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금융 사기 감지 AI: 중요 동향, 경쟁 스코어 보드, 시장 예측 2022-2027년

출판:Juniper Research(주니퍼 리서치) 출판년도:2022년11월

AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027
Juniper Research의 경쟁 스코어보드는 AI를 활용한 금융사기 탐지 및 방지 관련 벤더 17개사를 분석하고 있습니다.

가격 GBP2,990
구성 영문조사보고서

자세한 것은 여기를 참고해 주세요。

리포트목차    주문/문의    조사회사/구입안내

Juniper Research「금융 사기 감지 AI: 중요 동향, 경쟁 스코어 보드, 시장 예측 2022-2027년 – AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027」은 금융 사기의 탐지 및 방지에 있어서 인공지능(AI) 도입의 수요 촉진 요인이 되는 중요한 동향 시장을 조사해, AI가 활용되고 있는 주요 세그먼트와 향후의 과제에 대해 분석하고 있습니다.

금융 사기 감지 AI: 중요 동향, 경쟁 스코어 보드, 시장 예측 2022-2027년

이 보고서에서는 AI를 활용한 금융 사기 탐지 및 방지를 위한 플랫폼 지출, AI 감시에 의한 디지털 상거래 거래수와 룰 베이스 시스템에 의한 디지털 상거래 거래수의 비교, 금융 사기 거래 감시에 AI를 활용한 경우의 시간과 비용 저축에 대한 시장 예측도 제공합니다. 예측은 세계 8개 지역, 60개국을 대상으로 합니다.

주요 게시물

  • 시장 역학
  • 조사 결과 및 전략적 제안
  • Juniper Research의 경쟁 스코어보드: 17개 기업의 특성 및 능력 평가
    1. ACI Worldwide
    2. Cybersource
    3. Experian
    4. Featurespace
    5. Feedzai
    6. FICO
    7. GBG
    8. Kount, an Equifax Company
    9. LexisNexis Risk Solutions
    10. Microsoft
    11. NICE Actimize
    12. NuData Security
    13. Pelican
    14. Riskified
    15. SymphonyAI Sensa
    16. Temenos
    17. Vesta
  • 산업 예측
    • 제공 데이터
      • AI를 활용한 금융 사기 탐지 및 예방을 위한 플랫폼 지출
      • AI 모니터링을 통한 디지털 상거래 거래 수
      • 규칙 기반 시스템의 디지털 상거래 수량
      • 금융 사기 거래 감시를 위한 AI 활용으로 시간 절약
      • 금융 사기 거래 감시를 위한 AI 활용을 통한 비용 절감
    • 대상지역·국가(세계 8지역·60개국)
      • 북미: 캐나다, 미국
      • 라틴 아메리카: 아르헨티나, 브라질, 칠레, 콜롬비아, 에콰도르, 멕시코, 페루, 우루과이
      • 서유럽: 오스트리아, 벨기에, 덴마크, 핀란드, 프랑스, ​​독일, 그리스, 아일랜드, 이탈리아, 네덜란드, 노르웨이, 포르투갈, 스페인, 스웨덴, 스위스, 영국
      • 중동유럽: 크로아티아, 체코, 헝가리, 폴란드, 루마니아, 러시아, 터키, 우크라이나
      • 극동과 중국: 중국, 홍콩, 일본, 한국
      • 인도 아대륙: 방글라데시, 인도, 네팔, 파키스탄
      • 기타 아시아 태평양 지역: 호주, 인도네시아, 말레이시아, 뉴질랜드, 필리핀, 싱가포르, 태국, 베트남
      • 아프리카 & 중동: 알제리, 이집트, 이스라엘, 케냐, 쿠웨이트, 나이지리아, 카타르, 사우디 아라비아, 남아프리카, 아랍 에미리트

이 보고서는 다음 질문에 대한 답변으로 이어지는 정보를 제공합니다.

