Shulou(Shulou.com)11/24 Report--

Guanyuan data and Yiou think tank jointly issued the 2023 White Paper on Intelligent decision-making capacity of Commercial Banks (hereinafter referred to as "White Paper"). This paper comprehensively expounds and analyzes the background, difficulty, path and practice of bank intelligent decision-making. The White Paper focuses on the problems, value and significance of bank intelligent decision-making in practical application, and is expected to provide support and decision-making basis for bank decision-makers and scientific and technological personnel in digital transformation and business intelligence construction. Follow the official account of Guanyuan data and reply to [Commercial Banks] to download the "White Paper on the Building of Intelligent decision-making capacity of Commercial Banks".

Commercial banks are born to "manage digital" industries, and their data output is at the top of all industries. In the era of explosive growth, data overload makes banks slow to make decisions, and intelligent decision-making ability has become the key to break the situation. In the continuous iterative innovation of data analysis and intelligent technology, commercial bank BI has gradually realized the transformation from traditional business intelligence to new business intelligence.

The "White Paper" elaborates on the four major BI development stages of commercial banks: "statistical statement analysis", "local self-help analysis", "comprehensive self-help analysis" and "intelligent decision-making", which provides a path for the development of banks' intelligent decision-making. Limited by the degree of data application, most banks are still in the stage of local self-help analysis. The White Paper deeply analyzes the difficulties in the construction of intelligent decision-making from the aspects of data quality, organizational culture, and digital tools, and provides the six core links of bank BI platform construction, bank BI selection index system (IPSI) and many bank BI best practices, so that more banks do not take detours and help to achieve comprehensive self-help analysis.

The way to break the situation: BI tools are an important starting point for building intelligent decision-making capabilities.

BI is not only an important tool for banks to get through the last kilometer of data application, but also an important starting point for banks to build intelligent decision-making ability.

The White Paper points out that banks need to build adaptive BI platforms according to their digital foundation and strategic positioning differentiation. Among them, state-owned banks pay attention to the construction of common basic capacity; national joint-stock banks pay attention to the improvement of overall business development efficiency, and need to build a platform for the application of tools; urban commercial banks and agricultural commercial banks pay attention to regionalized characteristic services, need to break through the construction of departmental tool platform according to the scene.

According to the White Paper, the common BI construction path of banks can be divided into two types: comprehensive construction and single point breakthrough, and its main body is divided into large banks and small and medium-sized banks.

At present, large banks have completed the construction of big data platform, with corresponding data foundation and relatively perfect data insight system, which can solve business problems to a certain extent, but the whole system is still in a state of rough management. If large banks want to further release the value of data, they need to adopt the overall construction path and promote the building of intelligent decision-making capacity from top to bottom.

For small and medium-sized banks, their scientific and technological capabilities are relatively insufficient, and the quality of data needs to be improved. Under this premise, small and medium-sized banks will face many practical problems if they want to carry out a comprehensive reform. Therefore, for small and medium-sized banks, we should start from the scene / business, focus on a single point of breakthrough, and focus on solving the decision-making problems of a certain level / line.

Difficulties in construction: data quality, organizational culture, digital tools

Centering on the continuous iterative innovation of data analysis and intelligent technology, the commercial bank BI has undergone four changes so far, realizing the transformation process from traditional business intelligence to new business intelligence. See the following figure:

At present, small and medium-sized banks are generally in the stage of transition from statistical report analysis to self-service analysis, while large banks are generally in the transition stage to comprehensive self-service analysis, only a small number of banks are carrying out intelligent decision-making pilot.

The White Paper points out that banks are faced with difficulties in promoting the construction of BI platform, such as business personnel are not easy to use, do not want to use, do not know how to use, and so on. The core reasons mainly focus on three pain points: the distortion of data quality, the division of organizational culture and the high threshold for the use of digital tools. These pain points directly limit the value of bank data.

Data quality: the "departmental system" of bank informatization has gradually accumulated dozens or hundreds of business systems from the initial stage of its establishment to the present. Each system is usually built on its own, lack of horizontal communication and overall planning, resulting in inconsistent data design standards, inconsistent caliber, different business meaning of the same data, which brings great difficulties to the later integration. This difficulty directly leads to the perennial accumulation of unstructured data and the low quality of data, which leads to the distortion of BI display and prediction results, and finally leads to the poor use of BI platform.

