Big data and informatization will improve banks' risk and supervision capabilities, and reduce the cost of participation and the possibility of default on both sides of the transaction. Therefore, it is predicted that the successful banks in the future will be big data banks, and the banks that cannot play with big data analysis will be eliminated. Among them, big data mining technology is the core competitiveness of banks in the era of big data.
As a brand new business information processing technology, big data mining is the first large-scale application in the financial field. Its main feature is to extract, transform, analyze and model a large amount of data to extract the key data conducive to business decision-making. The rapid development of bank informatization has produced a large number of business data. Extracting valuable information from massive data to serve commercial decision making of banks is an important application field of data mining. Among them, HSBC, Citi and UBS are pioneers in the application of big data mining technology.
At present, the application of big data mining in the banking industry mainly includes risk management and customer management. On the basis of the customer's credit and business forecast, the method of big data mining is used to identify and estimate the types and causes of credit risks, so as to effectively control and reduce the occurrence of credit risks. Big data mining technology is used in every stage of the bank's customer management life cycle.
However, one of the important preconditions for the application of big data mining technology in the banking industry is that a unified central customer database must be established to improve the ability to analyze customer information. At the beginning of the analysis, all the information related to the customer is collected from the database, the transaction records are modeled, the data is analyzed, and the future behavior of the customer is predicted. The specific application is divided into five aspects:
1. Customer account information.
It is mainly to clean up the data, eliminate the inconsistencies of customer account data in the existing business system, and integrate them into the central customer information base. Each business department of the bank has a unified view of customers and can conduct relevant customer analysis, such as the number of customers, customer classification, basic needs, etc.
2. Customer transaction information.
The main task is to load all transaction data between customers and bank distribution channels, including counters, ATMs, credit cards, remittances, transfers, etc., into the central market customer information base. After the completion of this stage, banks can analyze customers' use of distribution channels and the capacity of distribution channels to understand the relationship among customers, channels and services.
3. Model evaluation.
This is to establish a profit evaluation model for each account of the customer. It needs to determine the income and amount, so it needs to load the data of the system to the central database. After this stage is completed, the bank can analyze the profit contribution from three aspects: organization, user and product. For example, banks can arrange appropriate distribution channels according to customers' profit contribution degree, simulate and predict the profit contribution degree of new products to banks, etc.
4. Optimize customer relations.
Banks should grasp customers' behavior changes in life, career and other aspects as well as external changes, and seize the opportunity to promote new products and services. This requires periodically loading the daily transaction details of the account into the central data warehouse to check the changes in customer behavior. If there is any change, the bank will take the initiative to get in touch with the customer by using the customer's purchasing tendency model, channel preference model, profit contribution model, credit and risk evaluation model, etc.
5. Risk assessment.
The object of bank risk management is mainly the risk related to assets and liabilities, so the transaction data of the business system related to assets and liabilities should be loaded into the central data warehouse; Then, the bank should analyze and calculate the gap between interest rate sensitive assets and liabilities according to different periods, so as to know the changes of the bank's capital ratio, asset and liability structure, funding position and net interest income in different periods.
With the development of big data technology, the banking industry has gradually moved to the stage of personalized service and scientific decision-making. Big data mining has strong information processing and analysis capabilities, and can provide scientific decision-making basis and technical support for banks. In today's globalization, banks must follow the trend of The Times and make full use of big data mining technology to win the future. For all banks, an efficient big data analysis platform is the key to mining the value of big data. Polaris Big Data analysis platform, committed to providing financial institutions with efficient financial big data solutions, is your trustworthy choice.
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