few days ago, Li Ming (pseudonym), a recent graduate from a Beijing university, participated in Ping An Bank’s AI interview. “After entering the system page and taking a photo, I could choose an AI interviewer to start the interview. The AI interviewer asked some basic questions, such as about self-introduction, career plans, and strengths. Each question had 15 seconds to think, and the entire interview lasted about 30 minutes. The AI recorded my answers, expressions, and movements throughout,” Li Ming told China Securities Journal.

“Overall, I found the bank’s AI interview not very difficult. I wasn’t asked technical questions; mostly questions related to personal experience, career outlook, and handling special situations,” said Zhang Na (pseudonym), who has participated in AI interviews at two banks. “Knowing I was facing an AI interviewer, I was more relaxed. When I finished answering a question, the AI didn’t follow up, and there was little interaction. But if I looked down, moved my body, or didn’t look directly at the screen, the AI would recognize and remind me.”

“AI interviews are mainly used for initial screening. By setting standardized criteria, they provide a preliminary assessment of candidates’ basic qualities. Currently, AI interviews won’t replace human interviews. Candidates who pass AI screening still need to go through multiple in-person interview rounds. We still rely on human evaluators to assess candidates’ innovation, stress tolerance, open-mindedness, and professional skills,” a senior HR official from a joint-stock bank told reporters.

Job Positions Tilt Toward AI and Tech Fields

If AI interviews represent the superficial change in bank talent screening methods, then the shift of recruitment positions toward AI and other tech fields reflects the deeper logic of this transformation.

Among state-owned banks, China Construction Bank recently announced its spring recruitment, with branches in Beijing, Inner Mongolia, Jilin, and other regions simultaneously hiring “specialized technology talents” mainly engaged in data mining, big data marketing, technical R&D, and system operation and maintenance. CCB’s subsidiary, Jianxin Financial Technology, also explicitly recruits talent in AI and cybersecurity. Industrial and Commercial Bank of China has dedicated “Technology Elite” positions for tech talents in system development, application R&D, information security, data mining, and product design.

In joint-stock banks, Shanghai Pudong Development Bank’s spring recruitment for generalist trainees aims to cultivate core technical talents in architecture, AI, and data operations, with a preference for students with backgrounds in AI, data science, software engineering, and fintech, especially those with multidisciplinary education.

Small and medium-sized banks are also making efforts. Guangzhou Bank’s head office fintech positions focus on AI and algorithm modeling, prioritizing applicants with backgrounds in fintech, computer science, and AI. Beijing Rural Commercial Bank has set up digital intelligence elite training and fintech training positions, both requiring candidates to have backgrounds in computer science and AI.

Many industry insiders believe that as banks vigorously promote digital finance, technologies like AI and cybersecurity have become key to optimizing risk control models and innovating financial products. The demand for fintech talent’s capabilities has also changed significantly. “The industry is experiencing a deep dive into ‘human-machine collaboration’ and digital intelligence. Previously, technical staff mainly focused on system development and operations support. Now, they need to apply cutting-edge technologies like AI algorithms and data mining to customer service, risk management, and product development,” said a fintech executive from a major state-owned bank.

Deepening Transformation

“Digital transformation ultimately depends on people. We need to accelerate cultivating digital talent teams, establish layered and categorized training systems, and improve digital literacy across all staff. We should optimize incentive mechanisms, encourage innovation, tolerate failure, and explore career development paths for digital talents to provide broad growth opportunities,” said Zeng Gang, deputy director of the National Institute of Financial Research.

Zeng Gang noted that over the past decade, China’s banking and insurance industries’ digital transformation has evolved from the initial “Internet+” to now “AI+” and “data elements×,” with significant advances in both depth and breadth.

While some banks are speeding up AI deployment and investing heavily in tech talent, the internal “technology gap” is widening. Many city and rural commercial banks face difficulties in digital transformation.

“We are somewhat powerless in promoting digital intelligence. The costs of transformation are high, and we face resource and technical capability shortages. Building fintech teams is difficult, and our salary competitiveness is limited, making it hard to attract top talent in this field,” said a rural bank official in western China.

Dong Ximiao, chief economist at LendingClub, analyzed that small and medium-sized banks generally face challenges such as limited funds, talent shortages, scarce data, and weak technical strength. For these banks, adopting a follow-the-leader strategy, carefully exploring AI application paths suited to their characteristics—such as focusing digital investments on key regions and core customer groups—can improve return on investment.