Our 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.
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.
Please note: the online download version of this report is for a global site license.
- 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 ($m), Split by 8 Key Regions, 2022-
- 2.2.2 Speed
- 2.2.3 Pattern Recognition
- 2.2.4 AI versus Rules Based
- Figure 2.4: Typical Rules-based Fraud Screening Process
- Figure 2.5: Typical AI-enabled Fraud Screening Process
- 2.2.5 The Importance of Data
- 2.3 Online Payment Fraud & the Fraud Prevention Market
- 2.3.1 Types of Fraud
- 2.3.2 Key Fraud Trends
- 2.3.3 Different Types of Fraud Detection & Prevention Systems
- i. Merchant/eCommerce Focused
- ii. Issuer Focused
- iii. General Platforms
- iv. Identity-focused Platforms
- 3. AI in Financial Fraud Detection – Competitor Leaderboard
- 3.1 Why Read This Section
- Table 3.1: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors Included & Product Portfolio
- Figure 3.2: Juniper Research Competitor Leaderboard for AI in Fraud Detection & Prevention Vendors
- Table 3.3: Juniper Research Competitor Leaderboard: AI in Fraud Detection & Prevention Vendors & Positioning
- Table 3.4: Juniper Research Leaderboard Heatmap: AI in Fraud Detection & Prevention Vendors
- 3.2 AI in Fraud Detection & Prevention – Vendor Profiles
- 3.2.1 ACI Worldwide
- i. Corporate Information
- Table 3.5: ACI Worldwide’s Financial Snapshot ($m), 2019-
- 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 ($m) 2018-
- ii. Geographic Spread
- iii. Key Clients & Strategic Partnerships
- iv. High-level View of Products
- v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
- 3.2.7 GBG
- i. Corporate Information
- Table 3.8: GBG PLC Financial Snapshot ($m) 2021-
- 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 ($m), Q1 2020 – Q3
- ii. Geographic Spread
- iii. Key Clients & Strategic Partnerships
- iv. High-level View of Offerings
- v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
- 3.2.15 SymphonyAI Sensa
- i. Corporate Information
- ii. Geographical Spread
- iii. Key Clients & Strategic Partnerships
- iv. High-level View of Offerings
- v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
- 3.2.16 Temenos
- i. Corporate Information
- Table 3.10: Temenos’ Financial Snapshot ($m) 2020-
- 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 &
- 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 ($m), Split by 8 key Regions, 2022-
- 4.3.2 Number of Transactions Monitored by AI
- Figure & Table 4.4: Number of Digital Commerce Transactions Monitored by Financial Fraud Detection Systems Including AI (m) Split by 8 Key Regions, 2022
- 4.3.3 Total Cost Savings from AI
- Figure & Table 4.5: Total Cost Savings from Digital Commerce Transactions Monitored by Financial Fraud Detection Systems including AI ($m), Split by 8 Key Regions, 2022
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