How PSBC Cut Branch Wait Times 30% with a Four-Dimensional AI Diagnosis System

China Post Savings Bank (PSBC) has developed a comprehensive intelligent diagnosis system for its physical branch network, moving beyond hardware automation into data-driven, large-model-augmented operational management. The system addresses four long-standing pain points: fragmented staff-efficiency evaluation, siloed self-service equipment data, lack of quantitative attribution for hall waiting times, and incomplete marketing-conversion tracking.

Industry Context: Two-Phase Branch Transformation

The article outlines two industry phases. Phase one focused on hardware deployment — intelligent teller machines (ITMs), self-service terminals (STMs), and mobile pads — to divert basic transactions from counters. Phase two, now underway, centers on mining the operational data these devices generate to build quantitative assessment models for scientific allocation of staff, devices, and space.

Four Pain Points Identified

Staff efficiency evaluation: Business systems operate in silos; no unified dashboard shows teller transaction volume, handling time, or marketing frequency.

Self-service equipment operations: ITM, STM, and pad transaction volumes, maintenance logs, and diversion data are scattered, making manual analysis costly.

Hall experience attribution: Customer wait times are influenced by staffing, device availability, pre-shift preparation, and teller proficiency; traditional management cannot weight each factor.

Marketing traceability: Customer potential tags, teller referral actions, and conversion outcomes are not centrally collected, breaking the closed loop.

Data Foundation: Full-Domain Fusion

The platform integrates four core data sources — unified counter, self-service banking, intelligent devices, and cash logistics — aggregating 30+ key indicators across five categories: teller transactions, device logs, customer flow, cash replenishment/clearing, and customer marketing tags. This eliminates manual cross-system ledger consolidation and provides real-time, auto-updated data pools.

Four-Dimensional Standardized Diagnosis Modules

1. Teller Efficiency Diagnosis

Uses a national-average benchmark to place each branch in a four-quadrant matrix (productivity vs. efficiency): low productivity/high efficiency → expand hall business; high/high → optimize staffing; low/low → targeted skills training; high productivity/low efficiency → adjust shifts and add service staff. The system drills into high-frequency, time-consuming transactions to pinpoint operational bottlenecks and recommends counter adjustments based on real-time customer flow.

2. Self-Service Equipment Diagnosis

Evaluates devices on three layers: service age, operational stability (maintenance frequency), and business structure. Aging, high-maintenance devices trigger replacement alerts. Defined reasonable ranges for device-to-customer-flow match and daily average transactions per terminal classify devices as idle or overloaded. Transaction-mix breakdowns guide physical layout and customer self-service guidance to maximize diversion.

3. Hall Experience Composite Diagnosis (Figure 1)

Takes customer wait time as the core metric and decomposes it into four factor layers:

Traffic-counter match: Calculates service capacity thresholds; enables flexible counter scheduling and weekend opening plans.

Pre-shift preparation: Identifies delays from cash delivery and centralized clearing; recommends staggered clearing operations.

Teller handling efficiency: Benchmarks against national averages to surface optimization room.

Equipment maintenance interference: Tracks maintenance frequency to reduce device-failure impact on counter diversion.

4. Hall Marketing Tracking

Leverages customer marketing tags to distinguish potential and converted customers. Core metrics — potential count, conversions, and hall new-account rate — visualize branch customer-management effectiveness. Unconverted prospects are retained for follow-up, closing the loop: potential identification → on-site referral → statistical tracking → continuous nurturing.

Core Innovations

Lightweight frontline tool: All indicators, thresholds, and conclusions align with daily branch management; outputs are immediately actionable without specialized data interpretation.

Full-domain data fusion: Breaks silos across personnel, equipment, traffic, and marketing, solving cross-system extraction latency and delivering a holistic operational view.

Internal large-model integration: Pilot-standardized cases train the bank's internal LLM platform. The model auto-fetches full data sets, generates real-time benchmarking reports, supports longitudinal trend and peer comparison, and drastically cuts manual analysis cost and remediation cycle time.

Full-chain quantitative attribution: Quantifies each link's impact on service efficiency, customer experience, and marketing conversion, turning hidden problems into visible, data-backed decisions.

Application Value

Internal Operational Gains

Nationwide rollout targets: converge pre-shift preparation to the 11-minute national benchmark; reduce single-transaction handling time by 10% via targeted training; raise self-service diversion ratio through continuous legacy-device monitoring; cut average wait time at high-wait branches by 30% via dynamic scheduling and layout optimization; boost offline product conversion and intermediate-income via end-to-end marketing tracking. Overall, achieves intensive allocation of labor and equipment, sustainably lowering comprehensive branch operating cost.

Social & Cross-Industry Value

Inclusive finance upgrade: Efficiency gains free teller capacity for dedicated services to elderly and disabled customers, fostering an inclusive financial ecosystem.

Cross-sector replicability: The four-dimensional quantitative framework requires only indicator tweaks to serve medical outlets, logistics sorting centers, and other offline scenarios, providing a standardized digital-transformation blueprint for physical industries.

Future Roadmap

Large-model iteration: Leverage internal intelligent-agent and big-data training resources to refine the LLM with pilot cases; incorporate frontline feedback to add granular metrics such as marketing tracking and segmented customer-service efficiency, continuously improving diagnostic precision.

Standardized product launch & nationwide rollout: Package the four-dimensional analytics as a standard module embedded in the bank-wide branch operations management system, progressively covering all self-operated branches. Establish continuous feedback loops from regional branches and outlets to iteratively enhance the indicator system and LLM analytics, fully realizing PSBC's digital, fine-grained branch management framework.