  1. AI에 의한 금융 사기 탐지 및 예방 시장의 총 가치는 2027년에 어떻게 될까?
  2. AI가 금융 사기 방지 사용에 대한 설명 가능성의 중요성은 무엇입니까? 그리고 어떻게 원활하게 진행됩니까?
  3. AI가 금융 사기에 얼마나 영향을 미치는가?
  4. AI를 통한 금융 사기 감지 시장에서 공급업체에게 가장 큰 비즈니스 기회는 어디에 있습니까?
  5. AI를 통한 금융 사기 감지 플랫폼의 주요 공급업체는?

Report Overview

Juniper Research’s new AI in Financial Fraud Detection research report provides a highly detailed analysis of this rapidly growing market. The report assesses key trends driving the need for AI implementation within financial fraud detection and prevention, the key segments where AI is being used, and challenges for future use of AI. It also analyses 17 leading AI in financial fraud detection and prevention vendors via the Juniper Research Competitor Leaderboard.

The research also provides industry benchmark forecasts for the market; covering spend on AI-enabled financial fraud detection and prevention platforms, as well as the number of digital commerce transactions screened by AI versus rules-based systems, and the time and cost savings from the use of AI in financial fraud transaction monitoring. This data is split by our 8 key regions and 60 countries.

This research suite comprises:

  • Strategy & Forecasts (PDF)
  • 5-year Market Sizing & Forecast Spreadsheet (Excel)
  • 12 months’ access to harvest Online Data Platform
Key Market Statistics
Market Size in 2022: $6.5bn
Market Size in 2027: $10bn
2022 to 2027 Market Growth: 57%

KEY FEATURES

  • Market Dynamics: Detailed assessment of how different trends are leading to greater adoption of AI and machine learning within the financial fraud detection and prevention space, such as the need for greater scalability, increases in digital transactions, and ongoing fraudster innovation.
  • Key Takeaways and Strategic Recommendations: This provides actionable recommendations and vital key takeaways, allowing vendors in this market to refine their strategies.
  • Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 17 AI in financial fraud detection and prevention vendors:
    • ACI Worldwide
    • Cybersource
    • Experian
    • Featurespace
    • Feedzai
    • FICO
    • GBG
    • Kount, an Equifax Company
    • LexisNexis Risk Solutions
    • Microsoft
    • NICE Actimize
    • NuData Security
    • Pelican
    • Riskified
    • SymphonyAI Sensa
    • Temenos
    • Vesta
  • Benchmark Industry Forecasts: 5-year forecasts for the spend on AI-enabled financial fraud detection and prevention platforms, as well as the number of digital commerce transactions screened by AI versus rules-based systems, and the time and cost savings from the use of AI in financial fraud transaction monitoring. Data is also split by our 8 key regions and the 60 countries listed below:
    • North America:
      • Canada, US
    • Latin America:
      • Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Uruguay
    • West Europe:
      • Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK
    • Central & East Europe:
      • Croatia, Czech Republic, Hungary, Poland, Romania, Russia, Turkey, Ukraine
    • Far East & China:
      • China, Hong Kong, Japan, South Korea
    • Indian Subcontinent:
      • Bangladesh, India, Nepal, Pakistan
    • Rest of Asia Pacific:
      • Australia, Indonesia, Malaysia, New Zealand, Philippines, Singapore, Thailand, Vietnam
    • Africa & Middle East
      • Algeria, Egypt, Israel, Kenya, Kuwait, Nigeria, Qatar, Saudi Arabia, South Africa, United Arab Emirates

KEY QUESTIONS ANSWERED

  1. What will the total value of the AI financial fraud detection and prevention market be in 2027?
  2. How important is explainability where AI is used to prevent financial fraud, and how can this be facilitated?
  3. How will greater AI use impact financial fraud?
  4. Where are the biggest opportunities for vendors in the AI financial fraud detection market?
  5. Who are the leading vendors of AI financial fraud detection platforms?

