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AI in Financial Fraud Detection: Key Trends, Competitor Leaderboards & Market Forecasts 2022-2027: Full Research Suite

Publisher Juniper Research
Published Nov 21, 2022
Length 64 Pages
SKU # JNRH19878214

Description

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.

Table of Contents

64 Pages
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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