Ping An Insurance (China) Files Patent for AI-Powered Drunk Driving Fraud Detection

Ping An Insurance (China) Files Patent for AI-Powered Drunk Driving Fraud DetectionOn December 5, 2024, the State Intellectual Property Office of China revealed that Ping An Property Insurance Company of China Ltd. filed a patent application for a "Method, Apparatus, and Device for Identifying Drunk Driving Fraud Based on Artificial Intelligence," with publication number CN119067684A and an application date of August 2024

Ping An Insurance (China) Files Patent for AI-Powered Drunk Driving Fraud Detection

On December 5, 2024, the State Intellectual Property Office of China revealed that Ping An Property Insurance Company of China Ltd. filed a patent application for a "Method, Apparatus, and Device for Identifying Drunk Driving Fraud Based on Artificial Intelligence," with publication number CN119067684A and an application date of August 2024. This patent application focuses on leveraging AI technology to effectively identify and combat drunk driving insurance fraud, thereby improving the efficiency and accuracy of insurance claims processing, reducing financial losses for insurance companies, and maintaining a fair and just insurance market.

The patent abstract details the core methods and processes of this technology. This method represents a cross-disciplinary fusion of artificial intelligence and fintech, aiming to accurately determine the presence of drunk driving fraud through comprehensive analysis of user data. The identification process can be broken down into several key steps:

First, the system acquires relevant user data, including user label data and user behavior data. This data may encompass personal information, driving records, insurance information, and other drunk driving-related behavioral data, such as social media activity and travel routes.

Second, the system preprocesses the collected raw data. This crucial step involves cleaning, denoising, and normalizing the raw data to ensure data quality and reliability. The preprocessed data is then transformed into label data and behavioral data suitable for subsequent analysis, preparing for feature extraction.

Next, the system extracts features from the preprocessed label and behavioral data. This step aims to extract key features related to drunk driving fraud from massive datasets. These features include driving time, routes, abnormal driving behavior, vehicle speed, and collision details. These features are converted into numerical forms understandable by machine learning models. The extracted features are categorized into first-order and second-order features. First-order features might include relatively straightforward features, while second-order features may involve more complex, higher-level features derived through data transformation or combination.

Then, the system integrates the extracted first-order features to obtain second-order features. This step aims to combine multiple first-order features into more representative and discriminative features to improve model accuracy. The integrated second-order features serve as input for the drunk driving fraud identification model.

Following this, the system employs two different drunk driving fraud identification models to independently analyze the second-order features, providing separate prediction results. The use of two models aims to enhance the reliability and accuracy of prediction results, mitigating the limitations of a single model through model diversity.

Finally, the system fuses the prediction results from the two models to generate the final user drunk driving fraud identification result. This fusion strategy effectively integrates the advantages of both models, improving overall identification accuracy and reducing the risk of prediction bias from a single model. Fusion strategies may include simple weighted averaging, voting mechanisms, or more complex ensemble learning methods.

Beyond the identification method, the patent also covers an AI-based drunk driving fraud identification apparatus, computer equipment, and storage media. This indicates that the technology encompasses not only algorithms but also supporting hardware and software systems. These components collectively form a complete drunk driving fraud identification system, ensuring efficient and stable operation.

Furthermore, the patent mentions storing drunk driving fraud identification results on a blockchain. The immutability and transparency of blockchain technology ensure the authenticity and reliability of the results, preventing malicious tampering or forgery, thereby further enhancing system security and trustworthiness.

In conclusion, Ping An's patent application utilizes AI technology to identify drunk driving fraud, significantly improving efficiency and accuracy. Through innovative technologies such as multi-model fusion and blockchain application, this patent provides powerful technological support for insurance companies in combating drunk driving fraud, contributing to a fairer and more just insurance market environment. The application of this technology not only reduces risk and losses for insurance companies but also maintains a healthy insurance industry order, ultimately benefiting society as a whole. The successful application of this patented technology heralds the increasingly widespread and in-depth application of AI in insurance fraud prevention, offering new directions and possibilities for future Insurtech development.