1、STATE OF SUPTECH REPORT 2022SUPPORTED BYThe Cambridge SupTech Lab at the Cambridge Centre for Alternative Finance,the University of Cambridge Judge Business School,acceler-ates the digital transformation of financial supervision.While financial services are becoming increasingly global,digital and c

2、omplex,analogue processing and antiquated technologies in data gathering,validation,storage and analysis erode the analytical capa-bilities of supervisory agencies,who are often too late in protecting consumers from fraud and seeing signs of stress in the financial sys-tem or miss the underlying cau

3、ses.This is all happening while financial crime remains a trillion-dollar issue,and public agencies face new challenges such as the regulation and supervision of crypto assets,and monitoring environmental,social and governance(ESG)aspects of the financial industrys business.The Lab aims to meet fina

4、ncial sector supervisors needs by working with them to develop new methodologies and processes that further market oversight and empower consumers,and to deployh suptech applications that generate relevant,reliable,timely insights to inform their decisions.From research to executive education,to tec

5、hnical assistance,to crafting production-grade suptech solutions,we are committed to supporting the emergence of the suptech ecosystem and to em-powering a new generation of innovation leaders seeking to digitally transform financial supervision.We invite you to find out more atABOUT THECamSupTechLa

6、bCambridesuptechlabwww.cambridgesuptechlab.orgAuthors Simone di CastriMatt GrasserJuliet OngwaeJose Miguel MestanzaDesignMarta LoperaEmily DuongJessica AliCopy editingAlpa SomaiyaAdebola Daramola,Alexander Apostolides,Kyriakos Christofi,Philip Rowan,Yue Wu and Bryan Zhang of the Cambridge Centre for

7、 Alternative Finance(CCAF)contributed to the analysis.Suggested citation:Cambridge SupTech Lab(2022),State of SupTech Report 2022,Cambridge:Cambridge Centre for Alternative Finance(CCAF),University of Cambridge.Available atwww.cambridgesuptechlab.org/SOSThe mention of specific companies,manufacturer

8、s or software does not imply that they are endorsed or recommended by the Cambridge SupTech Lab in preference to others of a similar nature that are not mentioned.All graphics and charts can be downloaded at www.cambridgesuptechlab.org/SOSThe Cambridge SupTech Lab is supported byCAMBRIDGE SUPTECH LA

9、B TABLE OF CONTENTSEXECUTIVE SUMMARY.6SAMPLE,METHODOLOGY,AND TAXONOMY.101.1.Research methods.111.1.1.Sample of financial authorities by geography and income classification.111.1.2.Questionnaire for financial authorities on specifics of supervisory data.131.1.3.Questionnaire for suptech vendors.131.2

10、 Suptech taxonomy.151.2.1.Supervisory areas and use cases.151.2.2.Technologies and data science tools in the supervisory stack.16EVOLUTION OF THE SUPTECH LANDSCAPE.182.1.Timeline of the digital transformation of financial supervision.192.1.1.19872007:Suptech foundations.212.1.2.20082016:The global f

11、inancial crisis and the mass adoption of fintech.212.1.3.20172019:The dawn of suptech.222.1.4.2020present:Covid-19 accelerates suptech.23THE STATE OF SUPTECH .243.1.Demand:Financial authorities .253.1.1.Adoption .253.1.2.Gaps .303.1.3.Suptech generations 2.0.333.1.3.1.Data collection .363.1.3.2.Data

12、 processing.373.1.3.3.Data storage.373.1.3.4.Data analytics.383.1.3.5.Data products.383.1.4.Supervisory areas.393.1.5.Enabling factors.403.1.6.Funding.403.1.7.Governance.423.1.8.Gender.463.1.9.Outcomes.483.2.Supply:Sourcing solutions.503.2.1.Sources of suptech apps.503.2.2.The vendors business case.

