Customer insight platform in financial insurance elimination scene

Abstract

The invention relates to a customer insight platform in a financial deprotection scene, and belongs to the field of financial science and technology. The data processing and analyzing module is used for identifying customer behavior modes, preference changes and potential risk points; the customer insight module is used for updating customer labels and realizing personalized classification and prediction; and the application service module is used for designing a customized financial service scheme according to the insight result. The method has the advantages that the capacity of the financial institution in the aspect of customer right protection is greatly improved, consumption disputes are reduced through accurate insight of customer requirements, the service process is optimized, customer trust and satisfaction are enhanced, and meanwhile sustainable development of the financial institution is promoted.

Description

Technical Field

[0001] The present invention relates to a customer insight platform in a financial consumer protection scenario, and belongs to the field of financial technology. Background Art

[0002] The rapid development of the financial industry has indeed brought consumers unprecedented diversified products and services, but it has also brought a series of challenges to consumer rights protection. In the existing financial service model, the disadvantages of technology are mainly reflected in the following aspects:

[0003] First, traditional financial institutions usually rely on static and limited data sets to make decisions, which limits their ability to understand and predict customer needs. Due to the lack of real-time and dynamic data collection and analysis mechanisms, financial institutions are often unable to capture changes in customer behavior and new market trends in a timely manner.

[0004] Secondly, existing technologies may be inefficient when processing big data. Financial institutions may not have sufficient technical capabilities to process and analyze massive and diverse customer data, resulting in slow decision-making and an inability to quickly respond to market changes and customer needs.

[0005] Furthermore, many financial institutions have limited data analysis capabilities and find it difficult to extract valuable information from complex data, which not only affects the identification of personalized customer needs, but also affects the prevention and control of potential consumer risks.

[0006] In addition, the application of existing technologies in customer service also has shortcomings. For example, the customer interaction interface may not be friendly enough, and the intelligent customer service system may not accurately understand the customer's questions, resulting in a poor customer experience.

[0007] Finally, data security and privacy protection are also important issues facing existing technologies. With the frequent occurrence of data leaks and privacy violations, consumers' trust in financial institutions has declined, which poses a serious threat to consumer rights protection.

[0008] In summary, these drawbacks of existing technologies not only affect the quality and efficiency of financial institutions' services, but also bring challenges to the protection of consumer rights. Therefore, financial institutions need to continuously update and upgrade technologies to better meet customer needs, improve service efficiency, and ensure that consumer rights are effectively protected. Summary of the invention

[0009] In order to overcome the defects of the prior art, the present invention provides a customer insight platform in the financial consumer protection scenario. The technical solution of the present invention is:

[0010] A customer insight platform for financial consumer protection scenarios includes a data acquisition module for all-round data collection; a data processing and analysis module for identifying customer behavior patterns, preference changes and potential risk points; a customer insight module for updating customer tags to achieve personalized classification and prediction; and an application service module for designing customized financial service solutions based on insight results.

[0011] The specific collection steps of the data collection module include determining the source of data, collecting data, data cleaning and preprocessing, data storage and management, and security and compliance checks, specifically:

[0012] 1.1 Determine the source of the data:

[0013] Internal data: Capture transaction records, account information, and service record data from the internal systems of banks, insurance companies, and securities financial institutions through API interfaces;

[0014] External data: Use data crawler technology to obtain customer behavior data, public opinion information, and credit scores from public websites, social media platforms, and external channels of credit rating agencies;

[0015] Customer interaction data: collect customer consultation, complaints, and feedback information through customer service hotlines, online customer service, and mobile application channels;

[0016] IoT and sensor data: Collecting customer behavior data through sensors and logging technologies at physical locations or online platforms;

[0017] 1.2 Collect data:

[0018] API Integration: Use RESTful API or SOAP protocol to exchange data with internal systems to achieve real-time or batch data capture.

[0019] Data crawler: automatically access external websites and crawl required data according to preset rules; Log analysis: extract user behavior data by parsing server logs and application logs;

[0020] Real-time stream processing: Use Kafka and Flink real-time stream processing frameworks to capture, process, and analyze real-time data;

[0021] 1.3 Data cleaning and preprocessing:

[0022] Data deduplication: remove duplicate data through hash function or primary key comparison;

[0023] Data cleaning: Check and correct errors, outliers, or missing values ​​in the data;

[0024] Data standardization: converting data from different sources into a unified format and unit for subsequent analysis;

[0025] 1.4 Data storage and management:

[0026] Distributed database: Hadoop HDFS, HBase, and Cassandra distributed storage systems are used to meet the needs of massive data storage;

[0027] Data warehouse: Build a data warehouse and integrate cleaned data into the data warehouse through the ETL process to support query and analysis;

[0028] Data Lake: Use data lake technology to store raw data and preliminarily processed data, supporting multiple data formats and flexible data access methods.

