The application of big data in finance has customer image application, precision marketing, risk control and operation optimization.

1, customer profiling applications

Customer profiling applications are mainly divided into individual customer profiling and corporate customer profiling. Individual customer profiles include demographic characteristics, spending power data, interest data, risk preferences, etc.; corporate customer profiles include corporate production, distribution, operations, finance, sales and customer data, and related industry chain upstream and downstream data. It is worth noting that the customer information owned by the bank is not comprehensive, based on the data owned by the bank itself is sometimes difficult to draw ideal results or even may draw the wrong conclusions.

2, precision marketing

Based on customer profiling, banks can effectively carry out precision marketing, including real-time marketing. Real-time marketing is based on the customer's real-time status to marketing, such as the customer's location at the time, the customer's most recent consumption and other information to target marketing (a customer using a credit card to purchase maternity products, can be modeled to predict the probability of pregnancy and recommend pregnant women like the business); or change of life events (change of job, change of marital status, home ownership, etc.) as a marketing opportunity.

3, risk control

Including SME loan risk assessment and fraudulent transaction identification and other means. SME loan risk assessment. Banks can analyze the risk of loans through the enterprise's production, distribution, sales, finance and other related information combined with big data mining methods to quantify the enterprise's credit limit and carry out SME loans more effectively.

Real-time fraudulent transaction identification and anti-money laundering analysis. Banks can conduct real-time transaction anti-fraud analysis using basic cardholder information, basic card information, transaction history, historical customer behavioral patterns, and ongoing behavioral patterns (such as money transfers) in combination with an intelligent rules engine (such as transferring money from an infrequent country for a unique user or conducting online transactions from an unfamiliar location).

4. Operational optimization

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

The pros and cons of big data

From ancient times to the present day, predictive analytics has been one of the most sought-after abilities, and big data prediction is the most important use of data. Today's big data forecasting is the analysis of recorded history, incorporating mathematical analytical models to predict the future and then project the outcome.

In the era of big data, we will inadvertently find ourselves in a situation where our privacy is threatened: big data technology service providers monitor people's privacy, buy things and monitor people's consumption habits, the Baidu search engine monitors people's browsing habits, dating software monitors people's interpersonal relationships, and investment products monitor people's wealth, and so on.