13、513.2.3.Offerings by focus area.523.2.4.Funding.524|STATE OF SUPTECH REPORT 2022CHALLENGES TO UPTAKE.534.1.Challenges:financial authorities.544.1.1.Implementation.544.1.2.Data lifecycle.574.1.3.Resources.574.1.4.Infrastructure.614.2.Challenges:vendors.61CASE STUDIES.645.1.Data collection:Bank of Eng

14、land transforming data collection from the UK financial sector.655.2.Data processing:Central Bank of the Philippines API-based prudential reporting system and back-office reporting and visualisation application.695.3.Data storage:National Bank of Rwanda Electronic Data Warehouse.735.4.Data analytics

15、:Central Bank of the Netherlands outlier detection tool for AML/CFT/PF supervision.765.5.Data products:Reserve Bank of India(RBI)DAKSH.815.6.Full stack:BIS Project Ellipse,an integrated regulatory reporting and data analytics platform.82CONCLUSIONS.86Develop a suptech strategy and/or roadmap.87Build

16、 data capabilities for the supervisors of the future.88Grow a data-driven innovation culture.88Scale.89References.90Appendix 1:List of respondents .95Appendix 2:Suptech Taxonomy.102Appendix 3:Definitions.106CAMBRIDGE SUPTECH LAB EXECUTIVE SUMMARY6|STATE OF SUPTECH REPORT 2022The State of SupTech Rep

17、ort 2022 focuses at how financial authorities are developing and implementing supervisory technologies(suptech),and establishes a baseline from which to track the progress and impact of suptech adoption allowing financial authorities across the world to benchmark the progress of their suptech initia

18、tives.To facilitate more granular analyses of these macro trends,the Report introduces a novel version of the“SupTech Taxonomy”adopted by the Bank for International Settlements(BIS)(BIS 2018,BIS 2019),classifying supervisory use cases,technologies,and data science tools in a standardized and structu

19、red manner.In order to complement the analyses and to ground the findings in a practical context,the Report also provides a timeline of disruptions and innovations in supervision,and a set of six case studies of suptech applications.The Report is based on the insights that 146 financial authorities

20、shared through:A survey of 134 financial authorities from 108 jurisdictions A questionnaire on data models with 74 individual supervisors representing 46 agencies and 35 jurisdictions.The analysis also advances the understanding of the suptech marketplace from the supply side,providing critical insi

21、ghts from the nascent but rapidly growing industry of suptech vendors through in-depth qualitative research of key vendors sampled from the Cambridge SupTech Labs SupTech Marketplace and highlighting their perspectives on the business case for suptech,the primary use cases they focus on and the chal

22、lenges they face in commercializing suptech solutions.The Cambridge SupTech Lab State of SupTech Report 2022 presents insights on the current state of the digital transformation of financial supervision worldwide.The Report provides a global snapshot across several facets of suptech,including underp

23、inning digital infrastructure and technologies,supported supervisory use cases,approaches employed for developing and deploying suptech applications,and the related challenges and risks.CAMBRIDGE SUPTECH LAB|7 Suptech is happening.Most financial authorities have already engaged in suptech initiative

24、s.While suptech development is still at a nascent stage with room for growth,the survey results indicate that 71%of financial authorities are rising to the challenge as we see the adoption of suptech solutions,strategies and roadmaps increasing.Suptech efforts remain in the experimentation stage,pri

25、marily focused on improving data collection and basic analysis.Based on the classification provided by the Bank for International Settlements(BIS 2019)and revised by the Lab in this report(see chapter 3),the technologies deployed by financial supervisors mostly fall into the first or second generati

26、on of data architecture,and mainly support data collection as well as descriptive and diagnostic analytics.Most suptech use cases centre around consumer protection and prudential supervision.59%of financial authorities report their suptech applications being deployed in support of consumer protectio

27、n supervision,while 58%report their suptech applications support prudential supervision use cases.Significant challenges to suptech adoption remain to be addressed.Limitations in budget,data quality and technical skills remain the most significant barriers to implementing suptech.There is a remarkab

28、le mismatch between the experience of financial authorities and vendors when it comes to procurement,with technologies providers urging the public agencies to address legacy procurement processes.Financial authorities also express an unmet need for data teams,data sharing and data synthesis as a fou

29、ndational part of their modernization.There are significant distinctions in the state of suptech in emerging markets and developing economies(EMDEs)as compared to advanced economies(AEs).Financial authorities in AEs are early adopters of suptech,more often have sufficient digital infrastructure,more

30、 often assign dedicated suptech roles and departments,have seen more substantial internal outcomes than those in EMDEs,and seek funding primarily to grow their teams.EMDEs agecies tend to run suptech initiatives within the supervision department itself,are more interested in trainings,technical assi