[0029] The specific steps of the data processing and analysis module include data preprocessing, customer behavior pattern recognition, customer preference change analysis, potential risk point recognition and visual report formation, specifically:

[0030] 2.1 Data preprocessing:

[0031] Data cleaning: remove noise data, outliers, and duplicate records;

[0032] Data conversion: converting data from different sources into a unified format and unit;

[0033] Data integration: Integrate data from different data sources into a unified data set for subsequent analysis;

[0034] 2.2 Customer behavior pattern recognition: Use K-means or DBSCAN clustering algorithms to group customer groups and identify customer groups with similar behavior characteristics; these algorithms cluster based on customer attributes and behaviors; use Apriori or FP-Growth algorithms to mine frequent item sets and association rules in customer transaction sequences to identify customer purchasing habits and consumption cycle behavior patterns; use ARIMA or LSTM time series models to analyze the changing trends of customer behavior over time and predict future behavior;

[0035] 2.3 Analysis of changes in customer preferences:

[0036] Preference modeling: Build customer preference models to analyze customer interest preferences and their changes. These models are based on customer historical transaction records, browsing behavior, and evaluation information data;

[0037] Sentiment analysis: Use natural language processing technology to conduct sentiment analysis on customer feedback and comment text data to understand changes in customer satisfaction with products or services and their emotional tendencies;

[0038] Trend analysis: Identify the changing trends of customer preferences by comparing customer preference data over different time periods;

[0039] 2.4 Identification of potential risk points:

[0040] Anomaly detection: Use Isolation Forest and LOF algorithms to detect anomalies in customer behavior that may indicate fraudulent behavior or potential credit risk;

[0041] Risk assessment model: Build a risk assessment model to identify potential risk points based on customer attributes, behavioral data, and historical default record information;

[0042] Network analysis: Use graph theory and network analysis methods to analyze the relationships between customers and identify potential fraud gangs or risk transmission paths;

[0043] 2.5 Form a visual report

[0044] Data visualization: The analysis results are displayed intuitively in the form of charts and dashboards, making it easier for business personnel to understand and use them;

[0045] Report generation: Automatically generate customer insight reports, including analysis results and recommendations on customer behavior patterns, preference changes, and potential risk points.

[0046] The specific steps of the customer insight module include:

[0047] 4.1 Data Collection:

[0048] Sources: including basic customer information, transaction records, behavioral data, social media interactions, and customer service records;

[0049] 4.2 Data preprocessing:

[0050] Cleaning: remove duplicate data, handle missing values, detect and correct outliers;

[0051] Formatting: unifying the format of data from different sources;

[0052] Coding: Encoding categorical variables;

[0053] Standardization / normalization: standardize or normalize numerical variables to eliminate the impact of dimension;

[0054] 4.3 Feature Engineering:

[0055] Feature selection: Use Scikit-learn's SelectKBest or RFE for feature selection; Feature construction: Construct new features based on existing features;

[0056] Feature dimensionality reduction: For high-dimensional data, PCA or LDA methods are used for dimensionality reduction; 4.4. Model selection:

[0057] Supervised learning: When the target variable is known, logistic regression, decision tree, random forest, gradient boosted tree or neural network supervised learning algorithms are used;

[0058] Unsupervised learning: When the target variable is unknown, clustering algorithms such as K-means and DBSCAN can be used to segment customers;

[0059] Semi-supervised learning: When some target variables are known, a semi-supervised learning algorithm is used; 4.5 Model training and evaluation:

[0060] Training: Use the training set data to train the model and adjust the model parameters to optimize the performance indicators; Evaluation: Use the validation set or test set to evaluate the model performance;

[0061] Optimization: Adjust the model structure or parameters according to the evaluation results and perform iterative optimization.

[0062] The specific steps of the application service module are as follows:

[0063] 5.1 Data integration: Obtain customer segmentation, label update, personalized classification and prediction results from the customer insight module;

[0064] 5.2 Integrate customer information: Combine insights with the customer’s basic information, transaction records, and behavioral data to form a comprehensive customer profile;

[0065] 5.3 Data cleaning and organization: Clean the collected data, remove redundant and erroneous information, and ensure the accuracy and consistency of the data.

[0066] When using sequence models to analyze customer behavior, the specific steps are:

[0067] Data preparation: Convert transaction data into a sequence form, where each sequence contains a series of transaction items arranged in chronological order;

[0068] Generate candidate frequent item sets: Start with an item set of length 1 and gradually generate longer candidate item sets;

[0069] Calculate support: Scan the database and calculate the support of each candidate item set;

[0070] Pruning: In the process of generating candidate item sets, the support threshold is used for pruning, that is, those candidate item sets that do not meet the minimum support are removed;

[0071] Generate association rules: Generate association rules from frequent item sets and calculate their confidence; only keep the rules whose confidence is higher than the pre-screening threshold;

[0072] The advantages of the present invention are:

[0073] 1. Source data fusion technology: realize efficient integration of cross-platform and cross-format data.