COMPANIES REFERENCED

Included in the Juniper Research Competitor Leaderboard: ACI Worldwide, Cybersource, Experian, Featurespace, Feedzai, FICO, GBG, Kount, an Equifax Company, LexisNexis Risk Solutions, Microsoft, NICE Actimize, NuData Security, Pelican, Riskified, SymphonyAI Sensa, Temenos, Vesta.

Mentioned: Accertify, Accuity, Acuris, Adidas, Air Europa, Aldo, Alipay, Amadeus, AT&T, Auchan, Azul Systems, Banca Sella, Barclaycard, Betfair , BioCatch, BlueSnap, BNP Paribas, BNY Mellon, Braintree, Bukalapak, Bvaccel, Canada Goose, Capgemini, CARDNET, Cayan, CellPoint Digital, Chargebacks911, Checkout.com, Citrus Pay, Cloudera, Coneta, Coop, Credorax, CSI, Data Robot, Datastax, Deloitte, Diebold Nixdorf, Discover, eBay, EgyptAir, Elevon, Emailage, Entersekt, Equifax, Ethoca, Etisalat, Eversheds, Evo Payments, Eway, Experian, FedNow, Finxact, First Data, Fiserv, FreedomPay, Gemalto/Thales, General Insurance , GPG (Global Payroll Gateway), Hay, HP, HSBC, IBM, ID R&D, IDology, ING, Innovalor, Invation, iovation, Jack Henry & Associates, JPMorgan Chase, Karlsgate, Last Minute, Lego, Linktera, Magneto, Mastercard, Mattel, Moku, NASDAQ, NetSuite, NorthRow, OpenWrks, Oracle, Oracle Commerce, PassFort, PayPal, Pilot Flying J, Plaid, PLDT, Prada, Protiviti, Red Hat, RELX, Revelock, Ring, RSA, Sage, Salesforce, Santander Bank, SAP, Sayari Labs, Sekura, SEON, Shopify, Singapore Airlines, Sionic, Socure, Solarisbank, Sparkling Logic, SPhonic, State Bank of India, Stripe, Stuzo, Swedbank, TCH, TCS (Tata Consultancy Services), Telcel, ThreatMetrix, T‑Mobile, TransUnion, UBS, UnionPay, United Colours of Benneton, Venmo, VeriFone, Visa, Visualsoft, Wells Fargo, Wendy’s, Westpac, Whitepages Pro, Wish , Zelle, Zilch, Zooz.

DATA & INTERACTIVE FORECAST

Key Market Forecast Splits

The AI in Financial Fraud Detection forecast suite provides data splits for the following metrics:

  • Spend on AI-enabled financial fraud detection and prevention platforms
  • The number of digital commerce transactions screened by AI-enabled systems
  • The number of digital commerce transactions screened by purely rules-based systems
  • Time savings from the use of AI in financial fraud transaction monitoring
  • Cost savings from the use of AI in financial fraud transaction monitoring

Geographical splits: 60 countries
Number of tables: 23 tables
Number of datapoints: Over 10,400 datapoints

harvest: Our online data platform, harvest, contains the very latest market data and is updated throughout the year. This is a fully featured platform; enabling clients to better understand key data trends and manipulate charts and tables, overlaying different forecasts within the one chart – using the comparison tool. Empower your business with our market intelligence centre, and get alerted whenever your data is updated.

Interactive Excels (IFxl): Our IFxl tool enables clients to manipulate both forecast data and charts, within an Excel environment, to test their own assumptions using the interactive scenario tool and compare selected markets side by side in customised charts and tables. IFxls greatly increase a clients’ ability to both understand a particular market and to integrate their own views into the model.

FORECAST SUMMARY

The global business spend on AI-enabled financial fraud detection and prevention platforms will exceed $10 billion globally in 2027; rising from just over $6.5 billion in 2022.