31、stance,digital tools,and seek funding primarily for design and development of suptech.Financial authorities in EMDEs and in AEs face very similar challenges in the digital transformation of their supervisory process and capabilities.Agencies in EMDEs and AEs report lack of budget being the main cons

32、traint to the development and deployment of suptech.Centralised data office models to accelerate suptech development and implementation are emerging.35%of the surveyed financial authorities have a dedicated centralised office reporting to a Chief Data Officer who is either solely responsible for the

33、 suptech initiatives or works with other functions to develop and deploy suptech.Funding to accelerate the suptech market is a key area of focus.Although suptech vendors report some secondary support from grants,funding for financial authorities suptech initiatives comes primarily from the financial

34、 authorities themselves.Most suptech solutions are provided by external sources Highlights FROM THE state of suptech REPORT 20228|STATE OF SUPTECH REPORT 2022like contracted vendors and purchased off-the-shelf software,yet these vendors also report challenges in funding and an ability to deeply unde

35、rstand financial authorities prioritized needs.The top suptech challenges differ between agency types.For central banks,the challenges are primarily related to internal culture and strategic buy-in.For capital markets,securities,and investment instruments supervisors,challenges tend to be related to

36、 upgrading their existing systems and processes.For other supervisors,the uniquely prominent challenges are with IT systems.Most authorities still do not have a gender data strategy.Only 21%have a currently operating strategy,9%have one in development,while 70%report no strategy at all.Suptech is en

37、abling new supervisory use cases that would not otherwise be possible.While suptech solutions use chatbots and APIs to optimize existing processes and augment legacy tools,others are opening completely new opportunities for supervisors.The ability to ingest massive online datasets like social media

38、streams to conduct sentiment analysis,to parse online reviews to assess risks or identify fraudulent fintech apps,and to conduct real-time,on-chain analyses for digital assets supervision are just a few of many examples.Taken on the whole,these insights frame a suptech space that is relatively nasce

39、nt,but rapidly and necessarily accelerating to address the needs of supervisors in the face of novel and newly-magnified risks introduced by a financial sector that is digitalizing and generating supervisory data at an exponential rate.Addressing the needs of the ecosystem in an effective and equita

40、ble manner will require close collaboration between financial authorities,vendors,funders,educators,researchers,technologists,data scientists,and the rest of the suptech ecosystem.This inaugural annual State of SupTech Report aims to feed that conversation and support collaboration,building a baseli

41、ne against which to conduct agency and regional benchmarking,methodically tracking year-on-year trends,and a growth of a marketplace to serve the needs of supervisors,who in turn serve the interests of the billions of financial citizens of the jurisdictions they oversee.CAMBRIDGE SUPTECH LAB|9SAMPLE

42、,METHODOLOGY,AND TAXONOMY1.10|STATE OF SUPTECH REPORT 2022Three primary data sources were used to compile this report:A survey of 134 financial authorities from 108 jurisdictions A questionnaire for 74 individual supervisors(representing 46 agencies and 35 jurisdictions)on the specifics of superviso

43、ry data A questionnaire for six selected suptech vendors.In addition,the Lab complemented these resources with qualitative interviews and case studies to further develop and test hypotheses arising from the quantitative data and more deeply understand the challenges and opportunities in adopting sup

44、tech applications.Most of the data presented in this Report were collected between May and October 2022 through a global survey conducted by Cambridge SupTech Lab.The respondents include financial authorities such as central banks,securities and capital market authorities,financial conduct authoriti

45、es,and insurance regulators.Of the 134 responses,81 are from central banks,representing 60%of the total sample.92 responses were received from agencies in emerging markets and developing economies(EMDEs),representing 67%of the responses,while the remainder were from advanced economies(AEs).1.1.Resea

46、rch methods1.1.1.Sample of financial author-ities by geography and income classificationFigure 1.Geographical distribution of survey respondentsNumber of Agencies PER COUTRY123CAMBRIDGE SUPTECH LAB|11TABLE 1.Geographical distribution of respondents By regionRegionNumber of respondentsPercentage of s

47、ample by regionPercentage of jurisdictions covered within region East Asia and the Pacific2216%46%Europe and Central Asia2922%41%Latin America and theCaribbean2720%44%The Middle East andNorth Africa1410%46%North America32%100%South Asia65%63%Sub-Saharan Africa3325%48%Total134*Income and region are b