[0074] 2. Deep learning algorithm: used for complex pattern recognition and prediction to improve the accuracy of customer behavior analysis.

[0075] 3. Real-time feedback mechanism: ensure that insights can be immediately applied to service optimization to form a closed-loop management.

[0076] This invention deeply integrates the concept of financial consumer protection into the design of the customer insight platform for the first time, emphasizing customer-centric service optimization. It introduces advanced AI technology to achieve dynamic capture of customer needs and forward-looking service response. It has established a complete closed-loop system from data collection to service application, effectively improving the intelligence and personalization of financial services.

[0077] The implementation of the present invention will greatly enhance the ability of financial institutions in protecting customer rights and interests, optimize service processes through accurate insight into customer needs, reduce consumer disputes, enhance customer trust and satisfaction, and promote the sustainable development of financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the main structure of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0080] See also Figure 1 ,The present invention relates to a customer insight platform in a financial consumer protection scenario, including a data acquisition module 1 for all-round data collection;

[0081] Data processing and analysis module 2, used to identify customer behavior patterns, preference changes and potential risk points;

[0082] The customer insight module 3 is used to update customer tags to achieve personalized classification and prediction; the application service module 4 is used to design customized financial service solutions based on the insight results. The specific collection steps of the data collection module include determining the source of data, collecting data, data cleaning and preprocessing, data storage and management, and security and compliance checks, specifically:

[0083] 1.1 Determine the source of the data:

[0084] Internal data: Capture transaction records, account information, service records and other data from the internal business systems, CRM systems, customer service systems, etc. of banks, insurance companies, securities and financial institutions through API interfaces;

[0085] External data: Use data crawler technology to obtain customer behavior data, public opinion information, and credit scores from external channels such as public websites, social media platforms, and credit rating agencies;

[0086] Customer interaction data: collect customer consultation, complaints, feedback and other information through customer service hotlines, online customer service, mobile applications and other channels;

[0087] IoT and sensor data: Collecting customer behavior data through sensors and logging technology at physical outlets (such as ATMs, smart counters) or online platforms (such as web browsing, APP usage);

[0088] 1.2 Collect data:

[0089] API Integration: Use RESTful API or SOAP protocol to exchange data with internal systems to achieve real-time or batch data capture.

[0090] Data crawler: Automatically access external websites and crawl required data according to preset rules. The crawler must comply with the robots.txt protocol and set the request frequency reasonably to avoid putting too much pressure on the target website.

[0091] Log analysis: extract user behavior data by parsing server logs, application logs, etc.; such as access time, page dwell time, click events, etc.

[0092] Real-time stream processing: Use real-time stream processing frameworks such as Kafka and Flink to capture, process, and analyze data generated in real time;

[0093] 1.3 Data cleaning and preprocessing:

[0094] Data deduplication: Remove duplicate data through hash functions or primary key comparison;

[0095] Data cleaning: Check and correct errors, outliers, or missing values ​​in the data, such as formatting date and time fields, filling missing values, etc.

[0096] Data standardization: converting data from different sources into a unified format and unit for subsequent analysis;

[0097] 1.4 Data storage and management:

[0098] Distributed database: Use distributed storage systems such as Hadoop HDFS, HBase, and Cassandra to meet the needs of massive data storage;

[0099] Data warehouse: Build a data warehouse and integrate cleaned data into the data warehouse through the ETL (extraction, transformation, loading) process to support complex query and analysis;

[0100] Data Lake: Use data lake technology (such as Amazon S3, Google Cloud Storage) to store raw data and preliminarily processed data, supporting multiple data formats and flexible data access methods.

[0101] The specific steps of the data processing and analysis module include data preprocessing, customer behavior pattern recognition, customer preference change analysis, potential risk point recognition and visual report formation, specifically:

[0102] 2.1 Data preprocessing:

[0103] Data cleaning: remove noisy data, outliers, duplicate records, etc.

[0104] Data conversion: Convert data from different sources into a unified format and unit, such as date and time formatting, numerical standardization, etc.