  • Growing at 57% over the period, we predict that as fraudsters become more sophisticated in their attacks, merchants and issuers will become more adept at utilising highly advanced AI-enabled fraud detection methods to combat crime. The ability of AI to recognise fraudulent payment trends at scale is critical to provide improved fraud prevention.
  • Cost savings from AI deployment will be critical to taking system use beyond regulatory compliance and providing a genuine return on investment on fraud prevention services, with improving models and greater data access creating a virtuous circle of improvement.
  • We forecast growth of 285%, with cost savings reaching $10.4 billion globally in 2027, from $2.7 billion in 2022.
  • By leveraging AI, businesses can shift their fraud management resource to where it matters, investigating the key issues, rather than dealing with endless false positives, boosting efficiency.
  • Additionally, AI is increasingly standard within financial fraud prevention services; making differentiation a challenge. Therefore, vendors should focus on access to transaction and trends data, as gaining the best level of network intelligence will allow businesses to benefit from fraud information from beyond just their own transactions, significantly improving fraud prevention. Vendors should make partnerships with third parties, such as credit bureaus and payment networks, to improve their data coverage.

보고서 구성 및 가격표

  • 시장 동향, 전략, 예측 보고서(PDF)
    Market trends, strategies and forecasts report (pdf)
  • 시장 데이터 및 예측(Excel)
    Market data & forecasts – All topic data and interactivity (xls)
  • 최신 데이터에 대한 12개월 액세스
    harvest market data platform (12 months’ online access)
GBP 2,990

[보도 자료]
AI-ENABLED FINANCIAL FRAUD DETECTION SPEND TO EXCEED $10 BILLION BY 2027, AS BUSINESSES SEEK TO COMBAT INCREASINGLY SOPHISTICATED FRAUDULENT ATTACKS

Hampshire, UK – 21st November 2022: A new study from Juniper Research has found the global business spend on AI-enabled financial fraud detection and prevention strategy platforms will exceed $10 billion globally in 2027; rising from just over $6.5 billion in 2022.

Growing at 57% over the period, the report predicts that as fraudsters become more sophisticated in their attacks, merchants and issuers will become more adept at utilising highly advanced AI-enabled fraud detection methods to combat crime. The report identified the ability of AI to recognise fraudulent payment trends at scale as being critical to provide improved fraud prevention.

AI-enabled fraud detection and prevention market platforms use AI to monitor transactions and identify fraudulent transaction patterns; reducing fraud risks by blocking transactions in real-time.

Cost Savings to Drive AI Use

The research analysis predicts cost savings from AI deployment will be critical to taking system use beyond regulatory compliance. Providing a genuine return on investment on fraud prevention services, with improving models and greater data access creating a virtuous circle of improvement.

It forecast growth of 285%, with cost savings reaching $10.4 billion globally in 2027, from $2.7 billion in 2022.

Research author Nick Maynard explained further: “By leveraging AI, businesses can shift their fraud management resource to where it matters, investigating the key issues, rather than dealing with endless false positives, boosting efficiency.”

Differentiation Key to Success for Vendors

Additionally, the fraud detection report found that AI is increasingly standard within financial fraud prevention services; making differentiation a challenge. The research recommends vendors focus on access to transaction and trends data, as gaining the best level of network intelligence will allow businesses to benefit from fraud information from beyond just their own transactions; significantly improving fraud prevention. The research recommends vendors make partnerships with third parties, such as credit bureaus and payment networks, to improve data coverage.

Juniper Research provides research and analytical services to the global hi-tech communications sector; providing consultancy, analyst reports, and industry commentary.