48、ased on the World Bank Country Classification.If a jurisdiction was not listed geo-graphically,its classification was based on neighboring jurisdictions.Figure 2.Breakdown of respondents by income groups(N=134)The final respondent sample is geographically diverse and representative of World Bank Cou

49、ntry income groups.Table 1 maps the 108 geographic jurisdictions of the 134 financial authorities who responded to the survey.The complete list is available in Appendix 1.Figure 2 illustrates the response distribution according to the World Banks classification by income level.The sample contains re

50、sponses from jurisdictions across all four income classifications,with 55 responses from either low or lower-middle-income jurisdictions.In some areas of the analysis,we group these categories into EMDEs(low,lower-middle and upper-middle income)and AEs(high income).12|STATE OF SUPTECH REPORT 2022eIn

51、 November 2022,we asked individual supervisors four questions on the specifics of supervisory data to further assess the state of data collection for financial supervision:1.Thematic areas:the supervisory areas for which data is collected2.Channels:the mechanisms and channels through which it is col

52、lected3.Formats:the digital format and structure of data that is collected4.Challenges:the specific challenges faced at each layer of the supervisory data lifecycle stackWe received information from 74 supervisors representing 46 agencies and 35 jurisdictions.This sample included some supervisors wh

53、ose agencies did not participate in the primary survey,whose agencies are listed in Appendix 1.1.1.3.Questionnaire for suptech vendorsTo complement the insights shared by the demand side of the suptech market and develop a deeper understanding of the broader suptech ecosystem,we also engaged directl

54、y with six suptech vendors to discuss ten questions that characterise the opportunities,challenges and other qualitative characteristics of the market.The vendors were selected from the Cambridge SupTech Labs SupTech Marketplace Vendor Database based on the following criteria:Centricity of suptech i

55、n strategic focus:While some vendors provide suptech solutions as a small part of a broader portfolio of products and services,others focus primarily on suptech solutions.For this set of interviews,we prioritised the latter.Maturity of offering:The sample prioritised vendors with a mature product or

56、 service to ensure actual experiences inform interviews of operating in the market,not hypothetical or early-stage ideas based only on pilots or experiments.Diversity of market position:The sample aimed to incorporate a range of market perspectives,including relatively new entrants(those who have on

57、ly recently adapted their mature offering to address supervisory use cases)and those who have been working with supervisors since before the inception of the word suptech.Diversity of geographies where solutions are deployed:The sample aimed to capture experiences across a range of jurisdictions to

58、avoid sample bias toward any one set of cultural norms or localised market restrictions.1.1.2.Questionnaire for financial authorities on specifics of supervisory dataCAMBRIDGE SUPTECH LAB|13Figure 3:Suptech taxonomy14|STATE OF SUPTECH REPORT 2022The 13 thematic focus areas are:Anti-Money Laundering/

59、Countering the Financing of Terrorism/Financing the Proliferation of Weapons of Mass Destruction(AML/CFT/PF)supervision:Suptech allows financial authorities to identify potentially suspicious customers or activities(for example,through customer due diligence and suspicious transactions detection)and

60、 enhances data analytics to monitor institutions compliance and AML/CFT/PF risk management(for example,assisted/automated examination,metadata analytics,and text analytics).Capital markets,securities and investments supervision:Suptech equips financial authorities to detect potential misconduct(for

61、example,insider trading,market manipulation and poor disclosure)and enhances data analytics to monitor the capital markets(for example,automated examination,peer-group/risk classification and text analytics).Securities and investments use cases focus on empowering securities commissions and other fi

62、nancial authorities with a securities mandate to augment their capabilities by generating improved data-driven insights and detecting insider trading and market manipulation.Climate/ESG risk supervision:Suptech enables financial authorities to enhance data collection and analytics to assess institut

63、ions climate and environment,social and governance(ESG)risk management(for example,green market monitoring,peer-group/risk classification and stress testing).Competition monitoring:Suptech focuses on monitoring market competition dynamics and rates and fees.Compliance assistance:Suptech makes availa

64、ble automating compliance auditing and automated guidance for compliance queries.The Cambridge SupTech Lab has developed a comprehensive classification system to consistently organise various entities namely,suptech vendors,suptech solutions and suptech diagnostics by supervisory use case(the sup in

