What are the combination points of big data analysis and finance?

The application in the banking industry is mainly manifested in two aspects: first, credit risk assessment. In the past, the bank's default risk assessment of corporate customers was mostly based on static data such as past credit data and transaction data. The big data after the integration of internal and external data resources can provide forward-looking predictions. The second is supply chain finance.

Using big data technology, banks can form a relationship map between enterprises according to their investment, holding, lending, guarantee and the relationship between shareholders and legal persons, which is conducive to enterprise analysis and risk control.

The main applications in the securities industry are: first, stock market forecasting. Big data can effectively broaden the dimension of quantitative investment data of securities companies and help them understand the market more accurately. By constructing more quantitative factors, the investment and research model will be more perfect.

The second is the stock price forecast. By collecting and analyzing structured and unstructured data of social networks such as Weibo, friends circle and professional forums, big data technology forms subjective judgment factors of the market and emotional scores of investors, thus quantifying the expectations of human factors on stock price changes. The third is intelligent investment consultant.

Intelligent investment consulting business provides online investment consulting services, and provides customers with personalized wealth management solutions with low threshold and low rate based on personalized data such as customers' risk preferences and trading behaviors and relying on big data quantitative model.

The application in the Internet finance industry is precision marketing. Big data classifies and screens customer preferences through multi-dimensional portraits of users, thus achieving the purpose of precise marketing. The second is consumer credit. Automatic scoring model, automatic approval system and collection system based on big data can reduce the default risk of consumer credit business.