[0105] Data integration: Integrate data from different data sources into a unified data set for subsequent analysis;

[0106] 2.2 Customer behavior pattern recognition: Use clustering algorithms such as K-means or DBSCAN to group customer groups and identify customer groups with similar behavioral characteristics; these algorithms perform clustering based on customer attributes (such as age, gender, transaction frequency, etc.) and behavioral data (such as transaction amount, transaction time, transaction channel, etc.); use algorithms such as Apriori or FP-Growth to mine frequent item sets and association rules in customer transaction sequences to identify customer behavior patterns such as purchasing habits and consumption cycles; use time series models such as ARIMA or LSTM to analyze the changing trends of customer behavior over time and predict future behavior;

[0107] When using sequence models to analyze customer behavior, the specific steps are:

[0108] Purpose: To discover frequent itemsets and association rules in customer transaction sequences to identify customer purchasing habits.

[0109] Data preparation: Convert transaction data into a sequence form, where each sequence contains a series of transaction items arranged in chronological order;

[0110] Generate candidate frequent item sets: Start with an item set of length 1 and gradually generate longer candidate item sets. The Apriori algorithm uses an important property: an item set is frequent if and only if all of its non-empty subsets are frequent;

[0111] Itemsets of length 1: First, generate item sets of all single items, which are candidates for frequent item sets. If the total number of times an item appears in all transaction sequences exceeds a certain minimum support threshold (min_support), then the item is considered frequent.

[0112] Gradually generate longer candidate item sets: Based on the determined frequent item sets of length 1, generate candidate item sets of length 2 by combining these frequent items. For example, if the frequent item sets are {milk, bread}, then the candidate item set {milk, bread} can be generated.

[0113] Longer candidate item sets: Continue this process, using frequent item sets of length k to generate candidate item sets of length k + 1. This process continues until no new frequent item sets can be generated or a predetermined maximum item set length is reached;

[0114] Calculate support: Scan the database and calculate the support of each candidate item set; (that is, the number of sequences that contain the candidate item set).

[0115] Pruning: In the process of generating candidate item sets, use the support threshold to prune, that is, remove those candidate item sets that do not meet the minimum support; this helps to reduce the amount of calculation and improve efficiency. Generate association rules: Generate association rules from frequent item sets and calculate their confidence; only keep the rules with confidence higher than the pre-screening threshold.

[0116] 2.3 Analysis of changes in customer preferences:

[0117] Preference modeling: Build customer preference models (such as content-based recommendation systems, collaborative filtering, etc.) to analyze customer interest preferences and their changes. These models are based on data such as customers' historical transaction records, browsing behaviors, and evaluation information;

[0118] Sentiment analysis: Use natural language processing technology to conduct sentiment analysis on text data such as customer feedback and comments to understand changes in customer satisfaction with products or services and their emotional tendencies; Trend analysis: By comparing customer preference data in different time periods, identify changing trends in customer preferences, such as switching from one type of product to another.

[0119] 2.4 Identification of potential risk points:

[0120] Anomaly detection: Use algorithms such as Isolation Forest and LOF (Local Outlier Factor) to detect anomalies in customer behavior, which may indicate potential risks such as fraud and credit risk; Risk assessment model: Build risk assessment models (such as logistic regression, decision tree, random forest, etc.) to evaluate the identification of potential risk points based on information such as customer attributes, behavioral data, and historical default records;

[0121] Network analysis: Use graph theory and network analysis methods to analyze the relationships between customers and identify potential fraud gangs or risk transmission paths;

[0122] 2.5 Form a visual report

[0123] Data visualization: The analysis results are displayed intuitively through charts, dashboards, etc., making it easier for business personnel to understand and use them;

[0124] Report generation: Automatically generate customer insight reports, including analysis results and recommendations on customer behavior patterns, preference changes, potential risk points, etc.

[0125] The specific steps of the customer insight module include:

[0126] 4.1 Data Collection:

[0127] Sources: including basic customer information (such as name, age, gender, address, etc.), transaction records, behavioral data (such as browsing history, click behavior, dwell time, etc.), social media interactions, customer service records, etc.;

[0128] Method: Collect through the company's internal CRM system, e-commerce platform, social media platform, customer service system and other channels.

[0129] 4.2 Data preprocessing:

[0130] Cleaning: remove duplicate data, handle missing values, detect and correct outliers;

[0131] Formatting: Unifying the format of data from different sources; such as date format, currency unit, etc. Coding: Coding categorical variables; such as converting gender into a numerical variable (male = 1, female = 0).

