A wave of privacy computing is sweeping the financial industry.
The bank introduces external real estate data through privacy calculation, and jointly establishes an enterprise mid-loan early warning monitoring model with the time-point loan balance, registered capital and other data of the intra-bank loan enterprise, to improve the bank's risk monitoring business capability; The bank uses the customer characteristic data of Federal Learning and Internet companies to complete the joint modeling to improve the accuracy of the credit card anti-fraud model; Insurance companies use privacy computing to develop more accurate customer marketing products through consumption and travel data of e-commerce and other companies
With the two-wheel drive of market demand and policies and regulations, private computing is rapidly opening up in the scale of commercial use in the financial industry. More and more financial institutions are deploying solutions related to private computing based on the two core business scenarios of risk control and marketing.
Especially this year, privacy computing all-in-one machine has become a phenomenal product in the financial industry deployment plan. The privacy computing all-in-one machine built by companies such as Inspur Information has quickly gained the favor of major financial institutions, and has become the first place for financial institutions to deploy privacy computing solutions.
So why is the financial industry so urgent about privacy computing solutions? Why can privacy computing all-in-one machine stand out in the financial industry deployment scheme? What is the future trend of the privacy computing all-in-one machine market?
01
Privacy computing opens the large-scale commercial use of financial big data
For a long time, data security compliance and data flow sharing seem to be a natural contradiction. It ensures data security and compliance, and often restricts data flow and sharing; Unlimited data flow and sharing is often easy to breed various data leakage chaos.
Especially in the typical data-intensive industry like the financial industry, on the one hand, the "three laws and one code" such as the Network Security Law have increasingly stringent compliance requirements for data security; On the other hand, financial institutions are eager to build a more open financial ecosystem and integrate more external data to maximize the release of data value.
Therefore, private computing for data that is "available and invisible" has become the "right man" of the financial industry, and has assumed the responsibility for the safe flow and sharing of external data in the financial industry. Today, the industry has reached a consensus that private computing will be a rigid requirement of the financial industry, and financial institutions will regard it as the underlying core basic technology in the future.
However, private computing is still in the early stage of large-scale commercial use in the financial industry. There are still many challenges and areas for continuous exploration in terms of engineering issues such as computing power performance, computing costs, and scenario landing.
For example, privacy computing involves many technology stacks, and the product form tends to be complex. In addition, the actual application environment of financial institutions is relatively complex. Many financial institutions need to spend a lot of time on environment deployment, data alignment, and other work when deploying privacy computing solutions. Even if the deployment is successful, it is only in the "available" stage, and there is still a certain distance from the "easy to use" stage.
In addition, although many financial institutions have taken a "taste" of privacy computing, in some risk control and marketing scenarios, "small trial", when the application scenario is relatively simple, the data processing scale is small, and the performance requirements have not been fully released, there is still a gap from large-scale commercial laboratory testing. For example, whether the privacy computing solution is compatible with the existing software and hardware devices of financial institutions; Whether it meets the requirements of financial industry for business stability under large-scale data throughput; And whether the connected compliance data source meets the business needs of financial institutions.
More importantly, because privacy computing involves many technologies and enterprises, the protocols and interconnection standards of privacy computing need to be further improved. It is necessary for manufacturers in the field of privacy computing to give full play to the ecological power and promote the widespread implementation and application of privacy computing solutions in financial institutions with the improvement of privacy computing protocols and standards.
At present, in view of the difficulties encountered in the deployment and application of privacy computing solutions, the industry has generally recognized that privacy computing all-in-one machine is a catalyst for the large-scale commercial use of privacy computing in the financial industry. Privacy computing all-in-one machine can well solve the above challenges and help financial institutions move from "usable" to "usable" when deploying and applying privacy computing.
This year, all major financial institutions have taken the aim of privacy computing all-in-one machine, moving from "usable" to "usable". This has also promoted the popularity of privacy computing all-in-one machine in the market and become a catalyst for the financial industry to accelerate the deployment of privacy computing solutions.
02
Why Privacy Computing All-in-one stands out
As we all know, with the increasing popularity of private computing in the financial industry and other industries, a large number of private computing related enterprises have been born in recent years, and related technologies, products and solutions are also numerous and mixed.
This year's IDC Perspective: Privacy Computing Panorama Research report pointed out that the current revenue scale of privacy computing technology service providers is generally small, with uneven technical performance, security, and productization capabilities, and even different in product form, connectivity, and vertical industry service capabilities.
At this time, the privacy computing all-in-one machine gradually stood out in the market and won the favor of major financial institutions. Many manufacturers have launched privacy computing all-in-one solutions to solve the problems of upward adaptation of business systems and downward compatibility of hardware ecology, and become the best carrier and choice for the large-scale commercialization of privacy computing technology in the financial industry.
