このレポートでは、オンライン決済詐欺に関する様々な詐欺の種類、代替決済の増加による影響、オープンバンキングAPIにおける今後の課題、銀行、遠隔デジタル・物理商品、航空会社など様々なセグメントにおける詐欺の種類の違いなど、市場の詳細な評価を提供しています。

 

Report　Description

Juniper Research’s Online Payment Fraud research report provides a detailed evaluation of the market, including different fraud types, the impact of the increase in alternative payment types, the future challenges within Open Banking APIs, and differing types of fraud in a variety of segments including banking, remote digital and physical goods and airlines. In addition, this report covers market opportunities; providing strategic insights into the development of new technologies, such as AI and machine learning, and key steps that are important for both vendors and merchants to take to manage increasing fraud risks.

 

The report also positions 21 online payment fraud detection and prevention vendors in our Juniper Research Competitor Leaderboard; providing an invaluable resource for stakeholders seeking to understand the competitive landscape in the market.

The research suite also contains a detailed dataset; providing forecasts for 60 countries across a wide range of different metrics, including fraud value for remote digital and physical goods purchases and online transactions, money transfer fraud rate and digital banking fraud rate, the splits for fraud rate by vertical, as well as including the split for mobile and online transactions.    

 

This research suite comprises of:

 

Key Market Statistics

Merchant Losses in 2023: $38 billion

Merchant Losses in 2028: $91 billion

Total Merchant Losses 2023-2028: $362 billion

 

KEY FEATURES

・Market Dynamics: A strategic analysis of the major drivers, challenges, and innovations shaping the adoption and development of fraud detection and prevention solutions:

COVID-19 pandemic’s impact on the online payment fraud market and the ongoing influence from it.

Future strategic direction and market outlook for fraud detection and prevention vendors.

Key drivers for fraud detection and prevention vendors, including an increase in data provided from merchants, the implementation of AI and ML and their impact on fraud detection and prevention, and the growing needs of SMEs to access sophisticated fraud prevention systems.  

・Segment Analysis: A segment analysis of banking and money transfer, remote physical and digital goods and airlines, looking into key challenges, key trends and future outlook. This gives readers insight into the markets with a growing need for improvement within fraud detection and prevention.

・Benchmark Industry Forecasts: Includes forecasts for the total fraudulent transaction value for airline eTicketing, remote digital goods, remote physical goods, money transfer and digital banking, as well as the total spend on fraud detection and prevention. This data is split by online and mobile transactions, and for our 8 key forecast regions and 60 countries:

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.

・Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 21 online payment fraud detection and prevention vendors:

 

KEY QUESTIONS ANSWERED

 

COMPANIES REFERENCED

Interviewed: Feedzai, Fraudio, Kount, LexisNexis, Vesta, Visa.

 

Included in Juniper Research Competitor Leaderboard: Accertify, ACI Worldwide, Experian, Featurespace, Feedzai, FICO, Fiserv, Fraudio, GBG, Kount, LexisNexis Risk Solutions, Microsoft, NuData Security, Riskified, RSA Security, SAS, Signifyd, TransUnion, Vesta, Visa, Worldpay.

 

Mentioned: 4Stop, Accelitas, ACI Worldwide, Acuris, Adobe, AFS (Advanced Financial Solutions), Allianz, ALTO, American Express, Aspenware, AT&T, AU10TIX, Authorize.net, AWS, Axerve, Bailhang Credit, Bank of Singapore, Barclaycard Payments, BAV (Bank Account Validation), BehavioSec, BigCommerce, Black Opal, Blibli, BlueSnap, BNP Paribas, Boemska, Braintree, Bukalapak, Cashplus, Cayan, Centro, Chargebacks911, checkout.com, CISCO, Citrix, Cognizant, Confused.com, ConnexPay, CredoLab, Crypto.com, Cybersource, Dell, Deloitte, Delta Air Lines, Dentsu, Deutsche Bank, Diebold Nixdorf, eBay, Emailage, Entersekt, Entree Capital, Entrust Datacard, Equifax, Esure, Etisalat, Eway, Fidelity Management & Research Company, Finxact, Fischer, GDS Link, General Atlantic, GeoComply, Global Payments, HSBC, IBM, ID R&D, IDology, Innovalor, Invation, Jack Henry & Associates, Inc, JPMorgan Chase, Kaidee, Kenbi, KPMG, Landsbankinn, Laurentian Bank, Lego, LeoVegas, Mastercard, Midigator, MySQL, NatWest, NewSuite, NorthRow, NortonLife Lock, NTT Data, Oracle, OTP Bank, P97, PassFort, Payoneer, Philippines Security Bank Corporation, Phonelink, PingIdentity, Pitango Venture Capital, Plaid, Playtech, Praxis, Primer, Provenir, PWC, Qumra Capital, Railsr, RedAbierta, Regily, RocketFuel Blockchain, Inc, Sage, Salesforce Commerce Cloud, Santander Bank, SAP, Sapiens, Scudetto, Sekura, SEON, Shopify, Shyft Network, Soldo, SPhonic, SumUp, Symphony Technology Group, Synectics Solutions, Temenos, Teradata, Thales, The Access Group, Threat Fabric, TruNarrative, Trunarrative, TSYS, UK Finance, Visualsoft, Viva Wallet, Vtex, WiPay, Worldline, Zilch.

