In today's market environment, corporate credit is an important factor affecting the competitiveness and development of enterprises. Enterprise credit is not only related to the enterprise's own financing, procurement, sales and other activities, but also related to the trust and cooperation between enterprises and partners, customers, suppliers, governments and other parties. Therefore, companies need to have a clear and accurate understanding of their own and others' credit status in order to make sound strategies and decisions.

Enterprise credit report is an important tool to assess the credit status and risk of enterprises. They usually contain basic information about the enterprise, its financial situation, business conditions, legal disputes, administrative penalties, social responsibility, etc., as well as the results of the enterprise's credit rating and risk assessment. By reading and analyzing these reports, companies can understand their own and others' credit strengths and weaknesses, identify potential risks and opportunities, and provide a basis for decision-making.

However, simply reading and analyzing these reports is not enough. Because these reports are often static, historical, one-sided and limited. They do not reflect the dynamic changes in the creditworthiness of the enterprise, nor do they cover all the factors that may affect the creditworthiness of the enterprise. More importantly, they do not directly answer the questions that companies face in practical decision-making, such:

How to choose the right partner or customer?

How to determine a reasonable credit line or payment period?

How to predict and control future credit risk?

How do you improve your credit rating or reputation?

To solve these problems, companies need to use data and analytics to support their decisions. Data and analysis can help companies understand the credit status of themselves and others from multiple dimensions, multiple angles, and multiple levels, discover the laws and trends hidden behind the report, predict possible future situations and results, and provide more evidence for decision-making. More valuable and convincing basis.

Shangpu Consulting is a consulting company focusing on data and analytics services. We are committed to helping customers use data and analytics to improve the efficiency and quality of decision-making and improve corporate credit management. We have professional data scientists, analysts, consultants and other teams, as well as advanced data acquisition, processing, analysis, visualization and other technical platforms. We can provide the following services to our customers:

Data collection and integration: We can collect and integrate data related to corporate credit from multiple sources and channels, including public, private, third-party, etc., such as corporate registration information, financial statements, operating data, and legal records, Social evaluation, etc., to form a comprehensive, accurate, and real-time data set to provide a basis for subsequent analysis.

Data analysis and modeling: We can use various data analysis and modeling methods, such as descriptive analysis, correlation analysis, causal analysis, cluster analysis, classification analysis, regression analysis, predictive analysis, etc., to conduct in-depth mining and exploration of data sets, discover hidden laws and trends in data, build data models, and provide a basis for decision-making.

Data visualization and presentation: We can use various data visualization and presentation tools, such as charts, dashboards, maps, stories, etc., to display the results of data analysis and modeling to customers in an intuitive, clear and beautiful way to help customers quickly understand and grasp the meaning and value of data and provide support for decision-making.

Our services are not limited to providing the results of data and analysis, but more importantly to help customers apply these results to actual decision-making. We can provide customized consulting solutions and suggestions according to the specific needs and scenarios of customers, such:

How to choose the right partner or customer? We can use clustering analysis or classification analysis and other methods to evaluate and grade the credit of potential partners or customers according to the customer's goals and standards, and help customers select the best or most suitable objects.

How to determine a reasonable credit limit or payment term? We can use regression analysis or predictive analysis to model and predict the relationship between credit limit or payment term and credit risk or return according to the customer's risk preference and return expectation, so as to help customers formulate the optimal or most reasonable credit policy.

How to predict and control future credit risk? We can use causal analysis or predictive analysis to simulate and predict possible future credit events or losses based on customers' historical data and current conditions to help customers develop effective risk prevention and response measures.

How to improve one's own credit rating or reputation? We can use methods such as correlation analysis or causal analysis to identify and quantify the factors that affect the customer's credit rating or reputation based on the customer's credit report and social evaluation data, and help customers develop strategies and actions to improve their credit status.

To illustrate the effectiveness and value of our services, here are some examples of the customers we have served and their cases:

CustomerAIs a company engaged in international trade and needs to evaluate the credit of its overseas suppliers. We are for the customerAThe multi-dimensional data, including the supplier's registration information, financial statements, operating data, legal records, etc., were collected, and the credit evaluation and classification of suppliers were carried out by cluster analysis method. We classify suppliers into four levels of credit risk from low to high, and provide advice on credit lines and payment terms for each level. CustomerAAccording to our proposal, different credit policies are adopted for different grades of suppliers, which effectively reduces credit risk and improves trade efficiency and profitability.

CustomerBIs a company engaged in car rental and needs to conduct a credit assessment of its potential customers. We are for the customerBThe multi-dimensional data, including the customer's personal information, credit history, social evaluation, etc., were collected, and the customer's credit evaluation and classification were carried out by classification analysis method. We classify customers into five levels of credit risk from low to high, and provide advice on lease conditions and deposits for each level. CustomerBAccording to our suggestion, different leasing policies are adopted for different grades of customers, which effectively reduces credit risk and improves leasing efficiency and profitability.

CustomerCis a company engaged in financial services and needs to make credit risk forecasts for its existing customers. We are for the customerCThe multi-dimensional data, including the customer's financial situation, repayment record, behavior data, etc., are collected, and the credit risk prediction of the customer is carried out by the prediction analysis method. We predict the probability of each customer defaulting or late repayment within a certain period of time in the future, and provide each customer with corresponding risk prevention and countermeasures. CustomerCAccording to our proposal, different risk management policies have been adopted for customers with different risk levels, which has effectively prevented and controlled credit risks and improved the quality and returns of financial services.

CustomerDIt is a company engaged in e-commerce and needs to improve its credit rating and reputation. We are for the customerDCollected multi-dimensional data, including its own credit report, social evaluation, competitors, etc., and used correlation analysis and causal analysis methods to influence customers.DCredit rating and reputation factors were identified and quantified. We found the clientDCredit rating and reputation are closely related to their financial status, business status, legal disputes, social responsibility, etc., and provide corresponding improvement strategies and suggestions for each aspect. CustomerDAccording to our suggestion, we have made a comprehensive improvement to our credit status, effectively enhance our credit rating and reputation, and enhance our competitiveness in the e-commerce market.