A wave of privacy computing is swee$εping the financial indu♣★>™stry.
The bank introduces external real estate data thr¶♥¥ough privacy calculation, and joi∞"ntly establishes an enterprise mid-loan eaπσrly warning monitoring model with the t$♠ ime-point loan balance, registered >λcapital and other data of the intra-b≈©βank loan enterprise, to improve♠ > the bank's risk monitorin☆ ₹g business capability; ∏ The bank uses the custom♦≠™er characteristic data of Federal Le<α≤arning and Internet companies to complete the "ε≠ joint modeling to improve →¶↑λthe accuracy of the c≠♥↑'redit card anti-fraud model; Insuran>÷ce companies use privacy computing to ₹ ←←develop more accurate c₩ β•ustomer marketing products throughΩ÷★ consumption and travel data of e-commerce a←±≥nd other companies
With the two-wheel drive of market §★₹÷demand and policies and regulations, π₹private computing is rapidly opening ₩up in the scale of commercial use in th≤ e financial industry. More€←♦ and more financial institutions ar←↓€πe deploying solutions related to 'σprivate computing based on the t÷ ≥wo core business scenarios of risk control and m≤£™arketing.
Especially this year, privacy computi↑§ng all-in-one machine has becom $e a phenomenal product in the fi☆&→nancial industry deployment plan. The priva ₩£¥cy computing all-in-one machine bu♣∏"ilt by companies such as Inspur Informa↕tion has quickly gained the favor of major fin ✔↑•ancial institutions, and has become •±the first place for financial ε₩πεinstitutions to deploy privacy comput'δ®₽ing solutions.
So why is the financial industry so urgent ¶®"about privacy computinε≤<≥g solutions? Why can privacy computing all-i§♥♠ n-one machine stand out in the financial ind♣&ustry deployment scheme? What is th"¶π÷e future trend of the privacy computing all-☆•in-one machine market?
01
Privacy computing open<±s the large-scale commercial use of financial biβ∏g data
For a long time, data security compliance and'× data flow sharing seem to be a natural contr≤adiction. It ensures data secu♦rity and compliance, and often restricts♠ data flow and sharing; Unlimited data≤∏φ> flow and sharing is o"γ★ften easy to breed various data leakage chao♣ ±•s.
Especially in the typica<♣©₽l data-intensive industry like the financial i∑ ndustry, on the one hand, the "three la☆≠αws and one code" such as the "γ✘σNetwork Security Law have increasingly stringen₹™ t compliance requirements for data securi₹★∑ty; On the other hand,★λ≥← financial institutions are eager to build a mo•♣≤re open financial ecosystem§♥ and integrate more external ≠₩data to maximize the release o©£ f data value.
Therefore, private computing for data tΩα÷εhat is "available and invisible&qu✘&α¶ot; has become the "right man" of th↑∑♠☆e financial industry, and has a™♥ssumed the responsibility for t✘≈he safe flow and sharing o←∞∞f external data in the financial©∏÷≠ industry. Today, the i∏≥♣ndustry has reached a consensus that p←≈rivate computing will be a rigid requirem§×Ωεent of the financial industry, and ∏$ ✔financial institutions will regar÷d it as the underlying c☆σ"©ore basic technology in the futur≥ε←βe.
However, private computing &÷is still in the early stage of large-scale ®α↑commercial use in the financial i&§ndustry. There are still manα™y challenges and areas for continu≈÷ ↕ous exploration in terms of engineering δα↓€issues such as compu§∑♣ting power performance, computing cos♦ts, and scenario landing.
For example, privacy computing in≠←volves many technolog¶≥σ↓y stacks, and the produσ<←ct form tends to be complex. I<δ×£n addition, the actual application env'γ∏ironment of financial institu≥™ ™tions is relatively 'complex. Many financial institutions n≥™≈£eed to spend a lot of time on environment d±♠eployment, data alignment, and other work when de ®★ploying privacy comput♠≤ing solutions. Even if the deplΩ≤☆oyment is successful, it is only in the★₽ "available" s €tage, and there is still a certain distance frεαom the "easy to use"₩↕; stage.
In addition, although many financial institutions ☆ have taken a "taste" of pr∞€ivacy computing, in some risk con ΩΩtrol and marketing scenarios, "small <∑trial", when the application sc&✘enario is relatively simple, the dat •a processing scale is small, and the perform"׶∑ance requirements have not been full↑×€$y released, there is stiφλ≤'ll a gap from large-scale commercial la★♣∞boratory testing. For exampl™¶☆e, whether the privacy computing solution is comp↔βatible with the existing softwγ€are and hardware devices of fin©↑ancial institutions; Whether©↔α it meets the requirements of fi £∞nancial industry for business stability undeΩ×r large-scale data throughput; And whether >₽the connected compliance data source me≤ε→♣ets the business need≤₩♥s of financial insti&Ωtutions.
