Network Security Compliance - Construction of Banking Industry Data Governance Framework (2)

This "Guidelines" uses many emerging terms, which are officially cited by the regulatory authorities for the first time and appear in the supervision and management documents of banking financial institutions. We will use these terms to better understand the Guidelines.
Chief Data Officer: "Chief Data Officer" was proposed for the first time as a management position that required a qualification license from a regulatory agency. Through the establishment of a chief data officer, clarify the data governance structure of banking financial institutions, and the division of responsibilities among the board of directors, board of supervisors, and senior management. Data governance needs a soul role. The "Guidelines" propose that banks can establish a chief data officer (CDO) according to the actual situation. Regardless of whether a CDO position is established, it is undeniable that banks need a "CDO" to formulate bank data strategies, carry out data management work, and build a data culture. Without such a soul role, the development of data governance work must be messy and lack of system. Compared with the management committee or CIO to promote the bank's digital strategy, the CDO has a stronger driving force and precise focus on data.
Data culture: This is the first time that a regulatory agency has formally proposed data as a part of corporate culture construction. It is required to establish the concept and criteria that data is an important asset and that data should be true and objective. Through the construction of data culture, the value of data is recognized in the whole industry.
Rectification system: This is the first time that a regulatory agency explicitly requires the establishment of a rectification system after clearly requiring the establishment of a management system. The purpose is to require the establishment of a data quality control mechanism, comprehensively improve data quality, strengthen the responsibility of banking financial institutions for data quality, and establish and implement a data governance accountability mechanism up to the senior management.
Data value: Propose the concept of data value, effectively realize data value through data governance, and drive management with data value. Banking financial institutions are required to strengthen data applications, give full play to the value of data, and realize data-driven bank development, emphasizing that data should become an important basis for business management, especially risk management.
Data aggregation capability: By proposing data aggregation capabilities, the value of data in comprehensive risk management is further clarified. Banking financial institutions should establish a unified and centralized data management system to ensure the integrity of the overall data, ensure the integration, relevance and consistency of various data, and meet the data requirements for risk management in normal operations, stress scenarios, and crisis situations. need.
Build a data governance framework system and consolidate the governance foundation
The "Guidelines" replaced the "Good Standards for Quality Management of Banking Regulatory Statistical Data (Trial)" (hereinafter referred to as "Good Standards") issued by the banking regulatory agency in 2011, and strengthened and extended the supervision of data: strengthen management objects, and supervise data Quality to comprehensive data governance; extend the scope of management, from supervision statistics to the full life cycle of data.
According to the requirements of the "Guidelines" in terms of data governance structure, data management, data quality management, and data value realization, an overall architecture from data governance to management and application can be built: the direction of bank data application: mining data, tapping potential value "
Guidelines
" "Separately opened the fifth chapter of data value realization, requiring that "banking financial institutions should strengthen data application in risk management, business operation and internal control, realize data-driven, improve management refinement, and give full play to the value of data". These three application areas are closely related to the nature of banking finance, strategic transformation, and lean management. Banks should adhere to application orientation and problem orientation, strengthen and optimize data applications, and improve the ability to transform data assets into data services and data value.
Risk management
Based on internal and external data, the bank realizes more automated, refined and accurate risk identification and early warning, and explores the application of artificial intelligence technology in the field of risk prevention and control.
For example, in the risk management of credit business, through integrated learning (Ensemble Learning) technology, multiple big data model technologies are integrated to build a full range of customer risk identification and early warning capabilities, providing higher approval efficiency and more refined credit granting strategy; in the risk monitoring of credit card business, face recognition and comparison technology can greatly reduce the occurrence of counterfeit application cases; open and analyze text data available through the Internet, combined with the evaluation model of the internal evaluation method, Carry out more frequent risk monitoring of corporate credit risk or asset targets, changing the low sensitivity of static periodic monitoring.
business operation
In order to realize the omni-channel construction of online business development combined with offline branch transformation, many banks identify the life cycle status of customers by analyzing online and offline customer behavior characteristics, so as to take corresponding customer management measures such as drainage, marketing, activation, and retention . Through financial technology transformation, establish a first-party customer data management platform, build real-time marketing opportunity capture capabilities, and provide each customer with the best products and services at the best time and channel.
For example, in the bank's APP application, through the customer portraits of thousands of people and faces, powerful computing performance and big data prediction technology are used to provide customers with personalized and exclusive function display and push, and to discover customers' next needs. , to reduce the search cost of customers in the application. In the APP applications of some leading banks in recent years, such recommendation engines have become an important part of the bank's revenue growth.