65、 suptech)and by the technologies and data science tools used(the tech).This taxonomy is based on past efforts to map the space(BIS 2018,BIS 2019)and explicitly differentiates between the sup and the tech.This disaggregation affords a novel opportunity to systematically map the needs of supervisors,c

66、lassify the tools serving those needs and ultimately serve as an ontology for connecting the solutions to needs strategically and intentionally.It was refined and validated through desk research,review of deployed suptech applications(see the Labs SupTech Marketplace),and input from over 130 financi

67、al supervisors and leading suptech experts.The taxonomy will be periodically revised,based on internal research and external feedback,to reflect the suptech spaces dynamic nature.1.2.1.Supervisory areas and use casesThis first iteration of the taxonomy covers 13 broad supervisory categories subdivid

68、ed into 87 use cases.The structure of the classification system is hierarchical and built on a conceptual framework that groups use cases according to the activities conducted by supervisory functions within authorities.While thematic focus areas refer to policy or supervisory areas/activities,use c

69、ases refer to more specific tasks supported by identified suptech tools.1.2 Suptech taxonomyCAMBRIDGE SUPTECH LAB|15Consumer protection and market conduct supervision(now referred to as consumer protection):Suptech empowers financial authorities to enhance data collection(for example,advanced/real-t

70、ime monitoring and data consolidation)and improve data analytics to monitor consumer risks and supervise market conduct(for example,assisted/automated examination,misconduct detection,peer-group/risk classification and text analytics).In addition,these use cases also support authorities in providing

71、 consumers with virtual assistance(for example,complaints handling and credit bureau rectification).Cyber risk supervision:Suptech improves data analytics to monitor institutions compliance and cyber risk management(for example,automated examination,assessment of vulnerabilities and compliance monit

72、oring).Digital assets supervision:Suptech is deployed to supervise cryptoassets or DLT-based protocols,platforms or systems(for example,cross-jurisdictional intelligence checks and information-sharing capacity,embedded supervision and on-chain analysis).Financial inclusion:Suptech is used by financi

73、al authorities to monitor the access and use of financial services(for example,gender-based and geospatial analysis).These use cases can also collect consumer data(for example,consumer satisfaction analysis)and provide virtual assistance(for example,financial education tools).Insurance supervision:S

74、uptech serves some prudential supervision use cases that enable insurance supervisors to enhance data collection and data analytics.In addition,and covers use cases that allow insurance supervisors to provide virtual assistance to firms for procedures often required in the insurance sector(for examp

75、le,registration of intermediaries and product registration).Licensing:Suptech supports financial authorities providing virtual assistance to firms requesting a license or authorisation to operate within the regulatory perimeter(for example,automated guidance and automated processing of requests).Pay

76、ments oversight:Suptech assists financial authorities in monitoring and testing the performance of payments infrastructures,networks and systems(for example,advanced/real-time monitoring and stress testing).Prudential supervision of banks and non-bank deposit-taking institutions:(now referred to as

77、prudential supervision):allows financial authorities to enhance data collection(for example,automated reporting,automated validation and data consolidation)and data analytics for both macroprudential and microprudential supervision(for example,assisted/automated examination,peer-group/risk classific

78、ation and stress testing).The complete list of suptech use cases grouped by thematic focus area is available in Appendix 2.1.2.2.Technologies and data science tools in the supervisory stackOn the other side of the taxonomy in Figure 3 are the technologies and data science tools deployed to address a

79、uthorities challenges and realise the aspirations within the aforementioned supervisory areas and use cases.These technologies are classified by their applications within the context of the five layers of a supervisory stack(RA 2020):Data collection:This is the layer where data is gathered.It is col

80、lected from entities,including supervised financial service providers,consumers of financial technologies and the general public,into the supervisors domain.Examples of data collection mechanisms used for supervision include web portals and other 16|STATE OF SUPTECH REPORT 2022document management,ap

81、plication programming interfaces(APIs),advanced collection techniques including AI-based tools like chatbots,embedded supervision of distributed ledger technologies(DLT),and automated data gathering like web scraping and data streaming.Data processing:As the data is being gathered,it should be valid

82、ated,cleaned and consolidated using data processing tools to maximise its utility.Examples of data processing technologies in the supervisory context include integrated validation techniques like rules on APIs that send errors back to the submitting party in real time,task automation techniques like

