On the road of digital transformation of banks, how can big data not deviate from the runway ?

First and foremost, the data must be secure. The so-called security means that the data generated by the bank itself is protected from infringement of users' rights and interests and is not used for other purposes. Second, be cautious with external data

At present, some so-called big data risk control companies have loopholes in regulations in the acquisition and use of data, and seriously polluted data will only bring greater obstacles to business development, so it needs to be treated with caution. Compliant and legitimate data sources are one way to control your own risk.

Third, a more fundamental and in-depth look at the data. Some game companies are good at mining user behavior data, so there is also a very relevant game psychology to study. Although there is a big difference between financial data and game data, game companies do have their own unique characteristics in terms of player privacy protection and player demand mining, which is very worthy of consideration by banks.

Finally, be prepared before the credit market is perfected and the era of IoT finance comes。In the era of IoT finance, the amount of data will be even larger, and if the data is false at the beginning, the value of cleaning and utilization will be equivalent to zero, and even the digital transformation will be derailed.

For financial institutions, most of the data integration and business intelligence are still led by the big data team, but realizing the value of data requires the attention of the whole company, first and foremost the leadership.

Summary; Digital transformation is a very important task. In the traditional financial industry, there is a common problem of "brain-to-brain" decision-making, but once a data culture is established, these problems can be better controlled, such as when leaders make decisions based on feelings, employees can use data to persuade bosses to make more scientific decisions.

I think we need to strengthen management and reduce loopholes so that we don't have to deviate from the runway.

Big data doesn't deceive, as long as the data is real and valid, it's fine.

This thing can only be seen by the bank, how has it developed on its own? I chose the route.

It should be to follow the pace of the country's development, so that it will have some effect.

I think it's still necessary to go through all aspects of analysis, not just data.

Nowadays, big data is also divided into many types. I think it's still important to be based on big data.

Sum them all up, comprehensively analyze the future development path, and analyze the pros and cons.

I think it depends mainly on the decision-maker's decision-making

I think this depends on the bank's planning and the handling of this money, after all, there is still a gap between the circulation of money and virtual currency.

1. Understanding: The digital transformation of banks refers to the process in which banking organizations digitally transform their traditional business processes, service models and management systems to achieve informatization, intelligence and networking, improve service quality and customer experience, and promote the overall development of the banking industry. 2. Suggestions:

1) Accelerate the construction of technological infrastructure, promote technological development, update technology, and improve the level of technical services; (2) Improve management level and service performance; (3) Improve the service system and improve the level of customer service; (4) Strengthen security and ensure the security of customer information; (5) Improve product design to meet customer needs; (6) Establish a scientific management mechanism to improve management efficiency; (7) Strengthen market promotion and promote the development of the banking industry. Relevant information; Digital transformation is the use of a new generation of information technology to collect, transmit, store and analyze data in a single bit, so as to break down the data barriers between various levels, and then improve the efficiency of production, work and operation. Digital transformation of banks refers to the use of technology by banking institutions to analyze data to improve operational efficiency within the bank.

The digital transformation of banks is a high-level transformation based on digitization and digitalization, which further touches the core business of the company and aims to create a new business model. Digital transformation is the development of digital technologies and supporting capabilities to create a dynamic digital business model.

Digital transformation shows that success can only be achieved if companies redefine their business systematically, radically (or significantly and completely), not just IT, but every aspect of organizational activities, processes, business models, and employee capabilities.

On the afternoon of May 13, 2020, the official website of the National Development and Reform Commission (NDRC) released the "Digital Transformation Partnership Action" initiative. The initiative proposes to unite with all sectors of society to jointly build a joint promotion mechanism of "leading guidance, platform empowerment, leading leadership, institutional support, and multiple services", focusing on driving the digital transformation of small, medium and micro enterprises.

Under the trend of digitalization, networking, and intelligence, especially under the impact of the "platform ecology" model, many commercial banks have begun to subvert themselves and reconstruct their values. Digital transformation of banks is the use of technology by banking institutions to analyze data to improve operational efficiency within the bank.

Coase's theorem tells that as long as the right to data is clear and the transaction cost is zero or small, then no matter who owns the data, the data will eventually flow to the best and most efficient institutions, achieving Pareto optimality of resource allocation. In fact, the real data is scattered, scattered in various departments, systems, front, middle and back offices, head offices and branches, etc., due to the contradiction between the natural characteristics of scattered data sources (data rights) and the value can only be maximized under data aggregation (data value).

1. Digital transformation.

Development Background. With the continuous advancement of a new round of scientific and technological revolution and industrial transformation, the digital economy has been superimposed on the impact of the epidemic.

It has become the most dynamic, innovative and extensive economic form at present, and has become one of the core growth points of the national economy. In the context of the vigorous development of the digital economy, the banking industry continues to increase financial technology.

Invest in actively exploring the application of digital technology and strengthening digital construction. According to the China Banking and Insurance Regulatory Commission.

According to the released data, the total investment in information technology in the banking industry in 2020 reached 207.8 billion yuan, a year-on-year increase.

Second, the development characteristics.

In the process of digital transformation and development, the business model of banks presents the following characteristics:

Platformization and integration: With the development of information technology, banks integrate their one-stop financial business needs such as wealth management, investment, consumption, and payment into one system platform and mobile app

customers can handle it in one stop.

Mobile and intelligent: The transformation of banking and financial services to fully online and intelligent is accelerating, and all banks are taking mobile banking as the forefront of customer service and digital transformation, creating a mobile banking APP platform ecology, and "a mobile phone is a bank outlet".

At the same time, it comprehensively uses emerging technologies such as big data risk control, precision marketing, robo-advisory, intelligent customer service, and intelligent systems both internally and externally in the bank.

