Financial credit intelligent marketing system and method

Abstract

The embodiment of the invention provides a financial credit intelligent marketing system and method. Comprising a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multi-modal data fusion and feature engineering module, a user portrait construction and subdivision module, an intelligent marketing strategy making module, a marketing execution and real-time monitoring module and an effect evaluation and strategy iteration module. The financial credit intelligent marketing system comprises a plurality of functional modules such as data acquisition, data processing, model training, strategy making, real-time marketing, batch marketing, execution monitoring and the like. A comprehensive and multi-dimensional user portrait is formed by integrating enterprise internal data and an external third-party authorization data source. And machine learning algorithms such as XGBoost, LightGBM and the like are utilized to carry out deep mining and analysis on user behaviors, and purchase intention and demand change of the user are predicted. Meanwhile, based on the Flink real-time data processing technology, quick response and dynamic adjustment of marketing activities are achieved.

Description

Technical Field

[0001] This invention relates to the fields of financial technology and big data analytics, and in particular to a smart marketing system and method for financial credit. Background Technology

[0002] Currently, existing intelligent marketing systems for financial lending have achieved basic applications of user profiling in financial lending marketing, but there is still room for improvement in data dimensions, model dynamism, and cross-industry adaptability. Existing user-profiling-based intelligent marketing systems for financial lending mainly suffer from defects such as data fragmentation and insufficient integration, static and outdated user profiles, single profile dimensions and over-reliance on historical data, homogenized marketing strategies and conflicting user experience, insufficient system stability, and weak anti-fraud capabilities. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a financial credit intelligent marketing system and method to achieve accurate user segmentation and personalized marketing strategy recommendation through multi-dimensional data fusion and machine learning algorithms.

[0004] In a first aspect, embodiments of the present invention provide a financial credit intelligent marketing system, comprising: a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multimodal data fusion and feature engineering module, a user profile construction and segmentation module, an intelligent marketing strategy formulation module, a marketing execution and real-time monitoring module, and an effect evaluation and strategy iteration module; the multi-source data acquisition and integration module is used to collect internal data, external data, and third-party data; the data preprocessing and fusion module is used for data cleaning and standardization and multi-source data fusion; the multimodal data fusion and feature engineering module is used for data fusion and feature engineering; the user profile construction and segmentation module is used for feature tag extraction and customer segmentation; the intelligent marketing strategy formulation module is used for personalized product recommendations, marketing reach optimization, and automated marketing processes; the marketing execution and real-time monitoring module is used for automated marketing execution and real-time effect monitoring; and the effect evaluation and strategy iteration module is used for marketing effect evaluation and strategy optimization.

[0005] In optional embodiments of this application, internal data includes: customer base data, credit behavior data, and channel interaction data; external data includes: credit data, consumer behavior data, and social media data; third-party data includes: loan assistance and loan aggregator platform data and advertising placement data.

[0006] In optional embodiments of this application, data cleaning and standardization include: deduplication and verification, missing value handling, and data standardization; multi-source data fusion includes: primary key association, fuzzy matching, and real-time data fusion.

[0007] In optional embodiments of this application, the primary key association is based on an identifier library that associates internal transaction data with external credit data. The identifiers in the identifier library include: legal identifiers, behavioral identifiers, and biometric identifiers. The association rules for the primary key association include: fuzzy matching and cross-institutional mapping. Real-time data fusion includes: data collection, primary key mapping, and data fusion.

[0008] In optional embodiments of this application, data fusion includes: real-time data link, batch processing fusion, and graph data fusion; feature engineering includes: static features, dynamic features, and graph features.

[0009] In optional embodiments of this application, feature tag extraction includes: basic attributes, financial attributes, and behavioral attributes; customer segmentation includes: life cycle stratification, value stratification, and risk stratification.

[0010] In optional embodiments of this application, personalized product recommendations include: dynamic pricing and scenario-based recommendations; marketing outreach optimization includes: timing selection and channel matching; and automated marketing processes include: a data management platform and an intelligent outbound calling system.

[0011] In optional embodiments of this application, automated marketing execution includes: multi-channel outreach and testing of different copywriting; real-time performance monitoring includes: key indicator tracking and anomaly alerts.

[0012] In optional embodiments of this application, marketing effectiveness evaluation includes: return on investment analysis and attribution analysis; strategy optimization includes: user feedback loop and model iteration.

[0013] Secondly, embodiments of the present invention also provide a financial credit intelligent marketing method, applied to the aforementioned financial credit intelligent marketing system. The financial credit intelligent marketing method includes: a multi-source data acquisition and integration module that collects internal, external, and third-party data; a data preprocessing and fusion module that performs data cleaning, standardization, and multi-source data fusion; a multi-modal data fusion and feature engineering module that performs data fusion and feature engineering; a user profile construction and segmentation module that extracts feature tags and segments customer groups; an intelligent marketing strategy formulation module that performs personalized product recommendations, optimizes marketing outreach, and automates marketing processes; a marketing execution and real-time monitoring module that performs automated marketing execution and real-time effect monitoring; and an effect evaluation and strategy iteration module that evaluates marketing effectiveness and optimizes strategies.

[0014] The embodiments of the present invention bring the following beneficial effects: This invention provides a smart marketing system and method for financial credit, including multiple functional modules such as data collection, data processing, model training, strategy formulation, real-time marketing, batch marketing, and execution monitoring. By integrating internal enterprise data with external third-party authorized data sources, a comprehensive and multi-dimensional user profile is formed. Machine learning algorithms such as XGBoost (eXtremeGradient Boosting, a distributed gradient boosting library) and LightGBM (Light Gradient Boosting Machine, an efficient and scalable machine learning algorithm based on gradient boosting decision trees) are used to deeply mine and analyze user behavior, predicting changes in user purchase intentions and needs. Simultaneously, based on Flink (an open-source stream processing framework) real-time data processing technology, rapid response and dynamic adjustment of marketing activities are achieved.

