Hybrid LLM-Based Invoice Complaint Prediction and Analysis Engine

Abstract

This system, developed for businesses operating in the telecommunications sector, predicts billing complaints in advance and automatically generates explanations. The system is used to increase customer satisfaction, improve operational efficiency, and shorten customer service representative response times. By integrating different data sources, it offers behavioral, content-based, and pattern-focused prediction and explanation capabilities. It makes it possible to resolve complaints before they arise. It explains the reasons for predicted complaints in a clear and consistent manner for both the customer and the representative. For high-risk customers, it triggers automatic notifications, corrective actions, or proactive campaign / package suggestions.

Description

1 TARIFF Hybrid LLM-Based Invoice Complaint Prediction and Analysis Engine Technical Area 5 The invention is an invoicing system developed for businesses operating in the telecommunications sector. It involves a system that anticipates complaints and automatically generates explanations. State of the Art Today, telecommunications billing management systems typically process 10 bills submitted by customers. It works on complaints and has limited capabilities in proactive complaint detection. This situation leads to increased customer dissatisfaction and longer complaint resolution times. This leads to delays. Current practices do not automatically and accurately identify the causes of complaints. There is no mechanism to explain this in this way. This deficiency affects customer representatives. spending too much time resolving complaints and each representative giving different explanations 15 This leads to inconsistencies in customer experience. As a result, both customer experience becomes inconsistent and... Operational efficiency is decreasing. Moreover, customer behavior and past experiences are also affecting operational efficiency. The fact that their interactions were not included in the prediction models makes it difficult to accurately estimate the probability of complaints. This prevents prediction in this way. Integrating different data sources. In existing systems lacking capacity; billing data, customer segmentation information 20 And past complaint patterns are generally kept in separate systems and integrated analysis is performed. This is not possible. This deficiency leads to inaccurate predictions and inadequate customer information. This is due to the negative aspects described above and the current solutions. Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main purpose of the invention is to provide businesses operating in the telecommunications sector with 30 improved systems that predict billing complaints and automatically generate explanations. The goal is to provide a system. Another aim of the invention is to increase customer satisfaction and improve operational efficiency. and to shorten the response time of customer representatives. Another aim of the invention is to integrate different data sources to analyze behavioral, content-based, and The goal is to provide pattern-based prediction and explanation capabilities; to resolve problems before they arise. 35 The goal is to make it possible to understand the reasons for anticipated complaints from both the customer and the representative. 2 The goal is to explain things in a clear and consistent way; to automate high-risk customers. The goal is to inform, correctively address, or trigger proactive campaign / package proposals; complaints to prevent its occurrence, to increase customer satisfaction and to ensure the system is constantly learning to provide. The structural and characteristic features and all the advantages of the invention are given in the figures below and in these 5 Thanks to the detailed explanation written with references to the figures, it becomes clearer. It will be understood. Figures that will help understand the invention. Figure 1 shows a general representation of the system that is the subject of the invention. 10 Explanation of Part References 1. Data collection layer 2. User database 3. User database network server 4. RFM model and user behavior analysis interface 15 5. RFM model user behavior analysis interface network server 6. Association Rules Module 7. Association Rules Module Network Server 8. Complaint probability prediction module 9. Complaint probability prediction module network server 20 10. LLM-based description generation module 11. LLM-based description generation module network server 12. Proactive action and notification module 13. Proactive action and notification module network server Detailed Description of the Invention In this detailed description, the preferred configurations of the system that is the subject of the invention are listed only. This will contribute to a better understanding of the subject and will not have any limiting effects. The invention is an invoicing system developed for businesses operating in the telecommunications sector. It is a system that anticipates complaints and automatically generates explanations. 30 The operating principle of the system described in the invention is as follows: The basic data infrastructure of the system described in the invention. It creates the user database (2) data running on the user database network server (3). Users’ billing history, usage details, payment via collection layer (1) It stores information, tariffs, campaigns, and past complaint records. The RFM model allows the user to... User behavior with RFM model running on behavioral analysis interface network server (5) 35 The analysis interface (4) calculates score values ​​for each user and analyzes usage patterns. 3 It generates all the necessary features for the prediction and explanation modules by extracting them. Association rules module association rules module (6) user behaviors running on network server (7) Through analysis, it explores the relationships between types of complaints. Thus, it identifies the possible causes of complaints. It detects and predicts based on data and provides meaningful signals to the prediction module. Complaint probability Complaint probability prediction module (8) running on the network server (9) RFM 5 predicting potential complaints by using results, association rules, and other customer characteristics. It does. LLM-based description production module running on network server (11) LLM-based Explanation of production module (10) predicts complaints and risk factors, It converts into personalized natural language descriptions. Proactive action and notification module network. Proactive action and notification module (12) running on server (13) notifies users 10 or sends in-app alerts and makes suggestions (package, campaign). In this way It prevents complaints from arising, increases customer satisfaction, and ensures the system is constantly learning. provides.

Claims

4 REQUESTS 1. Invoice system developed for businesses operating in the telecommunications sector. a system that anticipates complaints and automatically generates explanations Its characteristic is; • Collects user billing information and complaints, and processes data at this layer. 5 data collection layer (1) which cleans and makes available for the model • Cleaning the data collected by the data collection layer (1) and relationally where recorded and indexed, users' billing history, usage details, payment a user database that stores information, tariffs, campaigns, and past complaint records (2), 10 • the server hardware on which the user database (2) application runs User database network server (3), • It calculates a score for each user and analyzes customer segments and usage patterns. parameter-based analysis of user behavior analysis account that detects User behavior analysis interface with RFM model (4), 15 • User behavior analysis interface (4) application with RFM model The RFM model, which runs on server hardware, and the user behavior analysis interface network server (5), • exploring the relationships between customer behavior and complaint types, processing Association rules that reveal relationships between frequently occurring themes in clusters 20 module (6), • association which is the server hardware that runs the association rules module (6) software rules module network server (7), • RFM results and association rule outputs for each customer using their features. Complaint probability estimation module (8), 25 • server hardware that runs the complaint probability prediction module (8) software Complaint probability prediction module network server (9), • Predicted complaint, including explanatory model values ​​of predicted complaints. and LLM-based explanation generation that translates risk factors into natural language explanations. module (10), 30 • Server hardware that runs the LLM-based description generation module (10) software LLM-based description generation module network server (11), • Automatically take corrective and informative actions for high-risk customers initiating, sending notifications or in-app alerts to users, and making suggestions proactive action and notification module (12), 35 • server hardware that runs the proactive action and notification module (12) software Proactive action and notification module network server (13) It includes.

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