Achieve secure data sharing among multiple parties and solve the problem of asymmetric information in Inclusive Finance

Analysis of Yi Guan ： since this year on , The government and the regulatory authorities have increased their support for Inclusive Finance , Carry out Inclusive Finance for financial institutions 、 Support small and micro enterprises 、 Services to special customer groups have been deployed to varying degrees , Information sharing 、 Digital technology 、 Key words such as inclusive coverage have been mentioned many times , The deep development of Inclusive Finance is still one of the key tasks of the banking industry in the next stage . In the face of the huge customer base of Inclusive Finance , How to get data 、 Use the data well 、 Take care of the data , Solve the financing difficulties of small and micro enterprises , Improving the risk control capability of banks' inclusive loans is one of the core objectives .

Based on the requirements of privacy protection laws and regulations , Banks have higher and higher standards for the use of data for security compliance , Government departments 、 As a highly effective data source , Due to different data standards and openness , The channel is blocked , There are real barriers to access to information , As a result, the data dimensions required for big data risk control are not comprehensive , Not enough data , It is difficult to exert intelligent approval 、 Technical advantages in intelligent risk control scenarios ; Whether the loan funds to microenterprises flow to the real economy , Often involving inter-bank 、 Cross border and other complex situations , There is also a lack of effective monitoring means to track the post loan funds , These have become the urgent problems to be solved in front of banking technology users .

Information asymmetry is still

At present, inclusive finance faces prominent problems

The credit data sources of small and micro enterprises mainly include several parts ： In addition to the credit reporting system of the people's Bank of China and the bank's own internal data , Mainly concentrated in government departments or public utilities , Other interbank financial institutions , Some of them are scattered in the core enterprises of the supply chain , And alternative data in Internet enterprises , Decentralization of data sources 、 Fragmentation makes it expensive for banks to obtain data 、 Difficulty , In addition, the whole social credit information system has not been fully opened up , Access to information is blocked , There are still organizational barriers between them 、 The problem of data islands .

in addition , Some small and micro enterprises, especially the first loan customers, have no experience in handling credit business , Not clear about bank loan business regulations and inclusive financial products , I don't know whether I have the loan qualification , It is difficult to form effective communication between the demand side and the supply side of financial services , It causes two-way asymmetry of information .

Insufficient validity of data

Increased the difficulty of data governance

In addition to missing data , The validity of data is also a difficult problem . Due to the information collection of alternative data 、 The processing mechanism is different , The source is very complicated , The accuracy of the data 、 The authenticity is relatively low , For compliance reasons, the bank , We are also more cautious about the selection and cooperation of data manufacturers ; Upstream and downstream transaction data mastered by core enterprises in the supply chain 、 The confidentiality of product data is high , In addition, the data caliber of each link of the chain is not uniform , Data quality is hard to guarantee , And there are a lot of semi-structured 、 Unstructured data , As a result, the amount of data actually available to the bank and the effectiveness of the data are insufficient , Data governance is difficult .

Solutions and suggestions ：

（1） Explore the cooperation between the bank and the government , Security compliance obtains high-quality data

Relevant laws and regulations require data security protection , It also attaches importance to the compliant development and utilization of data . The quality of government data and financial industry data is high 、 Strong effectiveness , It can help banks fully understand the information of small and micro enterprises , Yinzheng 、 Bank to bank cooperation and layout Inclusive Finance , Can form a government 、 Bank 、 guarantee 、 Insurance and other multi-party data security sharing systems , Jointly solve the data problem of small and micro enterprise loans . Privacy computing “ Available not visible ” The advantage of this process is to achieve data desensitization 、 The key to declassification , It can technically guarantee the compliance and security of data circulation , Satisfy the government 、 Industry 、 Requirements of core enterprises in the supply chain for data confidentiality .

Multi party secure computing technology can be used to enhance the application in credit business scenarios , For example, in the small and micro enterprise credit business application 、 Customer rating 、 Image privacy protection 、 Blacklist sharing 、 Loan fund flow monitoring and other key links , By deploying private computing nodes on all sides , Work together to complete task scheduling .

Apply maturity model according to Analysys analysis technology （AMC） Trend judgment , At present, privacy computing technology is in the exploratory stage , The technology application is not yet fully mature , But with the end of the proof of concept and pilot deployment , In the future, the implementation cases of privacy computing will usher in explosive growth . The introduction of multi-party data requires the deployment of a privacy computing platform , There are certain technical difficulties and scene adaptability problems , You need to combine your own business structure with IT Based on the architecture .

（2） Improve data quality , Strengthen the use of unstructured data

On the basis of strengthening data collection , Data quality needs to be improved 、 Further judgment and Governance on authenticity and compliance . The key element of data governance is to establish a unified data standard throughout the bank , Build enterprise level data capabilities , Create a data base , On the basis of data governance , Promote rapid iteration and reuse of modules , Based on big data 、 Artificial intelligence technology forms the whole process 、 Full lifecycle data governance solutions , Combined with the credit business scenario of small and micro enterprises 、 Contract text 、 Unstructured data such as business images , Parse the original data , Fusion of computer vision 、NLP、 Knowledge map technology , Unstructured data processing through content management , Realize intelligent search 、 Content security insight 、 Automated content management , Improve the availability of small and micro enterprise data , Revitalize the value of data .

（3） Improve model performance , Fully mining data value in the case of limited data

Data itself is a neutral word , Only when the value of data is combined with the business can it really play a role . Introducing multi-party data securely solves the problem of missing data , How to make good use of existing data solves the problem of data validity . In the case of limited data , Data mining 、 The construction of model becomes more important , In addition, online credit business is very important for automation 、 The requirement of timeliness is very high , It is more necessary to improve the ability of the model itself to promote business progress .

On the one hand, we can use the deep learning and knowledge mapping technology of artificial intelligence , Set through business rules 、 Model design to identify 、 The fusion 、 Analyze own data , Deeply excavate the relationship between upstream and downstream enterprises in the industrial chain , Establish the relationship view of small and micro enterprises , Change the irregular financial statements of small and micro enterprises 、 The dilemma of data unavailability ; On the other hand, we can make insight into historical data through machine learning , Analyze which data can be more effective 、 Accurately identify customers , What data is universal , What data is only applicable to specific customer groups , Derive patterns from data to guide the improvement of credit models or business strategies .

On the expansion of inclusive financial business scenarios , Integrate privacy computing technology with big data 、 Cloud computing 、 Artificial intelligence 、 Blockchain 、 Integrated use of Internet of things and other technologies , Give full play to their respective technological advantages , Ensure the accurate identification of risks in the process of joint calculation 、 Requirements for traceability of secure cloud storage and data flow processes , Rich data dimensions , Realize the separation of data ownership and data use right , Solve the problem of information asymmetry between banks and enterprises , Boost the scale expansion and high-quality coordinated development of Inclusive Finance .

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