Quantitative assessment and intervention system for psychological stress of collection personnel

Abstract

The invention discloses a quantitative assessment and intervention system for psychological stress of collection personnel, and belongs to the technical field of psychological assessment and financial collection crossing. The core innovation lies in collaborative design of a collection scene customized pressure index scale and a data-driven scoring algorithm framework: constructing a multi-dimensional measurement table (including quantifiable scoring items) covering a collection core pressure source, and obtaining call basic data through voice data conversion; the total pressure score is calculated by adopting a machine learning dynamic weight model and a quantile interval assignment algorithm, three-level early warning is divided, and differential intervention measures are matched. Test data show that the system can improve the pressure assessment accuracy to 85% or more, reduce the occupational tiredness rate of collection personnel by 20% or more, reduce the compliance violation rate by 30% or more, effectively solve the technical problems of poor suitability, rigid scoring algorithm and no intervention closed loop of a traditional assessment scale, and is suitable for various financial institution collection teams.

Description

Technical Field

[0001] This invention relates to the field of psychological assessment and financial debt collection, specifically to a quantitative assessment and intervention system for the psychological stress of debt collectors based on a customized scale for debt collection scenarios and a data-driven algorithm. This system solves the problems of poor adaptability and rigid algorithms in traditional assessments and is suitable for the management of debt collection teams in financial institutions. Background Technology

[0002] The debt collection industry is characterized by high levels of occupational stress due to challenging clients, stringent compliance requirements, and intense emotional and physical workload. Public industry data shows that debt collectors have high rates of burnout and annual turnover, and uncontrolled stress can easily lead to compliance violations and regulatory penalties. Existing stress assessment solutions have significant technical flaws: First, they use general scales that do not cover key stressors specific to debt collection, resulting in low relevance between assessment results and business operations; second, the scoring rules rely on fixed manual settings, making them unsuitable for different debt collection scenarios and resulting in low accuracy; third, the scale design does not take into account data availability, making some dimensions unquantifiable and hindering algorithm implementation; and fourth, the assessment and intervention are disconnected, failing to form a closed loop. Therefore, there is an urgent need for a stress assessment system that combines the characteristics of debt collection scenarios, is quantifiable, and allows for coordinated intervention, in order to address the aforementioned pain points. Summary of the Invention

[0003] I. Purpose of the Invention We provide a psychological stress assessment system for debt collectors that features quantifiable indicators, adaptable algorithms, and implementable interventions. By combining customized scales with dynamic algorithms, we improve the accuracy and business adaptability of the assessment and build a closed loop of assessment-intervention-feedback. II. Technical Solution

[0004] The core technical solution of this invention revolves around "customized scales for debt collection scenarios" and "data-driven scoring algorithms," as detailed below: (a) Customized scale design for debt collection scenarios Based on the analysis of stress sources in debt collection operations and data availability, a multi-core stress dimension and quantitative scoring items were designed: Stress source coverage logic: Covering five major categories of stress sources: interpersonal conflict (customer confrontation, power structure), cognitive load (multiple needs, complex logic), emotional labor (empathy, emotional expression), business risk (compliance, task objectives), and behavioral representation (speech speed, language coherence), ensuring that the dimensions are strongly correlated with the collection scenario; Quantitative adaptation logic: All scoring items are designed to be calculated using call data (text, speech rate, number of rounds), avoiding unquantifiable dimensions and ensuring the feasibility of algorithm implementation.

[0005] (ii) Data-driven scoring algorithm The "dynamic weighting model + interval assignment algorithm" is adopted to replace the traditional fixed rules, thereby improving the adaptability and objectivity of the scoring. Dynamic weight model: Core function: Based on machine learning, it dynamically adjusts the weight of scoring items according to different collection scenarios (such as different business types and customer types), solving the problem of poor adaptability of fixed rules; Technical logic: The model is trained using a large amount of historical call data to learn the correlation between rating items and stress levels, output dynamic impact weights, and iterate and optimize regularly to adapt to business changes. Range assignment algorithm: Core function: To solve the quantitative problems of scoring items such as proportion and standard deviation (e.g., the proportion of language fragmentation and speech rate stability); Technical logic: Divide the intervals based on the distribution characteristics of historical rating items (such as the quantiles of samples of different pressure levels), and assign values ​​according to the intervals to avoid subjective manual setting.