[0132] Standardization / normalization: standardize or normalize numerical variables to eliminate the impact of dimension;

[0133] 4.3 Feature Engineering:

[0134] Feature selection: Use Scikit-learn's SelectKBest or RFE to select features; select features that have a significant impact on the model's prediction results based on business needs and data characteristics; Feature construction: construct new features based on existing features, such as customer lifetime value (CLV); Feature dimensionality reduction: For high-dimensional data, use PCA, LDA and other methods to reduce dimensionality to improve model efficiency;

[0135] 4.4. Model selection:

[0136] Supervised learning: When the target variable (such as customer value, purchase intention) is known, supervised learning algorithms such as logistic regression, decision tree, random forest, gradient boosting tree or neural network are used; Unsupervised learning: When the target variable is unknown, clustering algorithms such as K-means and DBSCAN can be used for customer segmentation;

[0137] Semi-supervised learning: When part of the target variable is known, a semi-supervised learning algorithm is used;

[0138] Semi-supervised learning is between supervised learning and unsupervised learning. Some target variables are known, but most are unknown. This learning method is suitable for situations where the cost of obtaining labels is high. For example:

[0139] Partially labeled classification problem: If we know that some customers belong to a specific group (for example, some customers are known to be high-value customers), but the labels of other customers are unknown, we can use a semi-supervised learning algorithm to predict the group of customers with unknown labels.

[0140] Feature Learning: In some cases, we may have prior knowledge about certain features in the dataset, but limited knowledge about the target variable of the entire dataset.

[0141] Semi-supervised learning algorithms include:

[0142] Self-training model: Train a model with known labels, then use this model to predict labels for data points with unknown labels, and use these predictions as pseudo labels to train the model again.

[0143] Pseudo-label generation: Similar to self-training, but may include more sophisticated techniques such as consistency regularization to improve the accuracy of predictions.

[0144] Graph-based methods: Utilize the relationships between data points, such as through graph representation, to propagate label information.

[0145] 4.5 Model training and evaluation:

[0146] Training: Use the training set data to train the model and adjust the model parameters to optimize the performance indicators; Evaluation: Use the validation set or test set to evaluate the model performance, such as accuracy, recall, F1 score, ROC curve, AUC value, etc.;

[0147] Optimization: Adjust the model structure or parameters according to the evaluation results and perform iterative optimization.

[0148] The specific construction steps of the customer insight module are:

[0149] 1. Data preparation

[0150] Step 1.1 Data Collection: Identify data sources, such as CRM systems, e-commerce platforms, social media, customer service records, etc. Collect data using APIs, data export tools, or data warehouses.

[0151] Step 1.2 Data cleaning: Remove duplicate records. Handle missing values ​​(fill, delete or interpolate). Detect and handle outliers (such as through quantiles, IQR, etc.).

[0152] Step 1.3 Format the data and unify the formats of date, time, currency, etc.

[0153] Encode categorical variables (e.g. using one-hot encoding or label encoding).

[0154] Step 1.4: Feature selection and construction: Select relevant features based on business knowledge and data exploration results. Construct new features to enhance the model's predictive power (such as consumption frequency, purchase cycle, etc.).

[0155] Step 1.5 Data Split: Divide the dataset into training set, validation set, and test set (usually using 70% / 15% / 15% or similar ratio).

[0156] 2. Model Construction

[0157] Step 2.1 Choose a suitable algorithm

[0158] Choose the appropriate algorithm (such as logistic regression, decision tree, random forest, K-means, etc.) based on the business objectives and data type.

[0159] Step 2.2 Model training: Train the model using the training set data.

[0160] Adjust model parameters to optimize performance metrics (such as accuracy, recall, F1 score, etc.). Step 2.3 Model Evaluation: Evaluate model performance using the validation set.

[0161] Check the confusion matrix, ROC curve, AUC value and other indicators.

[0162] Step 2.4 Model optimization: Adjust the model structure or parameters based on the evaluation results.

[0163] Use techniques such as cross-validation to improve the generalization ability of the model.

[0164] Step 2.5 Model selection: Select the best model from multiple candidate models.

[0165] 3. Customer Segmentation

[0166] Step 3.1 Apply clustering algorithm: Use clustering algorithms such as K-means, DBSCAN, etc. to segment customers.

[0167] Determine the number of clusters (K value) or the stopping condition for clustering.

[0168] Step 3.2 Analyze clustering results: Analyze the characteristic differences and behavior patterns of each cluster.

[0169] Give each cluster a name to facilitate understanding and communication.

[0170] 4. Label Update

[0171] Step 4.1 Design a labeling system: Design a customer labeling system based on business needs and clustering results.

[0172] Determine the naming convention and hierarchy of tags.

[0173] Step 4.2 Assign tags: Assign tags to customers based on clustering results and other relevant information (such as customer value, purchase intention, etc.). Update customer tag information in the CRM system.

[0174] 5. Personalized classification and prediction

[0175] Step 5.1: Personalized classification

[0176] Carry out personalized classification based on customer tags and feature information. Provide different product and service recommendations, marketing strategies, etc. for different categories of customers.

[0177] Step 5.2: Prediction model application: Use the trained prediction model (such as purchase intention prediction, churn risk prediction, etc.) to predict customer behavior. Adjust marketing strategies or intervention measures based on the prediction results.