83、 those programmed in scripting languages or recorded and replayed via robotic process automation(RPA),and advanced processing tools such as machine learning based computer vision and natural language processing models to extract structured supervisory data from less structured sources.Data storage:O

84、nce the data has been collected and processed,it needs to be stored in a manner that ensures security and ease of access across supervisory areas.Examples of storage methods for supervisory data include databases hosted and managed onsite by the financial authority itself,cloud and hybrid computing

85、technologies that introduce the benefits of virtualisation,and big data tools such as data lakes and data warehouses.Data analytics:With the data suitably stored,extracting insights can begin,a process enabled by data analytics technologies.Examples of data analytics tools used by supervisors includ

86、e descriptive and diagnostic analytics that summarise the current moment in time,predictive analytics that create statistical models from historical data to infer the most likely outcome in the future,and prescriptive analytics tools that use those predictions to recommend the most effective action

87、the financial authority can take to optimise achieving their mandates and goals.Data products:At the top of the stack are the products and interfaces that directly connect supervisors to the insights derived from the analytics.Examples of data products for financial authorities include charts and ke

88、y metrics from static reporting tools,interactive visualisations and dashboards that allow deeper exploration and combinations of data,and advanced business intelligence tools that leverage artificial intelligence(AI)to deliver alerts proactively.CAMBRIDGE SUPTECH LAB|172.EVOLUTION OF THE SUPTECH LA

89、NDSCAPE18|STATE OF SUPTECH REPORT 2022The use of technology and data science for financial supervision and market monitoring has rapidly evolved over the past two decades.2.1.Timeline of the digital transformation of financial supervisionIn part,this evolution has been a conjunctural phenomenon,a re

90、sponse to events-sometimes endogenous,other times exogenous to the financial system-that have reshaped financial supervision.Such events include international terrorism in the 2000s,major financial scandals at the beginning of the same decade and the global financial crisis in 2008,and more recently

91、 the Covid-19 pandemic.Moreover,this evolution also reflects a structural shift connected to the digitization of the financial market and the exploitation of big data by financial firms,driven by progress in technology and computing power,and their increased availability and affordability.Along with

92、 this progress comes the introduction and magnification of risks,such as cybersecurity and data privacy,which become ever more prominent with the advent of this digital era and proliferation of abundant digital financial data.In this context,financial authorities have increasingly experienced a digi

93、tal flood of supervisory data,without being able to distill more intelligence to govern the financial sector.Therefore,supervisory agencies have started to re-engineer their institutional arrangements,rescope their mandates,review their risk management frameworks,readjust their methodologies,step up

94、 their data management and governance approaches,and enhance their competencies and capabilities to further their digital transformation.Notably,the suptech era appears to be only the most recent chapter in the broader anthology of tech-enabled innovation in financial supervision.This section frames

95、 suptech in that broader context,highlighting some of the key milestones along the ongoing journey toward a suptech-augmented,responsible,and resilient approach to financial supervision.CAMBRIDGE SUPTECH LAB|19Figure 4.Timeline for the evolution of suptech 1920172008 872020The dawn of suptechSUPTECH

96、 FOUNDATIONSCOVID-19 ACCELERATES SUPTECHCovid-19 pandemic,lockdowns,and physical distancingMass adoption of fintech product and servicesPost-crisis increase of reporting requirementsCreation of BitcoinGlobal financial crisisFinancial scandals and the Sarbanes-Oxley ActBlack Monday market crashAdopti

97、on of XBRL by supervisory agencies U.S.Securities and Exchange Commisssion adopts EDGAR Emergence of the blockchain technology Foundations of API,cloud computing,big data and AI/MLWeb-based portals&automation in the regulatory data pipelineFormal adoption of the term“SupTech”The Central Bank of the

98、Philippines launches chatbot to collect com-plaints from users,and deployes an API-based applications to collect regulatory reports The National Bank of Rwanda develops a data warehouse2020201719872008THE GLOBAL FINANCIAL CRISIS AND THE MASS ADOPTION OF FINTECH20|STATE OF SUPTECH REPORT 20222.1.1.19

99、872007:Suptech foundationsAfter the Black Monday market crash of 1987,regulators and supervisors began to digitise their operations to improve transparency and risk management in the financial markets.In 1993,the United States Securities and Exchange Commission mandated electronic filing through its

100、 Electronic Data Gathering,Analysis,and Retri.BEEDS is an online application that enables firms