More importantly, because privacy compαγγuting involves many technologies and enter•≥prises, the protocols and interconnection st✘←•"andards of privacy computing need to be∏≤"δ further improved. It is necess↔σ©≤ary for manufacturers in the field ♦ ¶of privacy computing to give ful®☆ l play to the ecological power and promote ×₽ the widespread implemeπ©☆ntation and application of privacy computing©γ solutions in financial institutions€±"☆ with the improvement of privacy comput↓φ∏✔ing protocols and standards.
At present, in view of the difficulties enc©β↕πountered in the deployment and application of p€§εrivacy computing solu&∏tions, the industry has generally recogni¶βzed that privacy computing all-in≠<↕-one machine is a catalyst for the large-scal®☆e commercial use of privacy computing in th ♠¥e financial industry. Privacyαε computing all-in-one machine caσ♣∏n well solve the above ch&★↓allenges and help fina↑₽¶ ncial institutions move from "usable&quo↕₽t; to "usable" when deploying and aα&πpplying privacy computing.
This year, all major financial insti↔↕tutions have taken the aim of ±δ ↑privacy computing all-in-one machine, mφ>Ω₽oving from "usable" to Ω♠ ""usable". This has also promot<≥ed the popularity of privacy computing all-in-oεπne machine in the market↕©★ and become a catalyst for the financial industry♠ ↔☆ to accelerate the deployment of priva₹φcy computing solutions.
02
Why Privacy Computing All-i♣σn-one stands out
As we all know, with the increasing↓σ popularity of private computing in the α÷σfinancial industry and other indust¶♠ries, a large number of private computing relate≠©d enterprises have been born in recent years, a∑™'nd related technologies, products and s☆☆©≤olutions are also numerous and mixed.
This year's IDC Persp₽∏≈<ective: Privacy Computing Pan α♣×orama Research report pointed out that the $↕±"current revenue scale of privacy computing©©↕₹ technology service providers<§ε∏ is generally small, with uneven techn $'γical performance, security, an¶¶ d productization capabilities, and even→↔ different in product form, c€&≈onnectivity, and vertical industry service capδ≥abilities.
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 compβ<uting all-in-one solutions to ™βsolve the problems of upwa••<rd adaptation of busines>Ωs systems and downward compatibi★lity of hardware ecology¶®"±, and become the best carri™↑δσer and choice for the large-scale comme§δ←rcialization of privacy computing tech₩>nology in the financial indu >☆♠stry.
At present, no matter Ant Group, WeB≠ •λank and other Internet financial ent ∏erprises, or ISVs such÷∞'< as Keriban, which have been roo£↓™ted in the financial industrλ♣y for many years, or even some priΩπvate computing start-ups, have launched the corr§≈esponding products and solutions for the integr₹¶ated private computing machine. Amon✔♠φg the many privacy computing all-in-∞₩one products, the privacy computing all-in-one m ✘πachine jointly built by Inspur Informati on and Keriban can be regarded as ↕≥↕a model of cooperation in the indus≤♠try, and is also regarded as £↕✔the form of privacy computing products with t≈αδhe most market prospects a∑∞nd the most close to the actual needs of u★™₹sers.
First of all, the privacy ♠✘÷€computing all-in-one machine of Keriban and Insp€ur Information is not a simple piec"₽™e of software and hardware, but is b♣♠uilt based on their r★≈espective advantages and accordin§ε>←g to the needs of finan£™cial scenarios. For example, the "Big Dλ₩≤ata Privacy Computing Lab" has been es±♣tablished by Keliban and Inspur I→≈™→nformation, which incl•×≥→udes the research of distributed machine lea→&↓rning framework and technology of federated β¶learning, the research of trus♠←ted execution environment construction te→±¶✘chnology of trusted and <∞φ¶confidential computi♣γng, and the development of financial applicationφ≈♦♥ requirements based on pr✔♥→ivacy computing.
For example, the "Big££ Data Privacy Computing Lab" ha☆¥ s made many preliminary explorations in va↑÷rious application scenarios such as banking a'•≠nd insurance, and has formed a data collabo♦ ration network around credit card, perso±←βnal loan, microenterprise, inclusive ∑•and retail customers from riββ•≠sk control to operation by taking fina ←≠ncial institution customer >§ acquisition marketing, stock custome ₹r operation, risk assessm♥ ent and other segmentat☆™☆ ion scenarios as the starting point.
In the future, relyi©£ng on the "Big Data Privacy Co↕✘mputing Lab", the practice and expl$•↓≈oration results of privacy computing in the finan ₹cial industry can be continuously≠& input into the privacy c'♥≠→omputing all-in-one solution, so t÷∑>hat the application of privacy computing in t<$φhe financial industry can become a ✘ sustainable evolution solution.
Secondly, the privacy computing all<€γ™-in-one machine built by Kelibanγ×< and Inspur Informat©∞↑ion has shielded many §₽γcomplexities from insta>♣♠₩llation and deployment to application deliver≠y, which is conducive to reducing the th>↑reshold for the use of privacy computing a'Ωγnd promoting the large-∞÷scale commercial use∞♣ of privacy computing technologλ₩©™y in the financial industry.
