金融詐欺検知AI:重要動向、競合スコアボード、市場予測 2022-2027年

出版:Juniper Research(ジュニパーリサーチ) 出版年月:2022年11月

AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027
金融詐欺検知AI:重要動向、競合スコアボード、市場予測 2022-2027年

価格 GBP2,990
種別 英文調査報告書

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Juniper Research(ジュニパーリサーチ)「金融詐欺検知AI:重要動向、競合スコアボード、市場予測 2022-2027年 – AI in Financial Fraud Detection: Key Trends, Competitor Leaderboard & Market Forecasts 2022-2027」は 金融詐欺の検知および防止における人工知能(AI)導入の需要促進要因となる重要動向市場を調査し、AIが活用されている主要セグメントと今後の課題について分析しています。また、Juniper Researchの競合スコアボードではAIを活用した金融詐欺検知と防止関連ベンダ17社を分析しています。

このレポートではAIを活用した金融詐欺検知および防止向けプラットフォーム支出、AI監視によるデジタルコマース取引数とルールベースシステムによるデジタルコマース取引数の比較、金融詐欺取引監視にAIを活用した場合の時間とコストの節約に関する市場予測も提供します。予測は世界8地域・60ヶ国を対象としています。

主な掲載内容

  • 市場ダイナミクス
  • 調査結果と戦略的提言
  • Juniper Researchの競合スコアボード: 17社の特性と能力評価
    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
  • 産業予測
    • 提供データ
      • AIを活用した金融詐欺検知および防止向けプラットフォーム支出
      • AI監視によるデジタルコマース取引数
      • ルールベースシステムによるデジタルコマース取引数
      • 金融詐欺取引監視へのAI活用による時間節約
      • 金融詐欺取引監視へのAI活用によるコスト削減
    •  対象地域・国(世界8地域・60ヶ国)
      • 北米:カナダ、米国
      • ラテンアメリカ:アルゼンチン、ブラジル、チリ、コロンビア、エクアドル、メキシコ、ペルー、ウルグアイ
      • 西欧:オーストリア、ベルギー、デンマーク、フィンランド、フランス、ドイツ、ギリシャ、アイルランド、イタリア、オランダ、ノルウェー、ポルトガル、スペイン、スウェーデン、スイス、英国
      • 中東欧:クロアチア、チェコ共和国、ハンガリー、ポーランド、ルーマニア、ロシア、トルコ、ウクライナ
      • 極東と中国:中国、香港、日本、韓国
      • インド亜大陸:バングラデシュ、インド、ネパール、パキスタン
      • その他のアジア太平洋地域:オーストラリア、インドネシア、マレーシア、ニュージーランド、フィリピン、シンガポール、タイ、ベトナム
      • アフリカ&中東:アルジェリア、エジプト、イスラエル、ケニア、クウェート、ナイジェリア、カタール、サウジアラビア、南アフリカ、アラブ首長国連邦

当レポートは下記疑問への回答につながる情報を提供しています。

  1. AIによる金融詐欺検知と防止市場の総価値は2027年にどうなるか?
  2. AIが金融詐欺防止使用における説明可能性の重要度は?そしてどのように円滑に進めるか?
  3. AIが金融詐欺にどれほどの影響を与えるか?
  4. AIによる金融詐欺検知市場においてベンダにとっての最大のビジネスチャンスはどこにあるか?
  5. AIによる金融詐欺検知プラットフォームの主要ベンダは?

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.

レポート構成&価格表

  • 市場動向・戦略・予測レポート(PDF)
    Market trends, strategies and forecasts report (pdf)
  • 市場データ&予測(Excel)
    Market data & forecasts – All topic data and interactivity (xls)
  • 最新データへの12ヶ月アクセス
    harvest market data platform (12 months’ online access)
GBP 2,990

[プレスリリース]
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.


目次

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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