[0006] (iii) Matching of intervention measures Based on the correlation data of "low-score dimension - historical effect", we construct a mapping rule for intervention measures to ensure the targeted nature of the intervention: For low-scoring dimensions such as customer confrontation and emotional regulation, optimize communication techniques and allocate low-confrontation business tasks accordingly. For low-scoring dimensions such as compliance risks and task objectives, provide matching compliance training and process verification tools; For low-scoring dimensions such as fluctuating speech rate and fragmented language, we match expression training and visual cue tools. III. Test Sample Data

[0007] A comparative test was conducted using collection teams from multiple financial institutions. The experimental group used this system, while the control group used a traditional method. The test results verified the technical advantages of this system. Test results show that the technical effect of this system is significantly better than that of traditional solutions, and its core advantage stems from the synergistic effect of customized scales and dynamic algorithms. Detailed Implementation

[0008] The following case study, using a debt collection team from a financial institution, illustrates the application process of this system: Step 1: System Deployment

[0009] Scale and algorithm configuration: Enter customized scales and scoring algorithm parameters into the collection system, with the base score for core risk dimensions being higher than that for non-core dimensions; Data integration: Integrate the data acquisition module with existing speech conversion tools to obtain call text, speech rate, and turn data; Intervention rule configuration: Enter the mapping rule of "low score dimension - intervention measure" and associate it with business systems (training, scheduling, script library). Step 2: Daily Assessment

[0010] Single-call assessment: After the call ends, the system automatically extracts data (such as the number of negative customer interactions and speech rate fluctuations), calculates frequency-based scores using a dynamic weighting model, calculates proportion-based scores using an interval assignment algorithm, and sums them up to obtain a total score and grade (such as medium pressure). Daily summary: Generate individual stress reports (marking low-scoring dimensions) and team stress statistics, and push notifications to supervisors when warning thresholds are triggered. Step 3: Intervention Implementation and Effect Tracking

[0011] Intervention matching: For employees with moderate stress and low customer resistance scores, push communication script templates and assign low-resistance customers; Effectiveness verification: One week later, the employee's customer resistance score improved, and one month later, the proportion of stress in the team decreased, verifying the effectiveness of the intervention.

[0012] Beneficial effects of technology

[0013] Scenario-based scales: Customized scales cover key stressors unique to debt collection, significantly improving the correlation between dimensions and business risks, and solving the problem of poor adaptability of general scales; Dynamic algorithm: The dynamic weight model adapts to different scenarios, improves scoring accuracy, and solves the problem of rigid fixed rules; Closed-loop implementation: linking assessment and intervention to solve the problem of disconnect between traditional solutions; High compatibility: Adapts to existing speech conversion tools and business systems, with low deployment costs.

[0014] Business benefits Reduce personnel costs: Decrease burnout and turnover rates, reducing recruitment and training costs; Controlling compliance risks: reducing compliance violation rates and complaint rates, and avoiding regulatory penalties; Improve business efficiency: Increase information acquisition rate and collection success rate, and optimize business output; Industry demonstration value: Promote the transformation of the debt collection industry from experience-based management to technology-based management. Attached Figure Description 1. Figure 1: Architecture diagram of the quantitative assessment and intervention system for the psychological stress of debt collectors. The system uses a modular flowchart to illustrate the five core modules and data flow relationships: ◦ The data source (collection call) transmits voice data to the "data acquisition and processing module"; The “Data Acquisition and Processing Module” outputs text, speech rate, and dialogue structure data to the “Scoring Calculation Module”. ◦ The “Assessment Dimension Module” provides customized scale parameters to the “Score Calculation Module”; The “Scoring Calculation Module” outputs the total stress score to the “Stress Level Classification Module”; The "Stress Level Classification Module" pushes the graded early warning results and low-scoring dimensions to the "Intervention Implementation Module"; ◦ The “Intervention Execution Module” outputs differentiated intervention measures to the business system (training, scheduling, script library), clearly demonstrating the overall logical chain of the technical solution. 2. Figure 2: Schematic diagram of dynamic weight model process includes two parts: model training (data collection → annotation → training → iteration) and real-time application (scenario input → weight call → score calculation), and annotation of model type and accuracy standard. 3. Figure 3: Scoring-Grade Relationship Logic Diagram shows the "Data Input → Sub-item Scoring → Total Score → Grade Output" chain, including the core risk veto logic, and labels the risk levels. 4. Figure 4: Low-Score Dimension-Intervention Measure Mapping Table. This table displays the low-score dimensions, corresponding intervention measures, effect targets, and implementation cycles, with the mapping rules clearly indicated. The above figures were created using standardized tools to ensure the reproducibility of the technical solutions, and the figure labeling is consistent with the terminology in the instruction manual.