목차

1. AI in Financial Fraud Detection – Key Takeaways & Strategic Recommendations

1.1 Key Takeaways ………………6
1.2 Strategic Recommendations …………….7

2. AI in Financial Fraud Detection – Market Landscape

2.1 Introduction & Definition…………….9
Figure 2.1: AI Skills in Fintech……………9
Figure 2.2: Types of AI……………… 10
2.2 Why AI?………………..10
2.2.1 Scale……………….10
Figure 2.3: Total Transaction Value of eCommerce Fraud ($m), Split by 8 Key Regions, 2022-2027 ……………… 11
2.2.2 Speed………………..11
2.2.3 Pattern Recognition …………..11
2.2.4 AI versus Rules Based…………..11
Figure 2.4: Typical Rules-based Fraud Screening Process ……. 12
Figure 2.5: Typical AI-enabled Fraud Screening Process……. 13
2.2.5 The Importance of Data ………….14
2.3 Online Payment Fraud & the Fraud Prevention Market ……..14
2.3.1 Types of Fraud……………..14
2.3.2 Key Fraud Trends ……………16
2.3.3 Different Types of Fraud Detection & Prevention Systems ……19
i. Merchant/eCommerce Focused ……….19
ii. Issuer Focused……………..19
iii. General Platforms …………….19
iv. Identity-focused Platforms …………. 20

3. AI in Financial Fraud Detection – Competitor Leaderboard

3.1 Why Read This Section …………….. 22
Table 3.1: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors Included & Product Portfolio ……..23
Figure 3.2: Juniper Research Competitor Leaderboard for AI in Fraud Detection & Prevention Vendors …………….24
Table 3.3: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors & Positioning…………..24
Table 3.4: Juniper Research Leaderboard Heatmap: AI in Fraud Detection & Prevention Vendors …………….25
3.2 AI in Fraud Detection & Prevention – Vendor Profiles…….. 26
3.2.1 ACI Worldwide…………….. 26
i. Corporate Information…………. 26
Table 3.5: ACI Worldwide’s Financial Snapshot ($m), 2019-2021 ….26
ii. Geographical Spread …………… 26
iii. Key Clients & Strategic Partnerships ……… 26
iv. High-level View of Offerings………… 27
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities ……………. 27
3.2.2 Cybersource …………….. 27
i. Corporate Information…………. 27
ii. Geographic Spread …………… 28
iii. Key Clients and Strategic Partners………. 28
iv. High-level View of Offerings………… 28
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities ……………. 28
3.2.3 Experian……………….29
i. Corporate Information ………….29
ii. Geographical Spread…………….29
iii. Key Clients & Strategic Partnerships ……….29
iv. High-level View of Offering…………..29
v. Juniper Research’s View: Key Strengths & Strategic Opportunities…30
3.2.4 Featurespace …………….30
i. Corporate Information ………….30
ii. Geographic Spread…………….30
iii. Key Clients & Strategic Partnerships ……….30
iv. High-level View of Products………….31
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..31
3.2.5 Feedzai………………31
i. Corporate Information ………….31
Table 3.6: Feedzai’s Funding Round…………. 32
ii. Geographical Spread…………….32
iii. Key Clients & Strategic Partnerships ……….32
iv. High-level View of Offering…………..32
v. Juniper Research’s View: Key Strengths & Strategic Opportunities…33
3.2.6 FICO………………..33
i. Corporate Information ………….33
Table 3.7: FICO’s Financial Snapshot ($m) 2018-2021………. 33
ii. Geographic Spread…………….33
iii. Key Clients & Strategic Partnerships ……….33
iv. High-level View of Products………….34
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..34
3.2.7 GBG………………..34
i. Corporate Information ………….34
Table 3.8: GBG PLC Financial Snapshot ($m) 2021-2022…….. 35
ii. Geographical Spread …………… 35
iii. Key Clients & Strategic Partnerships ……… 35
iv. High-level View of Offering …………. 35
v. Juniper Research’s View: Key Strengths & Strategic Opportunities .. 36
3.2.8 Kount, an Equifax Company………… 36
i. Corporate Information…………. 36
ii. Geographical Spread …………… 36
iii. Key Clients & Strategic Partnerships ……… 36
iv. High-level View of Offering …………. 37