Organizational culture: in the traditional mode, business personnel need to put forward report requirements to the data analyst, and initiate a work order submission process to the science and technology department through the data analyst. After repeated communication with the data analyst to confirm the requirements, approve the data usage rights, and then complete the report processing to the business staff after the data analyst takes the number. This process divides the team that should be working for the same goal into three teams, while the KPI of different dimensions divides the business technology into two layers of skin, which is prone to contradictions in business coordination. The fragmentation and incoordination of this data organization culture makes the process of using BI complicated and time-consuming, so that it can not really land on the business side.

Digital tools: the low ease of use and high learning cost of traditional digital tools lead to the inability of business staff to use them. In the past, the threshold for the use of traditional reporting BI tools in banks is high, with more emphasis on technical expertise and higher skills for users. Relevant personnel need to meet their daily needs through training or additional positions of data analysts. These thresholds not only cause organizational redundancy, but also produce many contradictions in coordination, which affect the release of data value. Bank BI urgently needs to be upgraded to lightweight and agile to release data value.

The superposition of the three major pain points is to remind banks that they should upgrade their BI tools as soon as possible, from the traditional reporting BI based on local and static digital results to process-oriented, comprehensive and dynamic business results, and to lightweight, agile and easy-to-use upgrading to solve core problems such as business can not be used and do not want to use.

In order to achieve this transformation, Guanyuan data put forward an IPSI selection model in the White Paper to help banks find excellent ecological partners to improve their BI tools.

Construction guide: four dimensional standards to help banks find excellent ecological partners

At present, the bank is in an important node of the deepening of digital transformation, the accelerated process of localization and the continuous integration and development of new technologies. Guanyuan data believes that the domestic bank BI service market is highly prosperous, which is of great significance to the bank business line data cleaning, intelligent assistance, efficiency improvement, domestic environment adaptation and so on. However, there is no comprehensive evaluation system for relevant manufacturers from the perspective of comprehensive service value in the industry.

Accordingly, based on interviews with banking experts, in-depth investigation and analysis, and from the perspectives of products, services, ecology and innovation, the White Paper establishes the best partner value analysis model of commercial bank intelligent decision-making BI platform-IPSI, which systematically analyzes the comprehensive value performance of domestic banks'BI service manufacturers from four dimensions: "integration, product, support and innovation", 15 first-level indicators and 26 second-level indicators. The purpose of this paper is to enable commercial banks to make intelligent decision-making BI market more autonomous and mature.

Outlook for the Future: realizing Agile Operation and Intelligent Insight of Banks

As the leader of the financial industry, the banking industry is facing the challenge of digital information explosion and online business complexity soaring. The application of digital tools will promote data to become an important productivity and core competitiveness, and production relations will also be transformed. The culture of "everyone is a data analyst" will be integrated into the daily work of employees, and employees will change from users of data to producers of data.

The new changes enable business and technology to be deeply integrated and efficiently coordinated, and the two sides are no longer the kind of duel between Party An and Party B who raised and met the needs. Guanyuan data points out that organizational change supports decision-making layering, allowing people at different levels to get the information and support they need according to their own authority and responsibilities.

Data production relations will change, and bank-centered decision-making will also be transformed to hierarchical decision-making. People at different levels can make their own corresponding decisions. This can not only improve customer satisfaction, but also reduce the risk cost, so as to enhance the competitiveness and profitability of banks.

In addition, the application of the large model will expand the boundary of BI capabilities, and the new capabilities given by AI will also promote bank BI to completely break away from the traditional reporting system. Users do not need to design reports in advance. Users use the way of chat dialogue to query and analyze the data. As soon as the question is raised, the data chart is presented immediately.

It is expected that the White Paper can bring useful information and inspiration to more bank decision-makers and scientific and technological personnel, follow the trend of intelligent and digital times, and create a more hot and challenging new voyage of the banking industry.

Follow the official account of Guanyuan data and reply to [Commercial Banks] to download the "White Paper on the Building of Intelligent decision-making capacity of Commercial Banks".

Preview of the White Paper on Intelligent decision-making capacity Building of Commercial Banks in 2023

In order to better help bank scientific and technological personnel, decision makers and relevant professionals understand the white paper, understand how to strengthen the digital transformation of banks and the construction of business intelligence, and improve the business efficiency and customer service quality of financial institutions.

Guanyuan data, together with Yiou think tank, Bank of Beijing and Sand Dune Community, held an online press release of the "2023 White Paper on Intelligent decision-making capacity of Commercial Banks", together with industry research analysts, advanced bank practitioners, and business experts in the financial field. discuss how to help enhance the competitiveness of intelligent decision-making of commercial banks from a tripartite perspective.

Time of activity: August 29, 19:00-20:30

Venue: live online

Activity registration: follow the official account of Guanyuan data and reply [press conference] to register.

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