[0178] Step 5.3: Continuous optimization: Monitor customer behavior and market changes. Update models and datasets regularly. Continuously evaluate and optimize model performance.

[0179] 6. Report and Feedback

[0180] Step 6.1 Generate insight report: Organize the results of customer segmentation, tag update, personalized classification and prediction into a report. Analyze the impact of the insight results on the business and potential opportunities.

[0181] Step 6.2 Feedback and Iteration: Share the insight report with relevant departments and personnel. Collect feedback and use it for continuous optimization and iteration of the model.

[0182] Formula and Algorithm Example: Logistic Regression

[0183] Logistic regression is a linear model widely used in classification problems. Its prediction function is: h_\theta(x)=\frac{1}{1+e^{-\theta^T x}}\], where \(h_\theta(x)\) represents the probability of predicting the positive class, \(\theta\) is the model parameter, and \(x\) is the input feature. The goal of model training is to find the optimal \(\theta\) so that the error between the predicted result and the actual result is minimized. This is usually achieved by maximizing the log-likelihood function or using optimization algorithms such as gradient descent.

[0184] K-means clustering is a distance-based clustering algorithm. Its goal is to divide data points into K clusters so that the points within each cluster are as close as possible, while the points between different clusters are as far away as possible.

[0185] The algorithm steps include: 1. Randomly select K points as the initial centroid. 2. Assign each point to the cluster corresponding to the nearest centroid. 3. Recalculate the centroid of each cluster (i.e. the mean of all points in the cluster). 4. Repeat steps 2 and 3 until the centroid no longer changes significantly or the preset number of iterations is reached. Through these steps, the K-means algorithm can divide customer data into different groups, thereby achieving customer segmentation and personalized classification.

[0186] The specific steps of the application service module are as follows:

[0187] 5.1 Data integration: Obtain customer segmentation, label update, personalized classification and prediction results from the customer insight module;

[0188] 5.2 Integrate customer information: Combine the insights with the customer’s basic information, transaction records, behavioral data, etc. to form a comprehensive customer profile;

[0189] 5.3 Data cleaning and organization: Clean the collected data, remove redundant and erroneous information, and ensure the accuracy and consistency of the data.

[0190] The application service module is used to design customized financial service solutions based on customer insight results, which is a key link in realizing personalized and precise financial services. The following are the specific steps to realize the application service module, including data integration, solution design, implementation and monitoring: 1. Data integration to collect insight results: Get the latest customer segmentation, label updates, personalized classification and prediction results from the customer insight module.

[0191] Integrate customer information: Combine insights with basic customer information, transaction records, behavioral data, etc. to form a comprehensive customer profile.

[0192] Data cleaning and organization: Clean the collected data to remove redundant and erroneous information and ensure the accuracy and consistency of the data.

[0193] 2. Solution Design

[0194] Demand analysis: Based on customer profiles and insights, analyze customers’ financial needs, risk preferences, purchasing power and other factors.

[0195] Customized design:

[0196] Loan plan: Design loan plans with different loan amounts, interest rates, terms, etc. based on factors such as the customer's income status, credit record, and repayment ability.

[0197] Investment plan: Recommend suitable investment product portfolios based on customer risk preferences, investment goals, liquidity and other factors, including stocks, funds, bonds, financial products, etc. Payment and settlement plan: Provide convenient payment methods (such as mobile payment, Internet payment) and secure settlement solutions (such as electronic payment, hardware wallet) based on customer payment habits and needs.

[0198] Insurance plan: Design personalized insurance plans based on the client's occupational characteristics, health status, family structure and other factors, including life insurance, property insurance, accidental injury insurance, etc. Plan optimization: Use algorithms and models (such as machine learning, optimization algorithms, etc.) to simulate and evaluate the design plan to ensure the feasibility and optimality of the plan.

[0199] 3. Implementation and Monitoring

[0200] 1. Solution refinement and customization

[0201] Steps:

[0202] Refine the plan: Based on the preliminary design plan, further refine and personalize the plan according to the customer's specific needs and insights. This includes determining the specific service content, service process, service standards, etc.

[0203] Customized services: Ensure that the service plan fully meets the customer's personalized needs, such as adjusting the loan amount, interest rate, repayment method, etc. to meet the customer's specific situation.

[0204] 2. Communication and confirmation

[0205] Steps:

[0206] Communicate with customers: Present the refined customized financial service plan to customers in detail, explain each part of the plan and the expected results, and ensure that customers fully understand it.

[0207] Confirm the plan: Confirm the feasibility and acceptability of the plan with the customer, collect customer feedback, and make adjustments as needed.