[0015] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0016] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a financial credit intelligent marketing system provided in an embodiment of the present invention; Figure 2 A schematic diagram of the system architecture of a financial credit intelligent marketing system provided in an embodiment of the present invention; Figure 3 A flowchart of a financial credit intelligent marketing method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a financial credit intelligent marketing method provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Currently, existing intelligent marketing systems for financial lending based on user profiles mainly suffer from the following shortcomings and defects: I. Data Level: Data Silos and the Quality Dilemma

[0021] 1. Insufficient data fragmentation and integration: Internal data fragmentation: Data within banks and other institutions is scattered across different business departments (such as retail, risk control, and technology), creating data silos between departments. Online and offline marketing activities operate independently, resulting in user profiles relying on fragmented data and failing to form a comprehensive view. For example, Bank Z's precious metals marketing project lacked a unified big data platform, leading to incomplete customer profiles and impacting the effectiveness of targeted marketing.

[0022] Limited access to external data: Due to data compliance and barriers between institutions, banks have difficulty obtaining third-party data such as e-commerce and social platforms, resulting in a single dimension of user profiles (such as only including financial attributes and lacking consumption preferences, social behaviors, etc.).

[0023] 2. Data update delays and inconsistent quality: Slow tag iteration: User tags rely on manual or infrequent updates, which cannot reflect changes in user credit status in real time (such as income fluctuations, multiple loans), resulting in a mismatch between marketing strategies and user needs.

[0024] Data noise interference: External data (such as operators and financial service providers) may contain dirty or duplicate data, which can affect the accuracy of the model.

[0025] II. Technical Aspects: Algorithm and System Bottlenecks.

[0026] 1. Limitations of the algorithm: The recommendation logic is rigid: it relies excessively on traditional algorithms such as collaborative filtering and rule engines, lacking dynamic pricing and risk control coordination mechanisms. For example, similar products typically adjust interest rates statically based on user risk levels, rather than incorporating real-time market supply and demand and changes in user behavior.

[0027] Insufficient application of reinforcement learning: Only a few systems adopt deep reinforcement learning optimization strategies, and most products still passively iterate through A / B testing, resulting in limited room for improvement in conversion rates.

[0028] 2. System response and stability issues: Lack of marketing loop: CRM (Customer Relationship Management), call platform and risk control system operate independently, resulting in a break in the marketing process (such as inconsistency between telemarketing scripts and APP (application) push content).

[0029] Lag in high-concurrency scenarios: During promotional activities, system response delays can lead to user churn.

[0030] III. Business Level: Strategy and Compliance Challenges

[0031] 1. Homogeneous marketing strategies: Mass marketing dominates: Dissemination-based marketing still prevails, lacking segmented operations based on the user lifecycle.

[0032] The product positioning is too broad: credit products are highly homogenized, with most institutions adopting a fixed model of "interest rate discounts + SMS push notifications" without any differentiation strategy, making it difficult to meet the differentiated needs of small and micro business owners and individual consumers.

[0033] 2. Compliance and risk control loopholes: Data privacy risks: Some products do not strictly implement the Personal Information Protection Law, and there is a risk of excessive collection of user data (such as address books and location information).

[0034] Insufficient anti-fraud capabilities: Low recognition rate of new fraud patterns such as group loan fraud and identity theft.

[0035] IV. User experience: Lack of reach and feedback.

[0036] 1. Poor channel synergy: Disconnection of touchpoints: Lack of coordination between offline outlets, online apps, and third-party platforms leads to a broken user journey.

[0037] Insufficient content personalization: Marketing content is not dynamically adjusted based on real-time user behavior (such as browsing and clicking), resulting in a "broad-based" perception among users.

[0038] 2. Inadequate feedback mechanism: Delayed performance tracking: Lack of real-time monitoring of marketing performance and long strategy optimization cycles.

[0039] Lack of user feedback: Inadequate channels for complaints and suggestions negatively impact brand reputation.

[0040] Therefore, the intelligent marketing system for financial lending based on user profiles can be optimized in the following directions: Building a full-link data ecosystem: breaking down internal data silos and introducing technologies such as federated learning to achieve cross-institutional data collaboration. Introducing adaptive algorithm models: combining reinforcement learning and causal reasoning to achieve real-time strategy optimization. Strengthening cross-departmental collaboration mechanisms: establishing a triangular collaborative team linking "data-business-risk control" and unifying KPI (Key Performance Indicator) design. Improving the compliance and risk control system: embedding intelligent dual recording and anti-fraud modules to ensure the legality and compliance of data use. Cultivating multi-skilled talent: shortening the training cycle and enhancing the team's technological sensitivity through industry-academia-research cooperation.

[0041] In summary, existing intelligent marketing systems for financial lending based on user profiles are insufficient in four major areas: data integration, algorithm dynamism, business collaboration, and compliance risks. True intelligent marketing can only be achieved through multi-dimensional optimization in technology, organization, and processes.

[0042] Based on this, the present invention provides a financial credit intelligent marketing system and method, specifically involving a financial credit product intelligent marketing system and method based on user profile construction and dynamic optimization, which achieves accurate user segmentation and personalized marketing strategy recommendation through multi-dimensional data fusion and machine learning algorithms.

[0043] To facilitate understanding of this embodiment, a detailed description of a financial credit intelligent marketing system disclosed in this embodiment of the invention will be provided first.

[0044] Example 1: This invention provides a financial credit intelligent marketing system, see [link / reference]. Figure 1 The diagram shows the structure of a financial credit intelligent marketing system, which includes: a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multimodal data fusion and feature engineering module, a user profile construction and segmentation module, an intelligent marketing strategy formulation module, a marketing execution and real-time monitoring module, and an effect evaluation and strategy iteration module.

[0045] This invention provides a smart marketing system for financial lending, comprising multiple functional modules including data collection, data processing, model training, strategy formulation, real-time marketing, batch marketing, and execution monitoring. By integrating internal enterprise data with external third-party authorized data sources, a comprehensive, multi-dimensional user profile is formed. Machine learning algorithms such as XGBoost and LightGBM are used to deeply mine and analyze user behavior, predicting changes in user purchase intentions and needs. Simultaneously, based on Flink real-time data processing technology, rapid response and dynamic adjustment of marketing activities are achieved.

[0046] 1. Multi-source data acquisition and integration module, used to collect internal data, external data and third-party data.

