The applications of big data in finance include customer profiling applications, precision marketing, risk management and control, and operational optimization.

1. Customer portrait application

Customer portrait application is mainly divided into personal customer portrait and corporate customer portrait. Personal customer profiles include demographic characteristics, consumption power data, interest data, risk preferences, etc.; corporate customer profiles include the company's production, circulation, operations, finance, sales and customer data, and relevant upstream and downstream industry chain data. It is worth noting that the customer information held by banks is not comprehensive, and sometimes it is difficult to draw ideal results based on the data owned by the banks themselves, and even wrong conclusions may be drawn.

2. Precision marketing

Based on customer portraits, banks can effectively carry out precision marketing, including real-time marketing. Real-time marketing is based on the real-time status of the customer, such as the customer's current location, the customer's latest purchase and other information to carry out targeted marketing (a customer uses a credit card to purchase maternity products, the probability of pregnancy can be estimated through modeling and recommendations to pregnant women business that you like); or treat life-changing events (changing jobs, changing marital status, buying a new home, etc.) as marketing opportunities.

3. Risk management and control

Including means such as risk assessment of small and medium-sized enterprises and identification of fraudulent transactions. SME loan risk assessment. Banks can use the company's production, circulation, sales, finance and other related information combined with big data mining methods to conduct loan risk analysis, quantify the company's credit limit, and carry out loans to small and medium-sized enterprises more effectively.

Real-time fraudulent transaction identification and anti-money laundering analysis. Banks can use basic cardholder information, basic card information, transaction history, customer historical behavior patterns, ongoing behavior patterns (such as transfers), etc., combined with smart rule engines (such as transferring money from a country that does not appear frequently or for a unique user). Conduct online transactions from an unfamiliar location) for real-time anti-fraud analysis of transactions.

4. Operation optimization

Market and channel analysis and optimization. Through big data, banks can monitor the quality of different marketing channels, especially online channel promotion, to adjust and optimize cooperation channels. At the same time, you can also analyze which channels are more suitable for promoting which types of banking products or services, so as to optimize channel promotion strategies.

The pros and cons of big data

From ancient times to the present, predictive analysis capabilities have been one of the abilities that people have longed for, and big data prediction is the most important application of data. Today's big data prediction is to analyze and apply recorded historical records, integrate mathematical analysis models, predict the future and predict the results.

In the era of big data, people may inadvertently realize that their personal privacy is facing threats: big data technology service providers monitor people’s personal privacy, and shopping apps monitor people’s consumption habits. , Baidu search engine monitors people's web browsing habits, dating software monitors people's interpersonal relationships, investment and financial products monitor people's wealth, and so on.