v. Juniper Research’s View: Key Strengths & Strategic Opportunities .. 38
3.2.9 LexisNexis Risk Solutions………….. 38
i. Corporate Information…………. 38
ii. Geographical Spread …………… 38
iii. Key Clients & Strategic Partnerships ……… 38
iv. High-level View of Offering …………. 39
v. Juniper Research’s View: Key Strengths & Strategic Opportunities .. 39
3.2.10 Microsoft……………… 40
i. Corporate Information…………. 40
ii. Geographical Spread …………… 40
iii. Key Clients & Strategic Partnerships ……… 40
iv. High-level View of Offering …………. 40
v. Juniper Research’s View: Key Strengths & Strategic Opportunities .. 41
3.2.11 NICE Actimize ……………. 41
i. Corporate Information…………. 41
ii. Geographical Spread …………… 42
iii. Key Clients & Strategic Partnerships ……… 42
iv. High-level View of Offering …………. 42
v. Juniper Research’s View: Key Strengths & Strategic Opportunities .. 43
3.2.12 NuData Security…………… 43
i. Corporate Information…………. 43
ii. Geographical Spread…………….43
iii. Key Clients & Strategic Partnerships ……….43
iv. High-level View of Offering…………..44
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..44
3.2.13 Pelican ……………….44
i. Corporate Information ………….44
ii. Geographical Spread…………….44
iii. Key Clients & Strategic Partnerships ……….45
iv. High-level View of Offerings …………45
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..45
3.2.14 Riskified……………..45
i. Corporate Information ………….45
Figure 3.9: Riskified Financial Results, Revenue & Gross Profit ($m), Q1 2020 – Q3 2021……………….. 45
ii. Geographic Spread…………….46
iii. Key Clients & Strategic Partnerships ……….46
iv. High-level View of Offerings …………46
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..46
3.2.15 SymphonyAI Sensa …………….47
i. Corporate Information ………….47
ii. Geographical Spread…………….47
iii. Key Clients & Strategic Partnerships ……….47
iv. High-level View of Offerings …………47
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities……………..47
3.2.16 Temenos…………….48
i. Corporate Information ………….48
Table 3.10: Temenos’ Financial Snapshot ($m) 2020-2021…….. 48
ii. Geographical Spread …………… 48
iii. Key Clients & Strategic Partnerships ……… 48
iv. High-level View of Offerings………… 48
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities ……………. 49
3.2.17 Vesta………………. 49
i. Corporate Information…………. 49
Table 3.11: Vesta’s Funding Rounds, 2003 & 2020 ……….49
ii. Geographical Spread …………… 49
iii. Key Clients & Strategic Partnerships ……… 49
iv. High-level View of Offerings………… 49
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities ……………. 50
3.3 Juniper Research Leaderboard Assessment Methodology…… 51
3.3.1 Limitations & Interpretation………… 51
Table 3.12: Juniper Research Competitor Leaderboard Scoring Criteria – AI in Financial Fraud Detection……………52

4. AI in Financial Fraud Detection – Market Forecasts

4.1 Introduction……………….. 54
4.2 Methodology & Assumption ………….. 54
Figure 4.1: AI Fraud Detection Spend Forecast Methodology ……..55
Figure 4.2: AI Transaction Monitoring & Savings Forecast Methodology ….56
4.3 Forecast Summary ……………… 57
4.3.1 AI Fraud Detection Spend…………. 57
Figure & Table 4.3: Total Spend on AI-enabled Fraud Detection & Prevention Platforms ($m), Split by 8 key Regions, 2022-2027……….57
4.3.2 Number of Transactions Monitored by AI ………. 58
Figure & Table 4.4: Number of Digital Commerce Transactions Monitored by Financial Fraud Detection Systems Including AI (m) Split by 8 Key Regions, 2022-2027………………..58
4.3.3 Total Cost Savings from AI …………59
Figure & Table 4.5: Total Cost Savings from Digital Commerce Transactions Monitored by Financial Fraud Detection Systems including AI ($m), Split by 8 Key Regions, 2022-2027 ……………… 59


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