[0047] The internal data collection in this embodiment can be: collecting structured data such as the customer's basic data, transaction records, repayment history, credit report, and account behavior from financial institutions.

[0048] In some embodiments, internal data includes: customer base data, credit behavior data, and channel interaction data; external data includes: credit data, consumer behavior data, and social media data; third-party data includes: loan facilitation and loan aggregator platform data and advertising data.

[0049] 1. Customer basic data: Obtain basic information such as customer name, age, occupation, and income from the bank's CRM system, transaction system, etc.

[0050] Credit behavior data: Integrate customers' historical borrowing records, repayment behavior, overdue status, etc., to build a credit behavior profile.

[0051] Channel interaction data: Records customer behavior through channels such as apps, online banking, and offline branches, including page views, click streams, and inquiry records.

[0052] 2. External data access: Access unstructured data such as third-party credit reporting platforms, e-commerce consumption records (consumer behavior data), social media behavior, and telecommunications operator data.

[0053] Credit data: Connect with platforms such as the People's Bank of China Credit Reference Center and Baihang Credit Reference Center to obtain customer credit scores, debt information, public records, etc.

[0054] Consumer behavior data: Obtain customer consumption habits and payment preferences through third-party cooperation (such as e-commerce platforms and payment institutions).

[0055] Social media data: Analyze customer behavior on social platforms to supplement unstructured information such as interests, preferences, and social relationships.

[0056] 3. Third-party cooperation data: Obtain streaming data such as loan assistance / loan aggregator platform data, advertising data, customer real-time location, browsing behavior, and payment actions through IoT devices or API interfaces.

[0057] Loan facilitation / loan aggregation platform: Connect with partner interfaces to obtain data on campaign performance such as user source identification, registration conversion rate, and credit granting cost.

[0058] Advertising data: Collect metrics such as impressions, clicks, and downloads for news feed ads and app store promotions.

[0059] II. Data Preprocessing and Fusion Module, used for data cleaning and standardization and multi-source data fusion.

[0060] In some embodiments, data cleaning and standardization include: deduplication and verification, missing value handling, and data standardization; multi-source data fusion includes: primary key association, fuzzy matching, and real-time data fusion.

[0061] In this embodiment, data cleaning and standardization can be performed by: removing duplicate data, correcting erroneous fields (such as validating the format of ID card numbers), unifying data formats (such as converting timestamps), and filling in missing values ​​(such as predicting consumption capacity based on historical behavior).

[0062] 1. Deduplication and Validation: Perform format validation on key fields such as ID card number and mobile phone number, and delete duplicate or invalid records.

[0063] 2. Missing value handling: Fill in missing data (such as customers who did not fill in their income) through interpolation, model prediction, etc.

[0064] 3. Data standardization: unify the time format (e.g., unify "2025-07-01" and "2025 / 7 / 1" into a standard date format) and the unit (e.g., unify the amount to yuan).

[0065] In this embodiment, multi-source data fusion can be achieved by fusing multi-source data through primary key association, fuzzy matching, and real-time data fusion.

[0066] 1. Primary Key Association: Using unique identifiers such as customer ID number and mobile phone number as links, internal transaction data and external credit data are associated.

[0067] 2. Fuzzy matching: Perform semantic analysis on unstructured data (such as addresses and job descriptions) to achieve cross-data source association.

[0068] 3. Real-time data fusion: Through tools such as DataPipeline (an enterprise-level real-time data fusion platform), online transaction data and offline counter data are accessed in real time to ensure the timeliness of risk control and marketing strategies.

[0069] In some embodiments, primary key association is based on an identifier library that associates internal transaction data with external credit data. The identifiers in the identifier library include: legal identifiers, behavioral identifiers, and biometric identifiers. The association rules for primary key association include: fuzzy matching and cross-institutional mapping. Real-time data fusion includes: data collection, primary key mapping, and data fusion.

[0070] The primary key association technology in this embodiment is the core foundation for realizing multi-source data fusion. It uses unique identifiers such as customer ID numbers and mobile phone numbers as links to accurately associate internal transaction data with external credit data, providing data support for subsequent intelligent decision-making and precision marketing. The following section, based on the characteristics of this technology, further explains its technical details, beneficial effects, and synergistic effect with the overall system: (1) Data link design.

[0071] Unique Identification System: The system constructs a multi-dimensional unique identification database, including: Legal Identifiers: ID card number, unified social credit code (for corporate customers); Behavioral Identifiers: mobile phone number, device fingerprint, bank account; Biometric Identifiers: facial feature value (requires customer authorization).

[0072] Association Rule Engine: Develop intelligent matching algorithms to solve the data silo problem, including: Fuzzy matching: Using technologies such as edit distance and voiceprint recognition, associate inconsistent scenarios such as changes in mobile phone numbers and abbreviations of names (e.g., associating "Zhang San" with "Mr. Zhang"); Cross-institutional mapping: Establish a coding comparison table between internal bank customer IDs and external credit reporting agencies and third-party data sources (such as tax and electricity).

[0073] (2) Data fusion process: Step 1: Data Collection. Internal Data: Extract structured data such as customer transaction records, assets and liabilities, and product holdings from the core system; External data: Semi-structured data such as credit reports from the People's Bank of China, business registration information, and judicial proceedings are obtained through API interfaces.

[0074] Step 2: Primary Key Mapping. Utilize a graph database (such as Neo4j) to construct a customer relationship network. For data lacking a primary key, employ multi-anchor linking techniques, such as indirect links through mutual contacts or trading partners.

[0075] Step 3: Data Fusion. Based on the primary key merged dataset, a 360° customer profile is generated, including: basic attributes (age, place of origin, marital status), behavioral characteristics (purchase frequency, repayment habits, investment preferences), and risk tags (number of overdue payments, litigation records, multiple loans).

[0076] By employing primary key association technology, this embodiment constructs a data fusion infrastructure, not only resolving the long-standing "data silo" problem in the financial industry but also providing a reliable data foundation for higher-level applications such as intelligent risk control and precision marketing. This technology, along with innovations such as dynamic weight adjustment and multi-model fusion, creates a synergistic effect, jointly driving the digital and intelligent transformation of financial credit marketing.

