How to analyze the risk Control Framework of big data

This article introduces how to analyze the risk control structure of big data. The content is very detailed. Interested friends can use it for reference. I hope it will be helpful to you.

Ideal risk control system:

1. Data source: usually contains equipment data, platform data, tripartite data, list database, user authorization data, etc.

2. Middle layer:

(1) user basic data: data containing identity, mobile phone, address, bank card and other identity information

(2) list data: credit score, blacklist, watch list, whitelist, etc.

(3) Communication data: call information, address book information, etc.

(4) equipment data: equipment information, app installation details, etc.

(5) E-commerce / social data: placing orders, transaction information, etc.

3. Application layer

(1) report data

(2) indicator data for different scenarios, such as anti-fraud, social interaction, risk, etc.

(3) Model data

(4) label data

(5) Intelligence data

4. Decision-making level

The decision-making layer can make decisions through the data, and there can be a product layer between this layer and the application layer, which can make accurate decisions through the output results of packaged products.

Therefore, decisions can be made differently for different business scenarios.

Examples of solutions are as follows:

This is the end of the analysis on how to carry out big data's risk control framework. I hope the above content can be helpful to everyone and learn more knowledge. If you think the article is good, you can share it for more people to see.