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金融欺詐檢測中的人工智能：主要趨勢、競爭排行榜和市場預測 (2022-2027)
市場調查報告書
商品編碼
1158981

金融欺詐檢測中的人工智能：主要趨勢、競爭排行榜和市場預測 (2022-2027)

AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027
出版日期: | 出版商: Juniper Research Ltd | 英文 | 商品交期: 最快1-2個工作天內

價格
簡介目錄

全球在 AI 支持的金融欺詐檢測和預防平台上的支出預計將從 2022 年的 65 億美元以上增長到 2027 年的 100 億美元以上。 它顯示預測期內的增長率為 57%。 人工智能成本節約預計到 2027 年將達到 104 億美元，高於 2022 年的 27 億美元，增長 285%。

主要市場統計數據

2022 年市場規模 65 億美元
2027 年市場規模 100 億美元
2021-2027年市場增長率 57%

本報告調查了 AI 在金融欺詐檢測中的全球市場，定義和概述了市場，AI 在金融欺詐檢測中的重要性，分析了影響市場增長的各種因素，並與主要 AI 供應商競爭。分析，AI 欺詐檢測支出、AI 監控的交易數量、AI 成本節約預測、戰略建議等。

我們的研究包提供什麼

  • 戰略/預測 (PDF)
  • 5 年市場規模和預測電子表格 (Excel)
  • 12 個月訪問在線數據平台

數據和互動預測

主要市場預測：按細分

  • 在支持 AI 的金融欺詐檢測和預防平台上花費的金額
  • 啟用 AI 的系統篩選的數字商務交易數量
  • 純基於規則的系統篩選的數字商務交易數量
  • 通過使用 AI 監控金融欺詐來節省時間
  • 使用 AI 監控財務欺詐的成本降低效果

內容

第 1 章金融欺詐檢測中的人工智能：要點和戰略建議

第 2 章金融欺詐檢測中的人工智能：市場格局

  • 簡介/定義
  • 人工智能的重要性
  • 在線支付欺詐和欺詐預防市場

第 3 章金融欺詐檢測中的 AI：競爭排行榜

  • 供應商簡介
    • 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

第 4 章金融欺詐檢測中的 AI：市場預測

  • 介紹
  • 調查方法和假設
  • 預測摘要
    • 用於 AI 欺詐檢測的金額
    • AI 監控的交易數量
    • 通過 AI 降低總成本
簡介目錄

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
2021 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.

Table of Contents

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

  • 1.1. Key Takeaways
  • 1.2. Strategic Recommendations

2. AI in Financial Fraud Detection - Market Landscape

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

3. AI in Financial Fraud Detection - Competitor Leaderboard

  • 3.1. Why Read This Section
    • Table 3.1: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors Included & Product Portfolio
    • Figure 3.2: Juniper Research Competitor Leaderboard for AI in Fraud Detection & Prevention Vendors
    • Table 3.3: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors & Positioning
    • Table 3.4: Juniper Research Leaderboard Heatmap: AI in Fraud Detection & Prevention Vendors
  • 3.2. AI in Fraud Detection & Prevention - Vendor Profiles
    • 3.2.1. ACI Worldwide
      • i. Corporate Information
        • Table 3.5: ACI Worldwide's Financial Snapshot ($m), 2019-2021
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.2. Cybersource
      • i. Corporate Information
      • ii. Geographic Spread
      • iii. Key Clients and Strategic Partners
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.3. Experian
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.4. Featurespace
      • i. Corporate Information
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.5. Feedzai
      • i. Corporate Information
        • Table 3.6: Feedzai's Funding Round
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.6. FICO
      • i. Corporate Information
        • Table 3.7: FICO's Financial Snapshot ($m) 2018-2021
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.7. GBG
      • i. Corporate Information
        • Table 3.8: GBG PLC Financial Snapshot ($m) 2021-2022
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.8. Kount, an Equifax Company
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.9. LexisNexis Risk Solutions
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.10. Microsoft
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.11. NICE Actimize
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 3.2.12. NuData Security
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.13. Pelican
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.14. Riskified
      • i. Corporate Information
        • Figure 3.9: Riskified Financial Results, Revenue & Gross Profit ($m), Q1 2020 - Q3 2021
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.15. SymphonyAI Sensa
      • i. Corporate Information
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.16. Temenos
      • i. Corporate Information
        • Table 3.10: Temenos' Financial Snapshot ($m) 2020-2021
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
    • 3.2.17. Vesta
      • i. Corporate Information
        • Table 3.11: Vesta's Funding Rounds, 2003 & 2020
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Development Opportunities
  • 3.3. Juniper Research Leaderboard Assessment Methodology
    • 3.3.1. Limitations & Interpretation
      • Table 3.12: Juniper Research Competitor Leaderboard Scoring Criteria - AI in Financial Fraud Detection

4. AI in Financial Fraud Detection - Market Forecasts

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