[0077] III. Multimodal Data Fusion and Feature Engineering Module, used for data fusion and feature engineering.

[0078] In some embodiments, data fusion includes: real-time data linking, batch processing fusion, and graph data fusion; feature engineering includes: static features, dynamic features, and graph features.

[0079] The data fusion technology in this embodiment mainly includes: Real-time data link: Database changes are captured using the CDC (ChangeDataCapture) tool, real-time data synchronization is achieved through the Kafka (an open-source stream processing platform) message queue, and second-level data fusion is completed by combining the Flink (a framework for stateful computation of unbounded and bounded data streams) stream processing engine. Batch processing fusion: Historical data is batch-integrated using ETL (Extract, Transform, Load) tools (such as DataPipeline) to build a 360° view of customers. Graph data fusion: A customer relationship network is established using a distributed graph database (such as StellarDB) to analyze the implicit relationships between business owners, legal representatives, and relatives.

[0080] The feature engineering in this embodiment mainly includes: Static features: extracting basic attributes such as customer age, occupation, and asset size. Dynamic features: constructing a consumption stability index (such as the volatility of transaction frequency in the past 3 months) and a social influence score (such as the number of financial products held by WeChat friends). Graph features: calculating the customer's centrality and clustering coefficient in the association network to identify key propagation nodes.

[0081] The real-time data link in this embodiment is the core infrastructure supporting the system's dynamic decision-making and immediate response. This link, through the collaboration of the CDC (Change Data Capture) tool, Kafka message queue, and Flink stream processing engine, completes the entire process from data change capture to second-level fusion, providing real-time data support for scenarios such as intelligent risk control and precision marketing. The following section, in conjunction with this technical feature, further explains its technical details, beneficial effects, and synergistic role with the overall system: (1) CDC tools: the “nerve endings” of database changes.

[0082] Technical principle: By parsing database logs (such as MySQL's Binlog and Oracle's Redo Log), CRUD operations are captured in real time. It supports full initialization + incremental capture mode to ensure zero data loss.

[0083] Functionality expansion: Develop a data filtering plugin to transmit only marketing-related fields (such as customer balance and counterparty), reducing invalid data transmission by more than 30%. Integrate a data masking module to dynamically mask sensitive fields (such as ID card numbers).

[0084] (2) Kafka message queue: a high-throughput "data bus". Architecture design: build a multi-topic system and partition it according to business type (such as transaction data, credit data, behavioral data). Set data retention policies (such as 7-day rolling storage) and support historical data playback.

[0085] Performance optimization: A tiered consumption model is adopted to distribute data to the Flink real-time processing cluster and the offline data warehouse. Network bandwidth usage is reduced by more than 40% through compressed transmission (Snappy algorithm).

[0086] (3) Flink stream processing engine: the “super brain” of real-time computing.

[0087] Core Functions: Status Management: Maintains customer-level status (e.g., real-time risk scores, marketing response probabilities). Complex Event Processing (CEP): Defines event patterns (e.g., "single transaction > 100,000 RMB and first occurrence within 30 days"). Window Calculation: Supports various time semantics such as scrolling windows, sliding windows, and session windows.

[0088] Integration logic implementation: Develop a UDF function library containing 200+ pre-defined rules (such as anti-money laundering rules and customer segmentation algorithms). Dynamic configuration is achieved through SQL extensions.

[0089] (4) Synergistic effect of real-time data link and other technical features.

[0090] Real-time linking complements the timeliness shortcomings of traditional batch fusion by integrating multi-source data, such as linking social media behavior data with transaction data in real time. It also supports dynamic weight adjustment, providing the weight engine with the latest data input, enabling the model to respond instantly to market changes (such as interest rate adjustments). Furthermore, it supports multi-model fusion, enabling online learning frameworks to update model parameters in real time (e.g., incremental training when daily new data volume is <5%). Finally, it facilitates personalized recommendations by dynamically adjusting recommendation strategies through real-time calculation of customer preference drift (e.g., switching from fund recommendations to insurance products).

[0091] By utilizing real-time data link technology, this invention constructs the "digital nervous system" of a financial marketing system, not only achieving a closed loop from data to decision-making but also supporting advanced functions such as dynamic pricing and real-time risk control. This link, together with technologies such as multi-model fusion and personalized recommendations, creates a synergistic effect, jointly driving the evolution of financial credit marketing towards real-time and intelligent processes.

[0092] IV. User Profile Construction and Segmentation Module, used for feature tag extraction and customer segmentation.

[0093] In some embodiments, feature tag extraction includes: basic attributes, financial attributes, and behavioral attributes; customer segmentation includes: life cycle stratification, value stratification, and risk stratification.

[0094] 1. Feature tag extraction, mainly including: Basic attributes: age, gender, occupation, marital status, etc. Financial attributes: credit score, debt ratio, historical repayment performance, etc. Behavioral attributes: APP usage frequency, consumption scenario preferences (such as education installment payments, travel loans), marketing outreach response rate, etc.

[0095] 2. Customer segmentation, mainly including: Lifecycle segmentation: New customers (registered but not granted credit), active customers (recently transacted), dormant customers (long-term inactivity), and churned customers (cancelled accounts). Value segmentation: High-net-worth customers (large credit lines + low risk), potential customers (medium income + good credit), and long-tail customers (small needs + high risk). Risk segmentation: Predicting customer delinquency probability using machine learning models (such as logistic regression and XGBoost) and classifying them into low-risk, medium-risk, and high-risk customer groups.

[0096] Specifically, in feature tag extraction, tags are data identifiers describing the characteristics of a class of objects (customers, products, materials, etc.), such as customer age, gender, preferences, and product risk level. Systematic integration to form unified tag data assets and efficient data platform services can reduce redundant development and achieve data co-construction and sharing. Simultaneously, end-to-end data timeliness governance and full lifecycle quality management help improve data quality and save storage resources. Based on a mature customer tagging system, the intelligent marketing engine has built insightful analytical capabilities such as customer segmentation, customer profiling, and customer diffusion. It can accurately and efficiently filter customized target customer groups, flexibly analyze multi-dimensional customer profiles, and build rapid trial analysis capabilities for hundreds of millions of customer groups under thousands of tags, providing decision-making references for marketing campaign planning.