At present, no matter Ant Group, WeBank and other Internet financial enterprises, or ISVs such as Keriban, which have been rooted in the financial industry for many years, or even some private computing start-ups, have launched the corresponding products and solutions for the integrated private computing machine. Among the many privacy computing all-in-one products, the privacy computing all-in-one machine jointly built by Inspur Information and Keriban can be regarded as a model of cooperation in the industry, and is also regarded as the form of privacy computing products with the most market prospects and the most close to the actual needs of users.
First of all, the privacy computing all-in-one machine of Keriban and Inspur Information is not a simple piece of software and hardware, but is built based on their respective advantages and according to the needs of financial scenarios. For example, the "Big Data Privacy Computing Lab" has been established by Keliban and Inspur Information, which includes the research of distributed machine learning framework and technology of federated learning, the research of trusted execution environment construction technology of trusted and confidential computing, and the development of financial application requirements based on privacy computing.
For example, the "Big Data Privacy Computing Lab" has made many preliminary explorations in various application scenarios such as banking and insurance, and has formed a data collaboration network around credit card, personal loan, microenterprise, inclusive and retail customers from risk control to operation by taking financial institution customer acquisition marketing, stock customer operation, risk assessment and other segmentation scenarios as the starting point.
In the future, relying on the "Big Data Privacy Computing Lab", the practice and exploration results of privacy computing in the financial industry can be continuously input into the privacy computing all-in-one solution, so that the application of privacy computing in the financial industry can become a sustainable evolution solution.
Secondly, the privacy computing all-in-one machine built by Keliban and Inspur Information has shielded many complexities from installation and deployment to application delivery, which is conducive to reducing the threshold for the use of privacy computing and promoting the large-scale commercial use of privacy computing technology in the financial industry.
For example, the privacy computing all-in-one machine built by Keliban and Inspur Information fully takes into account the characteristics of financial scenarios, integrates different technologies, algorithms and services into a comprehensive platform for different application scenarios, trust environments and customer needs, which has multiple functions and full adaptation and optimization, is simple and easy to use, conforms to the use logic of business modelers, and greatly reduces the difficulty of using privacy computing; At the same time, the all-in-one machine also provides a variety of models such as data center type, small and medium-sized computing type and application type. Users can make flexible choices according to their own business conditions.
Third, the standardization of privacy computing is being put on the agenda of the industry. In addition to giving full play to their respective advantages, the privacy computing all-in-one machine model jointly built by Keliban and Inspur Information can really promote the division of labor and cooperation between industries, so that manufacturers who are good at algorithms can focus on the algorithm layer, and those who are good at hardware can focus on infrastructure, and accelerate the formation and improvement of industry standards in the division of labor and large-scale business.
It is reported that the privacy computing all-in-one machine built by Inspur Information and Keliban has been deployed by many financial institutions in the financial industry by virtue of the four advantages of security and compliance, one-stop service, containerized deployment and out-of-the-box use.
03
Ecology is the key to sustainable development in the future
Relevant institutions predict that the global privacy computing market will reach US $15 billion by 2024, and the size of China's privacy computing market will be around US $15-3 billion, and will maintain rapid growth in the next three years. In the financial industry, banks have accelerated the deployment of privacy computing solutions, and the demand for insurance to accelerate business development through privacy computing and external data is also growing. In addition, financial institutions such as asset management and financial companies are also paying close attention to privacy computing.
At present, the industry generally believes that ecology is the key to the sustainable commercial scale of private computing in the financial industry in the future. As we all know, the technology stack of privacy computing is complex and the technology is developing rapidly. It is difficult to control all technologies by only one manufacturer; In addition, the financial industry has a huge amount of data, strong business specificity, high sensitivity, high value and openness. With the increase of deployment scale, the requirements for privacy computing solutions will only become higher and higher. For example, in addition to high reliability, easy delivery and easy use, data processing performance, efficiency, interconnection and security consensus of different platforms will become hard requirements.
Therefore, it is necessary to gather various partners from industry, university and research to jointly form an open and diversified ecosystem and promote the continuous connection between private computing technology and the needs of the financial industry.
In fact, Inspur Information promotes the large-scale commercial use of private computing through meta-brain ecosystem, which has been successfully verified in the financial industry. Through working with Keliban and other partners, based on the meta-brain AIStore platform, Inspur Information has greatly expanded the cooperation space and depth of different types of partners, and effectively promoted the application of privacy computing in the financial industry.
In general, the spring of private computing in the financial industry has come. As financial institutions such as banks and insurance accelerate the deployment of privacy computing solutions, privacy computing technology is expected to be applied in more financial business scenarios. The gradual popularity of privacy computing all-in-one machine, like a catalyst, greatly reduces the technical threshold of privacy computing in the financial industry, and promotes the large-scale commercial use of privacy computing. Facing the future, as the key infrastructure of the financial industry, the privacy computing all-in-one machine will inevitably play an increasingly critical role in the digital transformation of the financial industry.
