Juniper Research’s new report examines the Fraud Detection and Prevention in Banking market landscape in detail; assessing current fraud trends and factors shaping the market, such as the growing use and anticipation surrounding different technologies such as AI and ML (Machine Learning), and the existing and impending regulations in the space. The report delivers comprehensive analysis of the strategic opportunities for fraud detection and prevention providers within banking; addressing key verticals, developing challenges, and how stakeholders should navigate these.
Please note: the online download version of this report is for a global site license.
- 1. Key Takeaways & Strategic Recommendations
- 1.1 Key Takeaways
- 1.2 Strategic Recommendations
- 2. Market Landscape
- 2.1 Introduction
- 2.2 Definitions and Scope
- Figure 2.1: Visualisation of Fraud
- 2.3 Types of Fraud
- 2.3.1 First-party Fraud
- i. Application Fraud and Fake Accounts
- ii. Money Mules
- iii. Fronting
- iv. Sleeper Fraud
- v. APP Fraud
- vi. Social Engineering
- 2.3.2 Money Laundering
- Figure 2.2: Visualisation of Money Laundering
- 2.3.3 Chargeback Fraud
- Figure 2.3: Visualisation of Chargeback Fraud
- 2.3.4 ATO
- Figure 2.4: Visualisation of Account Takeover
- 2.3.5 Synthetic Identity
- Figure 2.5: Visualisation of Synthetic Identity Fraud
- i. Detection of Synthetic Identity Fraud
- 2.4 Solutions Utilised in Issuer Fraud Detection & Prevention
- 2.4.1 Fraud Detection and Prevention Systems
- Figure 2.6: Types of Fraud Detection and Techniques
- i. Biometrics
- ii. Tokenisation
- iii. Behavioural Analytics
- iv. AML Software
- 3. Emerging Fraud Market
- 3.1 Key Themes and Areas Involved
- 3.2 Key Trends & Current Market Drivers
- 3.3 Payment Types
- 3.3.1 Open Banking
- i. Increase in Fraud Through Open Banking
- Figure 3.1: Visualisation of Open Banking
- ii. Decrease in Fraud Through Open Banking
- Figure 3.2: Visualisation of Open Banking
- 3.3.2 BNPL
- i. Increase in Fraud Through BNPL
- Figure 3.3: Buy Now Pay Later Flow
- ii. Decrease in Fraud Through BNPL
- 3.3.3 CBDCs
- Figure 3.4: Visualisation of CBDC
- i. Increase in Fraud Through CBDCs
- ii. Decrease in Fraud Through CBDCs
- iii. Mitigating CBDC Fraud
- 3.3.4 Cryptocurrency
- i. Increase in Fraud Through Cryptocurrency
- ii. Mitigating Cryptocurrency Fraud
- 3.3.5 Real-time Payments
- Figure 3.5: Visualisation of Instant Payments
- i. Increase in Fraud Through Real-time Payments
- ii. Decrease in Fraud Through Real-time Payments
- 3.3.6 Money Transfer
- i. Increase in Fraud Through Money Transfer
- Figure 3.6: Total Number of Fraudulent Money Transfer Transactions (m), Split by 8 Key Regions, 2024-
- ii. Decrease in Fraud Through Money Transfer
- 3.4 Technologies
- 3.4.1 AI
- i. Benefits of AI in Fraud Detection
- Figure 3.7: Benefits of AI in Fraud Detection
- ii. How AI Is Being Utilised by Fraudsters
- 3.4.2 ML
- i. Benefits of ML in Fraud Detection
- ii. How ML Is Being Utilised by Fraudsters
- 3.4.3 APIs
- i. Benefits of APIs in Fraud Detection
- ii. How APIs Are Being Utilised by Fraudsters
- iii. Open Banking APIs
- iv. FAPI (Financial-grade API)
- 3.5 Regulations
- 3.5.1 UK Faster Payments Regulation
- 3.5.2 PSD
- 3.5.3 RTS (Regulatory Technical Standards) Implications for Payment Service Providers
- i. Fraud Detection
- ii. Merger of Home Working, Personal Devices and Corporate Access
- iii. Exemptions from SCA
- iv. Implications
- v. Network Tokenisation in India
- vi. Regulation Differences
- 4. Segment Analysis
- 4.1 Introduction
- 4.1.1 Banks and Credit Unions
- Figure 4.1: Total Spend on Fraud Detection and Prevention by Banks and Credit Unions ($m), Globally, Split by 8 Key Regions, 2024-
- 4.1.2 Fintechs
- Figure 4.2: Total Number of Fintechs Using Fraud Detection and Prevention Solutions, 2024-
- 4.1.3 Lenders
- i. Reducing Exposure to Fraud
- ii. Enhance Risk Management
- iii. Improved Quality of Loan Portfolio
- iv. Protect Trust and Reputation
- Figure 4.3: Total Spend on Fraud Detection and Prevention by Lenders Globally ($m), Split by 8 Key Regions, 2024-
- 4.1.4 Investment Companies
- Figure 4.4: Total Spend on Fraud Detection and Prevention from Investment Companies ($m), Split by 8 Key Regions, 2024-
- 4.2 Key Challenges
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