[0097] Specifically, in the risk stratification model, at the algorithm level, the platform integrates advanced machine learning algorithms such as XGBoost and LightGBM, and performs customized optimizations and improvements based on business scenarios, thereby enhancing the model's accuracy and generalization ability. The model learns and predicts specific patterns in marketing data, such as the Click-Through Rate (CTR) model, which learns from customer browsing and clicking data within the app and predicts the probability of them clicking on new content. By applying differentiated underlying models to different business scenarios, intelligent applications supporting various business scenarios and operational goals can be built, effectively reducing reliance on human experience and improving the efficiency and effectiveness of automated operations.

[0098] Specifically, in the "Multi-Data Fusion Intelligent Marketing System for Financial Credit," user profiling and segmentation are the core engines of precision marketing. This system integrates structured and unstructured data, employing machine learning and graph computing technologies to construct high-dimensional, dynamic customer profiles, and then implements differentiated marketing based on segmentation strategies. The following will explain this from four dimensions: technical implementation, segmentation methods, application scenarios, and quantification of results. (1) Multidimensional data fusion layer.

[0099] Data sources: Internal data: transaction history (amount, time, counterparty), product holdings (wealth management, loans, credit cards), channel behavior (APP login, customer service interaction). External data: credit reports (overdue records, credit limits), business registration information (corporate customer equity structure), social media (public opinion sentiment analysis), geographical location (consumer preferences in business districts).

[0100] Integration Technologies: Primary Key Association: A unique customer identifier is constructed using ID card number and mobile phone number as the link, combined with device fingerprint and biometric features (authorization required). Real-time Link: Through a CDC+Kafka+Flink architecture, transaction data and behavioral data are synchronized in seconds. For example, a wealth management recommendation is triggered immediately after a customer completes a large transfer.

[0101] (2) Feature extraction and portrait generation.

[0102] Feature Engineering: Statistical Features: Transaction frequency in the past 90 days, average monthly AUM (Assets Under Management), and loan repayment timeliness rate. Graph Features: Constructing a customer-enterprise-industry knowledge graph and extracting relationships (such as guarantee chain risk and group customer identifiers). Text Features: Analyzing customer service dialogue records using NLP to identify customer emotions (such as "dissatisfaction" and "anxiety") and potential needs.

[0103] Profile Tagging System: Basic Attributes: Age, Gender, Occupation, Marital Status. Behavioral Preferences: Channel Preference (Online / Offline), Product Preference (Principal-Protected / Variable Return), Promotion Sensitivity. Risk Profile: Credit Rating, Multiple Borrowing Index, Anti-Fraud Score. Value Profile: Customer Lifetime Value (CLV), Product Cross-Selling Potential.

[0104] Dynamic update mechanisms: Real-time updates: High-frequency changing features (such as account balance and counterparty) are refreshed in seconds through Flink stream processing. Periodic updates: Low-frequency features (such as credit reports) are updated in batches daily / weekly. Event-driven updates: When a customer experiences a significant event (such as loan delinquency or large-scale consumption), a full profile recalculation is triggered.

[0105] (3) Subdivided dimension design.

[0106] Value Segmentation: RFM (Recency Frequency Monetary) Model: Customers are categorized based on their most recent purchase (Recency), purchase frequency (Frequency), and purchase amount (Monetary). CLV Segmentation: Predicts a customer's net income over the next 5 years, identifying high-value customers (Top 5%) and potential customers (Next 10%).

[0107] Demand Segmentation: Lifecycle Stages: New Clients (Account Opening < 3 Months), Growth Clients (AUM Growth > 20%), Mature Clients (Stable AUM), and Clients at Risk of Churn (AUM Declining for 3 Consecutive Months). Product Preference Clusters: Identifying groups such as "Conservative Investors" and "High-Risk Speculators" through cluster analysis (e.g., K-Means).

[0108] Risk Segmentation: Credit Risk Matrix: Combining credit scores and behavioral data, customers are categorized into "high-quality customers," "customers under observation," and "high-risk customers." Anti-Fraud Profiling: Identifying group fraud risks through device fingerprinting and transaction patterns.

[0109] (4) Implementation of the subdivision algorithm Supervised learning: Classification model: Using XGBoost to segment customers by value, the AUC value of the offline evaluation metric reached 0.89. Regression model: Predicting customer CLV (Customer Lifetime Value), with an error rate controlled within 8%.

[0110] Unsupervised Learning: Cluster Analysis: Identifying abnormal transaction patterns and discovering suspicious customer groups using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. Graph Neural Networks: Identifying community structures in customer relationship networks and uncovering implicit association risks. Reinforcement Learning, Dynamic Segmentation: Adjusting segmentation strategies in real time based on marketing response feedback (e.g., increasing discounts for certain customer groups).

[0111] V. Intelligent Marketing Strategy Formulation Module, used for personalized product recommendations, marketing outreach optimization, and automated marketing processes.

[0112] In some embodiments, personalized product recommendations include dynamic pricing and contextualized recommendations; marketing outreach optimization includes timing selection and channel matching; and automated marketing processes include a data management platform and an intelligent outbound calling system.

[0113] 1. Personalized product recommendations, mainly including: Dynamic pricing: Adjusting loan interest rates based on customer risk level and market interest rate fluctuations (e.g., high-credit customers enjoy preferential interest rates). Scenario-based recommendations: Pushing "0% interest tuition installment" products to customers with education installment needs, and recommending "business loans" to micro and small business owners.

[0114] 2. Marketing outreach optimization, mainly including: Timing selection: Pushing loan products on paydays and peak consumption periods (such as before Double Eleven) to improve conversion rates. Channel matching: Prioritizing push notifications through the app and WeChat for younger customers, and providing one-on-one service to high-net-worth clients through account managers.

[0115] 3. Automated marketing processes, mainly including: DMP (Data Management Platform): screening target customer groups (e.g., "30-40 years old + with a mortgage + no credit approval in the past 3 months"), configuring marketing rules (e.g., "successful credit approval will result in a repayment voucher"). Intelligent outbound calling system: automatically dialing customer numbers and playing personalized scripts (e.g., "Mr. Zhang, your pre-approved loan of 100,000 yuan has been credited to your account").