[0208] 3. Service preparation and deployment

[0209] Steps:

[0210] Resource allocation: According to the service plan, allocate necessary human, material, financial and other resources to ensure the smooth implementation of the service.

[0211] System configuration: If the service involves an online system or platform, corresponding system configuration and testing are required to ensure that the system runs stably and meets service requirements.

[0212] Personnel training: Provide professional training to personnel involved in the service to ensure that they are familiar with the service process, master service skills, and can provide high-quality services to customers.

[0213] 4. Service Execution and Monitoring

[0214] Steps:

[0215] Execute services: Execute customized financial services according to service plans and customer requirements. During the service process, maintain communication with customers and solve possible problems in a timely manner.

[0216] Monitoring services: Monitor the service execution process to ensure that the service quality meets the standards. At the same time, collect data and information during the service process to provide a basis for subsequent service improvement and optimization.

[0217] 5. Feedback and Adjustment

[0218] Steps:

[0219] Collect feedback: Actively collect customer feedback during and after the service execution, including satisfaction, improvement suggestions, etc.

[0220] Evaluate the effect: Evaluate the service effect and analyze whether the service has achieved the expected goals, as well as the existing problems and deficiencies.

[0221] Adjustment and optimization: Adjust and optimize service plans based on customer feedback and evaluation results to continuously improve service quality and customer satisfaction.

[0222] 6. Post-maintenance and follow-up

[0223] Steps:

[0224] Regular return visits: Regularly return visits to customers to understand service usage and collect new needs and opinions.

[0225] Continuous support: Provide customers with continuous technical support and service support to ensure that problems encountered during use can be solved in a timely manner.

[0226] Service upgrade: Upgrade and update services according to market changes and changes in customer needs to maintain their competitiveness and attractiveness.

[0227] 7. Monitoring and adjustment:

[0228] Effect monitoring: Regularly collect data on customers’ use of customized financial service solutions, including usage rate, satisfaction, benefits, etc.

[0229] Data analysis: Analyze the collected data to evaluate the actual effect of the plan and market feedback.

[0230] Program adjustment: timely adjust and optimize customized financial service programs based on data analysis results and market demand changes to ensure the continued effectiveness and market competitiveness of the programs.

[0231] 4. Continuous Improvement

[0232] Technology upgrade: Pay attention to the latest developments in financial technology, continuously introduce new technologies and methods, and improve the intelligence and automation level of customized financial services.

[0233] Market research: Strengthen market research to understand customers' latest needs and industry trends, and provide strong support for the continuous innovation and improvement of customized financial service solutions.

[0234] Customer feedback: Establish an effective customer feedback mechanism to collect and process customer opinions and suggestions in a timely manner, and continuously improve customized financial service solutions.

[0235] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A customer insight platform in the context of financial consumer protection, characterized by: include Data acquisition module, used for all-round data collection; Data processing and analysis module to identify customer behavior patterns, preference changes and potential risk points; Customer insight module, used to update customer tags and achieve personalized classification and prediction; The application service module is used to design customized financial service solutions based on insight results.

2. The customer insight platform in the financial consumer protection scenario according to claim 1 is characterized in that: The specific collection steps of the data collection module include determining the source of data, collecting data, data cleaning and preprocessing, data storage and management, and security and compliance checks, specifically: 1.1 Determine the source of the data: Internal data: Capture transaction records, account information, and service record data from the internal systems of banks, insurance companies, and securities financial institutions through API interfaces; External data: Use data crawler technology to obtain customer behavior data, public opinion information, and credit scores from public websites, social media platforms, and external channels of credit rating agencies; Customer interaction data: collect customer consultation, complaints, and feedback information through customer service hotlines, online customer service, and mobile application channels; IoT and sensor data: Collecting customer behavior data through sensors and logging technologies at physical locations or online platforms; 1.2 Collect data: API integration: Use RESTful API or SOAP protocol to exchange data with internal systems to achieve real-time or batch data capture; Data crawler: automatically access external websites and crawl required data according to preset rules; Log analysis: extract user behavior data by parsing server logs and application logs; Real-time stream processing: Use Kafka and Flink real-time stream processing frameworks to capture, process, and analyze real-time data; 1.3 Data cleaning and preprocessing: Data deduplication: remove duplicate data through hash function or primary key comparison; Data cleaning: Check and correct errors, outliers, or missing values ​​in the data; Data standardization: converting data from different sources into a unified format and unit for subsequent analysis; 1.4 Data storage and management: Distributed database: Hadoop HDFS, HBase, and Cassandra distributed storage systems are used to meet the needs of massive data storage; Data warehouse: Build a data warehouse and integrate cleaned data into the data warehouse through the ETL process to support query and analysis; Data Lake: Use data lake technology to store raw data and preliminarily processed data, supporting multiple data formats and flexible data access methods.