 

 

DATA & INTERACTIVE FORECAST

Juniper Research’s Online Payment Fraud forecast suite includes splits for:

Metrics include the total card not present fraud transaction value split by online and mobile, total spend on fraud detection and prevention, fraud rate by value.

 

Geographical splits: 60 countries

Number of tables: Over 67 tables

Number of datapoints: Over 30,000 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 receive alerts 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 client’s ability to both understand a particular market and to integrate their own views into the model.

 

 

FORECAST SUMMARY

・Merchant losses from online payment fraud will exceed $362 billion globally between 2023 to 2028, with losses of $91 billion alone in 2028. A rise in eCommerce transactions in emerging markets is driving this growth. Merchants there are facing new threats, such as an increased use of AI for attacks. Online payment fraud is where cybercriminals conduct false or illegal transactions online, using a number of different fraud strategies, such as phishing, business email compromise or account takeover.

・Underpinned by a robust scoring methodology, the new Competitor Leaderboard ranked the top 21 fraud detection and prevention vendors, using criteria such as the relative size of their customer base, completeness of their solutions and their future business prospects.

・The top 5 vendors for 2023:

・Leading players scored well based on the breadth of their anti-fraud orchestration capabilities, as well as their use of AI for analysing trends in fraudster behaviour. In order to stay ahead of the competition, vendors must utilise data collected throughout the whole eCommerce process to further develop their fraud detection and prevention solutions through training and advancing AI models.

・eCommerce payment vendors need to offer dashboards and data visualisations to their smaller SME customers. At present, SMEs lack access to good customer analytics, and this data could highlight consumer purchasing behaviours, as well as providing insights into payment method popularity and fraud. By offering additional services to SMEs, eCommerce payment vendors can differentiate their portfolios in an increasingly competitive and commoditised market.

 

Regions:8 Key Regions - includes North America, Latin America, West Europe, Central & East Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East

Countries:Algeria, Argentina, Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile, China, Colombia, Croatia, Czech Republic, Denmark, Ecuador, Egypt, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Kenya, South Korea, Kuwait, Malaysia, Mexico, Nepal, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russian Federation, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Arab Emirates, UK, Uruguay, USA, Vietnam

1. Market Trends &Strategies

Table of Contents

 