[0116] Specifically, in terms of user reach, the intelligent marketing platform has built diversified and precise reach channels to ensure that information can be delivered to target users efficiently and accurately. With customer permission, in addition to basic push services such as SMS and PUSH, the platform has further introduced an AI voice robot (interactive voice response) system. The addition of AI voice robots has taken the platform's services to a new level. They can simulate human conversation, conduct complex interactions, answer user questions, and even provide personalized suggestions and services, allowing users to enjoy convenience while also feeling warmth and care.

[0117] VI. Marketing Execution and Real-time Monitoring Module: This module is used for automated marketing execution and real-time performance monitoring.

[0118] In some embodiments, automated marketing execution includes: multi-channel outreach and testing of different copy; real-time performance monitoring includes: key metric tracking and anomaly alerts.

[0119] 1. Automated marketing execution, mainly including: Multi-channel outreach: Simultaneously reaching customers through channels such as SMS, app push notifications, WeChat Work, and intelligent outbound calling. A / B testing: Comparing the click-through rates of different copywriting (e.g., "Limited-time 0% interest" vs. "Flexible repayment") to optimize marketing content.

[0120] 2. Real-time performance monitoring, mainly including: Key performance indicator tracking: reach rate, connection rate, application volume, credit approval rate, and utilization rate. Anomaly alerts: If the conversion rate of a certain channel drops sharply, an investigation will be automatically triggered (such as checking whether the link is invalid or whether the wording is compliant).

[0121] Specifically, conversion metrics are data measures used to evaluate the effectiveness of marketing campaigns, such as clicks and the amount spent on financial products. By integrating and analyzing marketing data across the entire value chain, and creating a unified data asset and data platform service covering various business objectives, problems such as redundant processing of metrics and inconsistent feedback criteria can be avoided, which is conducive to improving the consistency of omnichannel marketing effectiveness evaluation.

[0122] Specifically, the platform possesses end-to-end evaluation and feedback capabilities. Based on a mature customer tagging system, the intelligent marketing engine has built insightful analytical capabilities such as customer segmentation, customer profiling, and customer diffusion. It can accurately and efficiently filter customized target customer groups, flexibly analyze multi-dimensional customer profiles, and build rapid trial and analysis capabilities for hundreds of millions of customer groups under thousands of tags, providing decision-making references for marketing campaign planning.

[0123] VII. Effectiveness Evaluation and Strategy Iteration Module, used for evaluating marketing effectiveness and optimizing strategies.

[0124] In some embodiments, marketing effectiveness evaluation includes: return on investment analysis and attribution analysis; strategy optimization includes: user feedback loop and model iteration.

[0125] 1. Marketing effectiveness evaluation, mainly including: ROI (Return on Investment) analysis: calculating cost per customer acquisition (CPA), cost per credit line (CPL), cost per loan disbursement (CPO), etc. Attribution analysis: determining the contribution of each step in the customer conversion path (e.g., "clicking an ad → registering → receiving credit → disbursement").

[0126] 2. Strategy optimization, mainly including: User feedback loop: collecting customer feedback on marketing activities (such as interest rate discounts, repayment coupons) and adjusting subsequent strategies. Model iteration: updating the risk control model (such as adding new consumption scenario features) and marketing model (such as optimizing customer segmentation logic) based on the latest data.

[0127] Through multi-data fusion and intelligent algorithms, this system achieves: Precise customer acquisition: reducing marketing costs by 30%-50% and increasing customer conversion rates by 2-3 times. Controllable risk: controlling delinquency rates below 1% through real-time risk control models. Compliant and efficient: automating the entire process from customer screening to performance evaluation while protecting data privacy, thus supporting the digital transformation of financial institutions.

[0128] In summary, this embodiment addresses the shortcomings of existing intelligent marketing systems for financial lending by solving the following technical problems: 1. Insufficient data fragmentation and integration.

[0129] Existing technical issues: Data within financial institutions is fragmented, data silos exist between departments, and the integration of external data (such as social behavior and consumption records) is insufficient, resulting in incomplete user profiles.

[0130] This embodiment's solution integrates user basic attributes, behavioral data, third-party credit information, and social network information through multi-source heterogeneous data fusion technology to construct a panoramic user profile.

[0131] 2. User profiles are static and updates are lagging.

[0132] Existing technical issues: Traditional systems use a batch update model, which cannot capture changes in user behavior in real time (such as income fluctuations and multiple borrowing), resulting in a disconnect between marketing strategies and current user needs.

[0133] This embodiment's solution employs a dynamic user profiling engine, based on time-series models (such as LSTM) and federated learning techniques, to achieve real-time updates of user credit risk, consumption preferences, and lifecycle stages. Addressing the marketing timing lag caused by the "T+1" timeliness of data such as customer behavior, model features, and performance feedback, it improves real-time data across the entire customer behavior and business conversion chain, formulates real-time marketing optimization strategies, and builds a second-level feedback optimization capability for customer behavior, enabling timely seizing of marketing opportunities and improving business conversion efficiency.

[0134] 3. The profile is too singular in its dimensions and relies too heavily on historical data.

[0135] Existing technical issues: Current systems focus only on structured data (such as credit scores and consumption records), neglect unstructured data (such as social behavior and emotional preferences), and rely too much on historical data, making it difficult to predict users' future needs.

[0136] This embodiment's solution involves constructing a multi-dimensional tagging system, combining structured and unstructured data, and using graph neural networks (GNNs) to uncover hidden risks and needs within users' social relationship chains.

[0137] 4. The contradiction between homogenized marketing strategies and user experience.

[0138] Existing technical issues: Traditional systems use a fixed marketing model (such as interest rate discounts + SMS push), lack differentiated strategies, and excessive marketing interferes with normal user operations, resulting in low engagement.