3. The customer insight platform in the financial consumer protection scenario according to claim 1 or 2, characterized in that: The specific steps of the data processing and analysis module include data preprocessing, customer behavior pattern recognition, customer preference change analysis, potential risk point recognition and visual report formation, specifically: 2.1 Data preprocessing: Data cleaning: remove noise data, outliers, and duplicate records; Data conversion: converting data from different sources into a unified format and unit; Data integration: Integrate data from different data sources into a unified data set for subsequent analysis; 2.2 Customer behavior pattern recognition: Use K-means or DBSCAN clustering algorithm to group customer groups and identify customer groups with similar behavior characteristics; These algorithms cluster customers based on their attributes and behaviors; use Apriori or FP-Growth algorithms to mine frequent item sets and association rules in customer transaction sequences to identify customers’ purchasing habits and consumption cycle behavior patterns; use ARIMA or LSTM time series models to analyze customer behavior trends over time and predict future behavior; 2.3 Analysis of changes in customer preferences: Preference modeling: Build customer preference models to analyze customer interest preferences and their changes. These models are based on customer historical transaction records, browsing behavior, and evaluation information data; Sentiment analysis: Use natural language processing technology to conduct sentiment analysis on customer feedback and comment text data to understand changes in customer satisfaction with products or services and their emotional tendencies; Trend analysis: Identify the changing trends of customer preferences by comparing customer preference data over different time periods; 2.4 Identification of potential risk points: Anomaly detection: Use Isolation Forest and LOF algorithms to detect anomalies in customer behavior that may indicate fraudulent behavior or potential credit risk; Risk assessment model: Build a risk assessment model to identify potential risk points based on customer attributes, behavioral data, and historical default record information; Network analysis: Use graph theory and network analysis methods to analyze the relationships between customers and identify potential fraud gangs or risk transmission paths; 2.5 Form a visual report Data visualization: The analysis results are displayed intuitively in the form of charts and dashboards, making it easier for business personnel to understand and use them; Report generation: Automatically generate customer insight reports, including analysis results and recommendations on customer behavior patterns, preference changes, and potential risk points.

4. The customer insight platform in the financial consumer protection scenario according to claim 4 is characterized in that: The specific steps of the customer insight module include: 4.1 Data Collection: Sources: including basic customer information, transaction records, behavioral data, social media interactions, and customer service records; 4.2 Data preprocessing: Cleaning: remove duplicate data, handle missing values, detect and correct outliers; Formatting: unifying data from different sources into a unified format; Coding: Encoding categorical variables; Standardization / normalization: standardize or normalize numerical variables to eliminate the impact of dimension; 4.3 Feature Engineering: Feature selection: Use Scikit-learn's SelectKBest or RFE for feature selection; Feature construction: construct new features based on existing features; Feature dimensionality reduction: For high-dimensional data, PCA or LDA methods are used for dimensionality reduction; 4.

4. Model selection: Supervised learning: When the target variable is known, logistic regression, decision tree, random forest, gradient boosted tree or neural network supervised learning algorithms are used; Unsupervised learning: When the target variable is unknown, clustering algorithms such as K-means and DBSCAN can be used to segment customers; Semi-supervised learning: When part of the target variable is known, a semi-supervised learning algorithm is used; 4.5 Model training and evaluation: Training: Use the training set data to train the model and adjust the model parameters to optimize the performance indicators; Evaluation: Use validation or test sets to evaluate model performance; Optimization: Adjust the model structure or parameters according to the evaluation results and perform iterative optimization.

5. The customer insight platform in the financial consumer protection scenario according to claim 4 is characterized in that: The specific steps of the application service module are as follows: 5.1 Data integration: Obtain customer segmentation, label update, personalized classification and prediction results from the customer insight module; 5.2 Integrate customer information: Combine insights with the customer’s basic information, transaction records, and behavioral data to form a comprehensive customer profile; 5.3 Data cleaning and organization: Clean the collected data, remove redundant and erroneous information, and ensure the accuracy and consistency of the data.

6. The customer insight platform in the financial consumer protection scenario according to claim 3 is characterized in that: When using sequence models to analyze customer behavior, the specific steps are: Data preparation: Convert transaction data into a sequence form, where each sequence contains a series of transaction items arranged in chronological order; Generate candidate frequent item sets: Start with an item set of length 1 and gradually generate longer candidate item sets; Calculate support: Scan the database and calculate the support of each candidate item set; Pruning: In the process of generating candidate item sets, the support threshold is used for pruning, that is, those candidate item sets that do not meet the minimum support are removed; Generate association rules: Generate association rules from frequent item sets and calculate their confidence; Only rules with confidence above the pre-qualification threshold are retained.

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