For example, the privacy computing ¶≠✔all-in-one machine b&≥$♥uilt by Keliban and Inspur ✘∏€Information fully takes into account the™∏ characteristics of financial scenarios, integraΩ★'÷tes different technologies, algori∏♦thms and services into a"€ comprehensive platform for differentΩ© application scenarios, trust enviroβ >nments and customer needs, which h÷®as multiple functions and full aλ ®daptation and optimization±♥♠, is simple and easy to use, conforβ♥®ms to the use logic of business modelers, a γασnd greatly reduces the difficulty oβ↓ ≈f using privacy computing; At the sa∏ <♥me time, the all-in-one m£✔♣achine also provides a §✔¶♣variety of models suc♦ h as data center type, smal♥γ₩∏l and medium-sized computing type and applicatio≥÷& n type. Users can make flexible choices accord¥"ing to their own business conditio∞≠δns.
Third, the standardization of privacy computi✘∑ng is being put on the agenda of the industry. I★↕¶n addition to giving full £∏play to their respective advantages, the privac∑y computing all-in-one machine model ×¥≠jointly built by Kelibaγσn and Inspur Information can really promote the<±ε division of labor and cooperatioπφΩn between industries, so that manufacturers ←♦Ωwho are good at algorithms ca♦≤∞₽n focus on the algorithm layer, and those w✘♥ho are good at hardware can focus on infrastruct∑₹ure, and accelerate $ the formation and improvement of ind™™ustry standards in the division of labor an☆ ™d large-scale business.
It is reported that the privacy c♣♠×≠omputing all-in-one machi→γ•ne built by Inspur Information a¥™nd Keliban has been deployed by m§λ₽σany financial institutions in the fin₩"♦ancial industry by virtue of the four adv♥≤antages of security and compliance, on¥e-stop service, containerized deployment a&&nd out-of-the-box use.
03
Ecology is the key to sust↓∏>™ainable development in the future
Relevant institutions predict that the glob€↑ε±al privacy computing market will reach U✔ ∞S $15 billion by 2024, and the size of China&#δ♦£39;s privacy computingΩ© market will be around US $15-3 billion, an♦×σd will maintain rapid growth §☆♠ in the next three years. In t©♠ ↑he financial industry, banks have accelerated ✘←δ✘the deployment of privacy computing solutions, ™₩€and the demand for insuran∑×ce to accelerate business™• development throughσ≈γ≠ privacy computing and external d ♠£ata is also growing. In addition, financial γ ♣institutions such as asset managemβ"ent and financial companies are also paying close✘₹♦φ attention to privacy computin•✘♥g.
At present, the industry "€¶™generally believes that ecol↑ <ogy is the key to the sustainable c∑¥§ommercial scale of private©÷♣ computing in the financial industry in the fut←¥ure. As we all know, the technolog∏π<y stack of privacy co>→♦mputing is complex and tπ₹γλhe technology is developing rapi≈ΩΩ∞dly. It is difficult to cont¥♣rol all technologies by only o$ φ♠ne manufacturer; In addition, the financi→↔£φal industry has a huge amou↓÷nt of data, strong business specificity, hig •±h sensitivity, high valu↕≤ e and openness. With the increase of de≠♠¥∑ployment scale, the requirements α≈for privacy computing solutions will only ¶ become higher and higher. For example, in additio"₽←÷n to high reliability, easy de₩γlivery and easy use, data processing perf•≤ ≤ormance, efficiency, interconnection a↑♦$nd security consensus of different platfλ₹orms will become hard req§β×uirements.
Therefore, it is nec±₽essary to gather various partners from industry≥© , university and research to jointly f∞≈♥orm an open and diversi≥★↔fied ecosystem and pro↑ ☆mote the continuous c$λ onnection between priv £λ★ate computing techno ÷αlogy and the needs o↑☆f the financial industryε✔®.
In fact, Inspur Information promotes the l∑→αarge-scale commercial use of priv≠₽☆ate computing through meta-br<×βεain ecosystem, which has been successfully→✔π× verified in the financial industry. T→©hrough working with Keliban and other p✘'artners, based on the meta-brain AI↕∑€Store platform, Inspur Informatioδ'÷n has greatly expanded the cooperation sσ∞₹>pace and depth of diff ₩erent types of partners, and effectively prom ✔€oted the application of privacy computing &←λin the financial industry.
In general, the spring of private com₩✔puting in the financial indu™®stry has come. As financial institutions such as §•φ→banks and insurance a∑&≠ccelerate the deployment of privacy computin →•g solutions, privacy computing te&α®γchnology is expected to be applied£₩≠§ in more financial business scenari↑>★os. The gradual popularity of privacy computi™"ng all-in-one machine↕✘×←, like a catalyst, greatly re©®λduces the technical threshold of privacy δ™∏♦computing in the financial ×πindustry, and promotes the large-★§scale commercial use of privacy computing. F±★ acing the future, as the key i"₩↔nfrastructure of the financia l industry, the privacy computing all-in-one mφ$£achine will inevitably play an ★✘★$increasingly critical role iλ↔ n the digital transformation of the fλδ→inancial industry.