[0139] This embodiment addresses issues such as reliance on expert rules in business operations, low strategy iteration efficiency, and inability to cover long-tail traffic. The invention explores automated management strategies and builds an intelligent system spanning the entire marketing insight, campaign, and feedback process, creating a deep collaboration model between humans and machines. Business experts focus on innovating business rules and considering overall interests, ensuring customer experience and strategic direction; machines focus on rapid strategy iteration and optimization, improving operational effectiveness and market responsiveness. Based on an adaptive marketing strategy recommendation module and combined with reinforcement learning algorithms, the product recommendation logic is dynamically optimized based on user feedback, supporting differentiated outreach channels (such as app push notifications and account manager outbound calls) and dynamic pricing models.

[0140] 5. Insufficient system stability and weak anti-fraud capabilities.

[0141] Existing technical issues: Some systems experience downtime during peak periods, and anti-fraud models lag behind, making it difficult to identify new fraud methods such as organized crime.

[0142] This embodiment's solution employs a highly available architecture to ensure system stability; it deploys a behavior sequence-based anti-fraud model, combined with real-time monitoring and anomaly detection technology, to improve risk interception rates. Addressing the significant differences in business objectives and operating models across various business scenarios, it templates operational strategies for key business scenarios to promote the reuse of best practices in marketing. Simultaneously, it optimizes the campaign launch process based on specific scenarios to improve marketing operational efficiency and conversion rates.

[0143] By addressing the aforementioned technical challenges, the intelligent marketing platform provided in this embodiment, leveraging its superior project management capabilities and technological innovation, constructs a comprehensive customer operation solution for financial credit marketing. The system deeply integrates AI technology, using algorithms such as XGBoost and LightGBM to build precise user profiles, and relies on Flink's real-time data processing technology to achieve second-level response and dynamic adjustment of marketing campaigns. Its multi-channel support significantly improves reach efficiency, and combined with A / B testing functionality, it enables automated strategy optimization, resulting in a substantial increase in marketing conversion rates. Simultaneously, by building an "AI + diverse scenarios" ecosystem, the platform promotes the transformation of marketing operations from experience-driven to data-intelligent driven. This significantly improves the accuracy, efficiency, and user experience of financial credit marketing, while reducing compliance risks and operating costs.

[0144] Example 2: This embodiment provides another intelligent marketing system for financial credit, which is implemented based on the above embodiment. The focus is on describing the working principle and process of the intelligent marketing system for financial credit.

[0145] See Figure 2The diagram shows a system architecture of a financial credit intelligent marketing system. In this embodiment, the system architecture of the financial credit intelligent marketing system can be: multi-source data access layer → data fusion and preprocessing layer → intelligent marketing engine → risk assessment and decision-making layer → marketing execution layer.

[0146] The working principle of the intelligent financial credit marketing system in this embodiment can be summarized as follows: 1. Multi-source data access layer: Accesses structured data (such as central bank credit information and historical credit records) and unstructured data (such as social media sentiment and mobile behavior logs); real-time data collection and caching are achieved through Kafka message queues.

[0147] 2. Data Fusion and Preprocessing Layer: Graph Neural Networks (GNNs) are used to fuse cross-domain data, such as linking corporate legal person social relationships with credit records; AutoML (Automated Machine Learning) is used to automatically optimize data cleaning rules, improving the accuracy of missing value imputation.

[0148] 3. Intelligent Marketing Engine: Customer Segmentation Module: Based on the XGBoost-Federated learning framework, it achieves joint modeling while protecting data privacy; Strategy Generation Module: It dynamically generates marketing plans through deep reinforcement learning (DQN), and the reward function design includes both customer response rate and risk cost indicators.

[0149] 4. Risk assessment and decision-making level: Calculate customer risk scores in real time. If the score exceeds the threshold, it will trigger adjustments to the marketing strategy (such as reducing the credit limit). Adverse verification technology is used to ensure the robustness of the risk model under marketing intervention.

[0150] 5. Marketing Execution Layer: The intelligent routing engine selects the optimal outreach channels (such as APP push, SMS, AI outbound calls); marketing results are fed back to the engine in real time for strategy iteration.

[0151] Existing financial credit marketing systems generally suffer from severe data silos, crude marketing strategies, and lagging risk assessments, among other technical bottlenecks. Specifically: insufficient data fusion capabilities: traditional systems can only integrate internal structured data and cannot access external unstructured data (such as social media behavior and e-commerce transaction records), resulting in a single-dimensional customer profile; low level of marketing strategy intelligence: relying on manual rule engines or simple statistical models to generate marketing plans makes it difficult to achieve dynamic pricing and precise targeting; disconnect between risk assessment and marketing: the risk assessment module is independent of the marketing process and cannot provide real-time feedback on the impact of changes in customer credit on marketing strategies; poor system scalability: traditional architectures struggle to support the real-time processing and model iteration of massive amounts of multi-source data.

[0152] The intelligent financial credit marketing system provided in this embodiment of the invention has the following main advantages: Expanded data fusion dimensions: By integrating customer credit data, behavioral data, and social data through multi-source heterogeneous data fusion technology, the customer profile dimensions are improved, and the accuracy of risk assessment is increased; Dynamic optimization of marketing strategies: Based on a reinforcement learning-based marketing strategy generation model, the discount intensity and reach channels can be adjusted in real time according to customer responses, improving marketing conversion rates; Risk-driven marketing decisions: A closed-loop linkage mechanism between risk assessment and marketing strategies is constructed, automatically triggering conservative marketing plans for high-risk customers to reduce potential default rates; Upgraded system architecture: Adopting a distributed stream processing engine and containerized deployment, it supports millions of concurrent requests, shortening the model iteration cycle.

[0153] See also Table 1 for the improvements, technical implementation, and beneficial effects of the above-mentioned intelligent marketing system for financial credit provided in the embodiments.

[0154]

[0155] As shown in Table 1, the multi-data fusion technology in this embodiment integrates multi-source data through primary key association technology, providing comprehensive and accurate data support for marketing decisions. The real-time processing technology in this embodiment utilizes a stream processing engine to achieve real-time data analysis, ensuring the timeliness and effectiveness of marketing strategies. The intelligent decision-making technology in this embodiment achieves intelligent decision-making through machine learning models and rule engines, improving the accuracy and efficiency of marketing activities. The personalized recommendation technology in this embodiment combines user profiles and real-time behavioral data to generate personalized recommendation lists, enhancing customer experience and satisfaction.

