Big data finance refers to a collection of massive unstructured data. Real-time analysis can provide internet financial institutions with all-round customer information. By analyzing and mining customer's transaction and consumption information, we can master customers' consumption habits and accurately predict customers' behaviors, so that financial institutions and financial service platforms can be targeted in marketing and risk control.

Content of big data finance: The financial service platform based on big data mainly refers to the financial services carried out by e-commerce companies with massive data. The key to big data is the ability to quickly obtain useful information from a large amount of data, or the ability to quickly realize cash from big data assets. Therefore, the information processing of big data is often based on cloud computing.

Extended data:

Disadvantages of big data finance:

1. The massive acquisition of personal information by big data leads to privacy and security problems.

With the massive data of personal location, purchasing preference, health and financial status being collected, and the financial trading habits, asset distribution and credit status being stored and analyzed in more detail, institutional investors and financial consumers can obtain financial services with lower prices and better requirements, thus improving the market's ability to allocate financial resources.

But at the same time, the information infrastructure of financial market and even the whole society will be more and more integrated and extroverted, which will bring greater risks to privacy, data security and intellectual property rights. As far as personal privacy is concerned, the privacy of big data goes far beyond the scope of conventional identity confirmation risks.

2. Big data technology cannot replace people's value judgment and logical thinking.

Big data is the product of human design. Big data tools (such as Hadoop software) can't get rid of misinterpretation, estrangement and prejudice. The correlation between data is not equal to causality, and there is also the problem of selective coverage of big data.

For example, social media is an important information source for big data analysis, but the proportion of young people and urban people is too high, and there are a large number of "robot" accounts or "semi-robot" accounts controlled by programs. wave

The StreetBump application in Houston collects data from drivers' smartphones to count potholes in urban roads, which may underestimate the situation in areas with more elderly and poor citizens. "Google Flu Trend" once overestimated the incidence of influenza in 20 12 years. This shows that relying on flawed big data may have a negative impact on government decision-making and may also aggravate social injustice.

3. Financial products and trading tools based on the development of big data challenge financial supervision.

The use of big data is changing the financial market, and the way of regulating the market needs to be changed to ensure that market participants use big data responsibly.

For example, the "flash crash" in May of 20 10 led to a sudden collapse of the Dow Jones Industrial Average, and American regulators believed that high-frequency trading led to rapid selling and more selling. A data point error in big data may lead to a "meaningless plunge".

The potential risk of regulators restricting the use of big data technology or directly interfering with its use is enormous, and industries should be encouraged to use more complex technologies or even larger data.

Baidu Encyclopedia-Big Data