1. Key Takeaways and Strategic Recommendations

1.1 Key Takeaways . 5

1.2 Strategic Recommendations . 6

2. Market Landscape

2.1 Introduction . 8

2.2 Types of Fraud . 8

2.2.1 First-party Fraud . 8

i. Fronting . 8

ii. Address Fronting . 8

iii. Friendly Fraud . 8

iv. Chargeback Fraud . 9

2.2.2 Third-party Fraud . 9

i. Identity Fraud and KYC (Synthetic Identity) . 9

ii. Account Takeover . 9

iii. Silent Fraud . 9

2.2.3 Phishing . 9

i. Phishing . 9

ii. Whaling . 10

iii. Pharming . 10

2.2.4 Other Types of Fraud . 10

i. Clean Fraud . 10

ii. Affiliate Fraud . 10

iii. Re-shipping . 10

iv. Botnets . 10

v. Triangulation . 11

vi. Pagejacking . 11

2.3 Physical and Digital Goods . 11

Figure 2.1: Total Value of Fraudulent Transactions, Split by Segment, Global

2023-2028. 11

2.3.1 Remote Physical Goods . 11

Figure 2.2: Graph Showing Total Remote Physical Goods Fraud ($m), Split by 8

Key Regions, 2023-2028 . 12

2.3.2 Remote Digital Goods . 12

Figure 2.3: Graph Showing Total Remote Digital Goods Fraud ($m), Split by 8 Key

Regions, 2023-2028 . 12

2.4 Key Trends in Digital Fraud . 13

2.5 Alternative Payment Methods . 18

2.5.1 Payments: Changing Dynamics & Expanding Ecosystem . 18

2.5.2 Online Banking . 18

i. Online Banking Fraud . 18

ii. Online Banking Fraud Prevention Solutions . 18

2.5.3 BNPL . 19

i. BNPL Fraud . 19

ii. BNPL Fraud Prevention Solutions . 19

2.5.4 CBDCs . 19

i. CBDC Fraud. 19

ii. CBDC Fraud Prevention . 19

2.5.5 Cryptocurrencies . 20

i. Cryptocurrency Fraud. 20

ii. Cryptocurrency Fraud Prevention . 20

3.1 Introduction . 22

3.2 Future Challenges and Open APIs . 22

3.2.1 Open Banking APIs . 22

3.2.2 The API in the Machine . 22

3.2.3 FAPI (Financial-grade API) . 23

3.2.4 Open Banking, CIBA (Client-initiated Back Channel Authorisation) and

Premium APIs . 23

3.2.5 PSD2 Overview . 24

3.2.6 PSD2 State of the Nations . 24

Figure 3.1: Ravelin 3DS2 Statistics . 24

3.2.7 RTS (Regulatory Technical Standards) Implications for Payment Service

Providers . 25

i. Fraud Detection . 25

ii. Merger of Home Working, Personal Devices and Corporate Access 26

iii. Exemptions from SCA . 26

iv. Implications . 27

v. Regulation Differences . 27

3.3 The Fintech in the Equation . 27

3.3.1 Establishing Resilient Fintech Products . 27

Figure 3.2: Visualisation Displaying the 10 Step Framework Process for

Establishing Resilient Fintech Products . 28

3.4 Consumer Behaviour and Bots, a Wealth of Opportunities for Fraudsters . 28

3.4.1 Type of API Attacks . 28

i. Unauthorised API Requests . 28

ii. Unauthorised Modification of Request Or Token Responses . 28

iii. Unauthorised Token Use . 28

iv. Exposure and Modification of API Response Data . 29

3.4.2 API Authentication Security . 29

3.4.3 Avoiding Logic Abuse . 30

3.5 Real-time Payments. 30

Table 3.3: Global Instant Payments Market Status . 31

3.6 Fraud and Payments . 32

3.7 Digital Identity & Fraud . 32

3.7.1 Decentralised Identity Wallets . 32

Figure 3.4: Visualisation of Decentralised Identity Wallets . 32

i. Benefits of Decentralised Identity Wallets . 33

3.8 3DS 2.0 & Biometric Authorisation of Transactions. 33

3.8.1 Authentication Mechanisms . 33

i. OTP (One-time Passwords) . 33

ii. Biometrics . 34

iii. SIM Swap Fraud . 34

3.8.2 Further 3DS Implications . 34

i. 3D Secure Authentication Failure . 35

3.8.3 Next Steps & Regional Outlook . 35

4. Segment Analysis

4.1 Introduction . 37

4.2 Banking and Money Transfer . 37

4.2.1 Key Challenge: Advanced Persistent Threats . 37

Table 4.1: Phases of Threats . 38

4.2.2 Key Challenge: Open Banking & Multi-part Attacks . 38

4.2.3 Key Trends & Outlook in the Financial Sector . 39

4.3 Remote Physical and Digital Goods . 40

4.3.1 Physical Goods . 40

Table 4.2: Revenue & Growth of Selected eCommerce Players. 40

i. Impact of COVID-19 . 40

