Woofun AI reports that the banking sector is rapidly integrating AI digital humans capable of working 24 hours a day for a monthly cost of 8,000 yuan, with underperforming units instantly terminated without severance. These digital entities require only two to four weeks of training to assume full job responsibilities in post-loan collection, customer service, and document verification, offering a productivity equivalent to four traditional human employees. Unlike standard chatbots, these systems are deeply embedded in business workflows, yet their deployment faces significant hurdles regarding professional qualifications, data security, and the allocation of legal responsibility for high-risk financial decisions.

The operational model represents a fundamental shift from traditional software procurement to a productivity-based billing structure. Zhao Ming, an industry practitioner using a pseudonym, explained that the service fee for one AI digital employee position is approximately 8,000 yuan per month, supplemented by an annual technical support fee of around 20,000 yuan. This support fee is charged based on the "business entity" rather than the number of units deployed; whether a bank deploys one or 100 digital employees for a specific scenario like overdue reminders, the technical cost remains fixed. Consequently, the larger the scale of deployment, the lower the technical cost per unit, driving a higher overall return on investment. In high-frequency scenarios such as overdue reminders, comprehensive costs can be reduced by more than 50%, with customers reporting that collection costs have been cut in half.

The deployment cycle for these digital workers is remarkably short, typically completed within 2 to 4 weeks. The process begins with testing in a small scenario, followed by the establishment of standard operating procedures and scripts, multiple rounds of security and compliance checks, and finally large-scale deployment. During the testing phase, real business data including response rates, answer accuracy, and coverage rates are rigorously analyzed. Zhao Ming noted that while there is no fixed "universal standard" for all banks due to varying business scenarios, digital employees must meet three critical criteria before official deployment: compliance, business effectiveness, and stability. Compliance is paramount; any violation of scripts or excessive promises is strictly prohibited, and the system is designed to respond immediately to sensitive scenarios without room for negotiation. In actual bank projects, the probability of having to restart due to non-compliance is very low, as multiple rounds of SOP reviews and Grayscale testing ensure performance meets expected standards prior to launch.

Evaluation mechanisms for digital employees are closely tied to their specific tasks, mirroring the assessment of human staff but with automated tracking. For instance, digital employees in risk control are evaluated on the accuracy of overdue reminder services, error rates, and customer satisfaction. Key metrics include whether node timing for reminders is accurate and whether scripts are strictly followed. Systems utilize lists of sensitive words and strategic guidelines to ensure AI does not make promises beyond its authorized scope.

Furthermore, the ability of digital employees to seek help from real employees when facing complex issues is a critical evaluation point. Back-end systems maintain complete records of conversations and quality inspection reports, enabling random manual inspections. A bank employee specializing in digital employees stated that work and responsibilities are clearly tracked in information systems, focusing on the number of service interactions, clients served, and service quality. Digital employees who perform poorly are taken offline, which is equivalent to being "terminated." Unlike real employees, the decision to take a digital employee offline lies entirely with the user. Since billing is monthly, units failing to meet performance or compliance expectations can be disabled with one click, eliminating costs associated with resignation handovers or compensation.

The concept of "offline-retraining" allows for the optimization of digital employees without replacing the underlying model. Zhao Ming explained that re-training focuses on optimizing the business knowledge base and SOP processes, akin to providing a more professional set of guidelines and strategies. When re-deployed, these units exhibit significantly improved business handling capabilities. Although the underlying models remain the same, training and fine-tuning equip digital employees with more accurate business knowledge and scripts. This active business iteration occurs when banks change policies or launch new products, requiring AI to quickly learn new information, which is distinct from termination due to errors. Despite these mechanisms, the industry acknowledges that evaluation frameworks still require refinement in practice. A senior researcher at a city commercial bank noted that while regulations mention evaluation, no specific implementation plan exists yet. Most systems are purchased from software companies, with each costing millions of yuan and incurring substantial maintenance costs.

The adoption of digital employees has accelerated across the banking sector, with incomplete statistics showing deployment in more than 20 banks. In April 2019, SPDB's digital employee, Xiao Pu, made its official debut as an 'AI-driven 3D financial digital human' rotating between various branches. In December 2020, China Everbright Bank launched Digital Employee No. 001, integrating artificial intelligence, facial recognition, and voice recognition. In January 2021, the AI digital employee created jointly by the Agricultural Bank of China and SenseTime officially started working at the Hangzhou Zhongshan Branch as an offline lobby manager. At the end of 2021, Baixin Bank introduced its first "digital employee" with a "2D" character design named AIYA, interacting with customers through short videos, virtual live broadcasts, and apps. In August 2024, Zheshang Bank announced the launch of the digital human "Zhi Ying", planned for gradual application in AI customer service, investment advisory, product management, and remote banking. Performance data indicates significant impact, ranging from the annual equivalent productivity of 55,000 people at ICBC to the replacement of tens of millions of man-hours at China Merchants Bank.

Despite rapid technological progress, industry experts maintain a cautious stance regarding widespread adoption. Dong Yaohui, deputy dean of the Shenzhen Institute of Financial Stability and Development, argued that the time is not yet ripe for large-scale deployment in the financial industry. He emphasized that the financial sector is inherently highly regulated, with many businesses requiring licensed operations and clear regulations regarding professional capabilities, qualifications, compliance requirements, and behavioral norms. Dong Yaohui analogized the situation to hospitals hiring "digital doctors"; while AI can organize medical records and assist in diagnosis, society does not accept unqualified digital doctors treating patients independently. Similarly, in investment advisory, wealth management, insurance sales, and credit approval, practitioners must hold corresponding qualifications and assume responsibilities. Currently, digital employees do not meet these regulatory requirements, and premature replacement of humans could lead to misleading customers, insufficient risk warnings, and unclear responsibility boundaries.

Dong Yaohui further highlighted that financial services involve risk disclosure, suitability assessment, customer rights protection, and professional judgment in complex situations, areas where digital employees have significant limitations. They struggle with understanding real customer needs, handling special cases, responding to emergencies, and assuming responsibility. At the data level, digital employees require access to vast amounts of customer information, transaction records, and internal knowledge bases. If permission boundaries are not clear, this could lead to personal information leakage and data abuse. Responsibility boundaries must be clearly defined in advance to prevent the use of system malfunctions or misinformed customers as excuses for unclear liabilities. Banks should not view digital employees as substitutes for real humans but rather as tools for standardized, low-risk, and verifiable scenarios such as customer service inquiries, document organization, internal knowledge retrieval, process reminders, and preliminary information screening. For businesses directly affecting customer rights and risk judgment, licensed personnel must continue to play a role in review, manual verification, and providing backup support.

Institutions must clearly define job responsibilities, data access rights, script usage limits, operation logs, and responsibility assignments to prevent digital employees from handling business beyond their authority or making inappropriate promises. Overall, digital employees are currently more suitable as tools to improve efficiency and assist in management rather than as substitutes for professionals capable of independent judgment. Only as regulatory rules, technical capabilities, and responsibility mechanisms are further improved will the application scope of digital employees gradually expand. This marks a critical juncture where the drive for low-cost digital transformation must be balanced against the rigid constraints of financial regulation and the necessity of human accountability in high-stakes economic activities.