Claims

1. A quantitative assessment and intervention system for the psychological stress of debt collectors, characterized in that, include: ◦ Assessment Dimension Module: Used to store customized stress indicator scales for collection scenarios. The scale contains multiple core stress dimensions associated with the core stress sources of collection. Each core stress dimension corresponds to at least one scoring item that can be quantified through call data. ◦ Data acquisition and processing module: used to acquire voice data from collection calls and convert it into text data, speech rate data, and dialogue structure data that can be analyzed; ◦ Scoring Calculation Module: Used to quantify the scoring items of the indicator scale based on a preset scoring algorithm, which includes dynamic association rules and interval assignment algorithms, and outputs the score of a single scoring item and the total stress score; ◦ Pressure Level Classification Module: Used to map the total pressure score to a preset pressure level range and output a graded early warning result with business risk labels; ◦ Intervention Execution Module: Based on the graded early warning results and the low-score core pressure dimension, this module matches and outputs differentiated intervention measures using preset mapping rules.

2. The system according to claim 1, characterized in that, The core stress dimensions include speech rate fluctuation stress, customer resistance stress, cognitive load stress, language fragmentation stress, emotion regulation stress, dialogue power structure stress, compliance risk stress, task goal stress, logical coherence stress, and implicit stress cues.

3. The system according to claim 2, characterized in that: The scoring items for speech rate fluctuation pressure include the frequency of abnormal speech rate exceeding a preset threshold and speech rate stability index. The customer stress assessment criteria include the frequency of negative customer interactions, the frequency of customer refusal to cooperate, and the frequency of customer identity questioning. The scoring items for cognitive load stress include the frequency of responding to multiple customer demands, the frequency of handling complex logic, and the frequency of information response delays. The scoring criteria for language fragmentation stress include the percentage of incoherent language expression; The scoring items for emotion regulation stress include the percentage of empathic interaction, the frequency of negative expressions, and the balance index of emotional vocabulary; The scoring items for the dialogue power structure include the frequency of customer interruptions and the percentage of passive responses. The scoring items for compliance risk pressure include the frequency of missing compliance information and the frequency of non-compliant statements; The scoring items for the task objective pressure include the number of missing key information items and the frequency of process deviations; The scoring items for logical coherence pressure include the frequency of irrelevant answers and the frequency of information errors; The scoring criteria for the implicit stress cues include the first-person usage percentage deviation and the usage percentage of strong imperative words.

4. The system according to claim 1, characterized in that, The dynamic association rules are constructed based on a machine learning model, which is one of gradient boosting tree, lightweight gradient boosting, or ensemble learning models, and satisfies the following: Training was conducted based on a large amount of historical collection call data with manually labeled pressure levels; Using the quantitative characteristics of each rating item as input, the output is the dynamic influence weight of each rating item on the stress level; Regularly iterate and optimize by adding new call data to ensure that the weights adapt to changes in business scenarios, and the prediction accuracy of the model is ≥85% after iteration.

5. The system according to claim 1, characterized in that, The interval assignment algorithm divides the scoring intervals based on the distribution characteristics of historical scoring items and outputs the corresponding scores according to the intervals. The distribution characteristics include quantile thresholds for samples of different pressure levels.

6. The system according to claim 1, characterized in that, The logic for calculating the total score is as follows: Frequency-based scoring item score = base score of corresponding dimension - (quantitative value of target behavior × dynamic influence weight), with a minimum score of 0. The score for the proportional / standard deviation category is equal to the score output by the interval assignment algorithm. The total stress score is the sum of scores from all rating items, with a maximum score of 100.

7. The system according to claim 1, characterized in that, The pressure level range includes: The first zone (high pressure) corresponds to the "communication out of control" risk label, and the criteria for judgment include high complaint rate, high risk of violation, and low customer cooperation. The second zone (medium pressure) corresponds to the "efficiency decline" risk label, and the judgment criteria include low information acquisition rate, high proportion of passive response, and high time consumption. The third zone (low pressure) corresponds to the "high efficiency and stability" risk label, and the judgment criteria include zero compliance errors, high empathy ratio, and high information acquisition rate. A core risk veto mechanism has been added: when the proportion of deductions for compliance risk pressure exceeds a preset threshold, it will be forcibly identified as a high-pressure zone.

8. The system according to claim 1, characterized in that, The intervention measures include: High-stress period: business operations suspended, special intervention for compliance and emotional management, one-on-one business debriefing; Medium-stress range: scenario simulation training, low-difficulty business allocation, and real-time business support; Low-pressure zone: high-difficulty task assignment, benchmark case output, performance incentives; The intervention measures were constructed based on low-scoring dimensions and correlation data with historical intervention effects.

9. The system according to claim 2, characterized in that, The base scores of the indicator scale are determined through expert weight assessment and verification of historical business data. The base scores of core risk dimensions are higher than those of non-core dimensions, and the consistency of weight assessment meets the preset standards.

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