[0156] Example 3: This embodiment provides a smart financial credit marketing method, implemented based on the above embodiments, and applied to the smart financial credit marketing system provided in the aforementioned embodiments. (See also...) Figure 3 The flowchart shown illustrates a smart marketing method for financial credit, which includes the following steps: Step S302: The multi-source data acquisition and integration module acquires internal data, external data, and third-party data; Step S304: The data preprocessing and fusion module performs data cleaning, standardization, and multi-source data fusion. Step S306: The multimodal data fusion and feature engineering module performs data fusion and feature engineering; Step S308: The user profile construction and segmentation module performs feature tag extraction and customer segmentation; Step S310: The intelligent marketing strategy formulation module performs personalized product recommendations, optimizes marketing outreach, and automates marketing processes. Step S312: The marketing execution and real-time monitoring module performs automated marketing execution and real-time performance monitoring; Step S314: The effect evaluation and strategy iteration module evaluates marketing effectiveness and optimizes strategies.

[0157] See Figure 4 The diagram illustrates a smart marketing method for financial credit, which mainly includes the following: 1. Data Acquisition: Responsible for collecting data from multiple data sources (such as internal transaction systems, external credit reporting agencies, third-party data providers, etc.).

[0158] 2. Data Processing: Clean, integrate, transform, and load the collected data to provide a high-quality dataset for subsequent analysis.

[0159] 3. Model Training: Use machine learning algorithms to train the processed data to build prediction and classification models.

[0160] 4. Decision support: Based on the output of the model training layer, provide decision support for the marketing execution layer.

[0161] 5. Marketing Execution: Based on the output of the decision support layer, execute specific marketing activities.

[0162] This invention provides a smart marketing method for financial lending, comprising multiple functional modules including data collection, data processing, model training, strategy formulation, real-time marketing, batch marketing, and execution monitoring. By integrating internal enterprise data with external third-party authorized data sources, a comprehensive, multi-dimensional user profile is formed. Machine learning algorithms such as XGBoost and LightGBM are used to deeply mine and analyze user behavior, predicting changes in user purchase intentions and needs. Simultaneously, based on Flink real-time data processing technology, rapid response and dynamic adjustment of marketing activities are achieved.

[0163] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the above-described intelligent marketing method for financial credit can be referred to the corresponding process in the foregoing embodiments, and will not be repeated here.

[0164] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0165] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A financial credit intelligent marketing system, characterized in that, The intelligent marketing system for financial credit includes: a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multimodal data fusion and feature engineering module, a user profile construction and segmentation module, an intelligent marketing strategy formulation module, a marketing execution and real-time monitoring module, and an effect evaluation and strategy iteration module. The multi-source data acquisition and integration module is used to acquire internal data, external data, and third-party data; The data preprocessing and fusion module is used for data cleaning and standardization and multi-source data fusion; The multimodal data fusion and feature engineering module is used to perform data fusion and feature engineering. The user profile building and segmentation module is used for feature tag extraction and customer segmentation; The intelligent marketing strategy formulation module is used for personalized product recommendations, marketing outreach optimization, and automated marketing processes. The marketing execution and real-time monitoring module is used for automated marketing execution and real-time performance monitoring. The effect evaluation and strategy iteration module is used to evaluate marketing effectiveness and optimize strategies.

2. The intelligent financial credit marketing system according to claim 1, characterized in that, The internal data includes: customer base data, credit behavior data, and channel interaction data; The external data includes: credit data, consumer behavior data, and social media data; The third-party data includes: data from loan assistance and loan aggregation platforms and advertising placement data.

3. The intelligent marketing system for financial credit according to claim 1, characterized in that, The data cleaning and standardization process includes: deduplication and validation, missing value handling, and data standardization. The multi-source data fusion includes: primary key association, fuzzy matching, and real-time data fusion.

4. The intelligent financial credit marketing system according to claim 3, characterized in that, The primary key association is based on the association of internal transaction data and external credit data in an identifier library. The identifiers in the identifier library include: legal identifiers, behavioral identifiers, and biometric identifiers. The association rules for the primary key association include: fuzzy matching and cross-institutional mapping. The real-time data fusion includes: data acquisition, primary key mapping, and data fusion.

5. The intelligent marketing system for financial credit according to claim 1, characterized in that, The data fusion includes: real-time data linking, batch processing fusion, and graph data fusion; The feature engineering includes: static features, dynamic features, and graph features.

6. The intelligent financial credit marketing system according to claim 1, characterized in that, The feature label extraction includes: basic attributes, financial attributes, and behavioral attributes; The customer segmentation includes: lifecycle segmentation, value segmentation, and risk segmentation.

7. The intelligent marketing system for financial credit according to claim 1, characterized in that, The personalized product recommendations include: dynamic pricing and scenario-based recommendations; The marketing outreach optimization includes: timing selection and channel matching; The automated marketing process includes a data management platform and an intelligent outbound calling system.

8. The intelligent marketing system for financial credit according to claim 1, characterized in that, The automated marketing execution includes: multi-channel outreach and testing of different copywriting; The real-time performance monitoring includes: tracking of key indicators and early warning of anomalies.

9. The intelligent marketing system for financial credit according to claim 1, characterized in that, The marketing effectiveness evaluation includes: return on investment analysis and attribution analysis; The strategy optimization includes: user feedback loop and model iteration.

10. A financial credit intelligent marketing method, characterized in that, The financial credit intelligent marketing system according to any one of claims 1-9, wherein the financial credit intelligent marketing method comprises: The multi-source data acquisition and integration module collects internal data, external data, and third-party data. The data preprocessing and fusion module performs data cleaning, standardization, and multi-source data fusion. The multimodal data fusion and feature engineering module performs data fusion and feature engineering. The user profile building and segmentation module performs feature tag extraction and customer segmentation; The intelligent marketing strategy development module provides personalized product recommendations, optimizes marketing outreach, and automates marketing processes. The marketing execution and real-time monitoring module automates marketing execution and monitors real-time results. The effectiveness evaluation and strategy iteration module evaluates marketing effectiveness and optimizes strategies.

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