Scenario-based and ecological: In the process of digital transformation, banks are gradually co-building and sharing with multiple entities such as traditional financial institutions, scenario-based platform institutions, fintech companies, emerging Internet financial institutions, and enterprise merchants in multiple fields such as scenarios, data, and technologies, and jointly building a smart business ecosystem.

Allinpay. Based on digital marketing products, three major marketing scenarios are built to help banks build a smart marketing ecosystem and facilitate their digital transformation.

The current situation and challenges of digital transformation in the banking industry.

At present, with the rapid development of financial technology, there is a third wave of digital transformation, and technologies such as artificial intelligence, blockchain, cloud computing, and big data are widely used in the financial field.

According to relevant statistics, more than 20% of banks have already deployed in the field of emerging technologies and carried out large-scale digital transformation plans, and 85% of banks will focus on promoting digitalization. In order to move to the next stage of business competition, the vast majority of banks are actively preparing for digital transformation.

In the future, banks will use technical means to realize financial penetration services, so that financial functions can serve all areas of people's lives.

01 The current state of digital transformation in the banking industry.

Lack of awareness, mispositioning. There is no doubt that many banks already have a clear understanding of digital banking, and can build a digital transformation path that meets their own conditions in all aspects based on the current situation of the bank's internal business planning and information infrastructure. However, in addition to these, there are many banks that still have a vague understanding of digital transformation, only know its basic concepts, but do not really understand it.

Due to insufficient awareness of digital transformation, such banks often pursue the short-term benefits of digital transformation too much and lack long-term digital capability planning.

The system is aging and has poor support. At present, some banking systems are aging and isolated, and there is still a considerable gap between them and the requirements of comprehensive digital transformation. The support of the banking system is a prerequisite for achieving comprehensive digital transformation, but the current banking system is not optimistically divided.

02Opportunities for digital transformation of banking business.

Customer-centric, service-oriented. No matter how the transformation is made, the customer comes first, and the digital transformation must always focus on the customer. The key to digital transformation is to use digital technology to reconstruct the service model and serve customers in a more convenient and humanized way.

A number of major banks have added the life service function of mobile banking and opened banking services to various Internet applications, reshaping the customer relationship while reconstructing the customer service model of the bank.

Strategic planning and implementation ideas. Decision-makers in the banking industry need to deeply understand the importance of digital transformation, pay attention to data investment and long-term layout, and not just stop at business development and short-term benefits. Therefore, bank managers should take a strategic view of digital transformation and grasp the core competitiveness of the future.

At the same time, bank managers also need to use this as a starting point to promote the realization of digital transformation and establish a guarantee mechanism to ensure that the transformation proceeds smoothly.

At present, the competition within the banking industry is fierce, and external financing institutions have entered one after another. In the face of such a white-hot situation, the banking industry must plan and promote it as soon as possible to cultivate new momentum for the future. At the same time, banks should maintain their position as core financial intermediaries to ensure that financial supply meets the needs of the real economy.

With the increase of banking business forms and the increase in volume, the scale of the system has expanded rapidly, and the data information generated every day has increased geometrically, including a large number of customer data, transaction data and operation data.

This information is a huge amount of data, and has great potential value, and it is also the basis for big data applications. However, it is scattered in different locations of each central server or equipment, and it becomes more and more difficult to manage, monitor, and mine information of O&M data in a unified manner, which also makes the O&M workload more and more large.

Therefore, it is necessary to use certain means and methods to enhance the ability of data governance and comprehensive analysis, and turn passive operation and maintenance into active operation. And these are all provided by intelligent O&M AIOPS. Intelligent O&M is a new digital O&M capability and a must-have capability for digital transformation.

The essence of intelligent O&M is to improve the cognitive ability of O&M data, which is substantially improved in terms of improving O&M data governance capabilities, optimizing enterprise business digital risks, reducing O&M labor costs, and improving the influence of O&M on the business side.

Background

According to the relevant requirements for the integrity and retention period of log files in the Guidelines for Information Technology Risk Management of Commercial Banks issued by the China Banking Regulatory Commission (CBRC No. 19 2009), combined with the current guidelines of the regulatory authorities on the supervision of bank data governance, in order to improve the competitiveness of banks, complete digital transformation with high quality and speed, and transition data value to capitalization, a bank plans to build a unified operation and maintenance big data processing platform. Priority is given to logging scenarios, refined analysis capabilities, and scenario-based applications to achieve comprehensive observability and ensure the smooth and efficient operation of services.

Brief description of the solution

The O&M big data platform is built based on a distributed high-availability architecture to meet the needs of dynamic expansion of capacity with business needs. Optimize data collection methods to achieve real-time data collection of the IT environment, as well as centralized and efficient storage, query, analysis and visual display; Based on the data processing technology of stream-batch integration, global data can be queried in seconds. The built-in AI intelligent analysis engine can not only solve O&M pain points such as anomaly detection, anomaly location, and auxiliary fault location, but also conduct comprehensive health and risk analysis of the system through data modeling and insights.

In addition, the platform is very friendly to data processing operations, and realizes the processing of complex data in a low-quality way, such as the real-time response time calculation of transaction data, which needs to extract the time of request and response from the log, and then calculates and aggregates the time window according to the transaction characteristics.

This case is a successful implementation practice of intelligent O&M to help achieve comprehensive observability, and the case starts from two aspects: one is to do various types of data links and monitoring in advance, and find correlations at different levels; On the other hand, after observing the problem, it can quickly assess the impact of the problem, converge the problem and find the root cause.

In digital transformation, user-centricity is the core foundation that drives the financial industry, and the adoption of advanced O&M means (intelligent O&M) is the driving force for fiber core enterprises to move forward.