ii. Impact of Inflation and Cost of Living Crisis . 40

Figure 4.3: Total Transaction Volume Physical Goods (m), Split by 8 Key Regions,

2022-2027. 41

4.3.2 Digital Goods . 41

i. Impact of Inflation and Cost of Living Crisis . 41

ii. Gaming . 41

iii. Music . 41

iv. Video . 42

Figure 4.4: Share of Netflix Subscribers Worldwide, 2023. 42

v. eBooks . 42

vi. Adult Content . 42

vii. Ticketing . 42

4.3.3 Key Challenge: Synthetic Identity . 43

i. Detection . 43

ii. Activity History . 43

4.3.4 Key Challenge: Deepfakes . 43

4.3.5 Account Takeover . 44

Figure 4.5: Visualisations Displaying Process of ATO (Account Takeover) . 44

4.3.6 Omnichannel Fraud . 44

4.3.7 Key Trends & Outlook in eRetail . 45

4.3.8 Machine Learning . 45

Figure 4.6: Goal Revenue Optimisation Fraud Management . 45

4.4 Airlines . 46

4.4.1 Key Challenge: Security . 46

4.4.2 Key Challenge: Third-party Attacks . 46

4.4.3 Key Challenge: Chargebacks . 47

4.4.4 Key Trends & Future Outlook in the Airline Sector . 47

 

2. Competitor　Leaderboard

Table of Contents

 

1. Juniper Research Competitor Leaderboard

1.1 Why Read This Report . 5

Table 1.1: Juniper Research Competitor Leaderboard Vendors: Online Payment

Fraud . 6

Figure 1.2: Juniper Research Competitor Leaderboard ? Online Payment Fraud . 7

Table 1.3: Juniper Research Competitor Leaderboard: Online Payment Fraud

Vendor Ranking . 7

Table 1.4: Juniper Research Competitor Leaderboard Online Payment Fraud ?

Heatmap . 8

Table 1.5: Juniper Research Competitor Leaderboard Online Payment Fraud ?

Heatmap ? Continued. 9

2. Company Profiles

2.1 Online Payment Fraud Vendor Profiles . 11

2.1.1 Accertify . 11

i. Corporate . 11

ii. Geographical Spread . 11

iii. Key Clients & Strategic Partnerships . 11

iv. High-level View of Offering . 11

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 13

2.1.2 ACI Worldwide . 13

i. Corporate . 13

Table 2.1: ACI Worldwide’s Revenue ($bn), 2016-2022 . 13

ii. Geographical Spread . 13

iii. Key Clients & Strategic Partnerships . 13

iv. High-Level View of Offering . 14

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 15

2.1.3 Experian . 15

i. Corporate . 15

ii. Geographical Spread . 15

iii. Key Clients & Strategic Partnerships . 15

iv. High-level View of Offering . 15

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 16

2.1.4 Featurespace . 16

i. Corporate . 16

ii. Geographical Spread . 16

iii. Key Clients & Strategic Partnerships . 16

iv. High-level View of Offering . 17

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 17

2.1.5 Feeedzai . 17

i. Corporate . 17

ii. Geographical Spread . 17

iii. Key Clients & Strategic Partnerships . 18

i. High-level View of Offering . 18

ii. Juniper Research’s View: Key Strengths & Strategic Opportunities . 18

2.1.6 FICO . 19

i. Corporate . 19

ii. Geographical Spread . 19

iii. Key Clients & Strategic Partnerships . 19

iv. High-level View of Offering . 19

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 20

2.1.7 Fiserv . 20

i. Corporate . 20

ii. Geographical Spread . 21

iii. Key Clients & Strategic Partnerships . 21

iv. High-level View of Offering . 21

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 22

2.1.8 Fraudio . 22

i. Corporate . 22

ii. Geographical Spread . 22

iii. Key Clients & Strategic Partnerships . 22

iv. High-level View of Offerings . 23

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 23

2.1.9 GBG . 24

i. Corporate . 24

ii. Geographical Spread . 24

iii. Key Clients & Strategic Partnerships . 24

iv. High-level View of Offering . 24

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 25

2.1.10 Kount . 25

i. Corporate . 25

ii. Geographical Spread . 26

iii. Key Clients & Strategic Partnerships . 26

iv. High-level View of Offering . 26

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 27

2.1.11 LexisNexis Risk Solutions . 27

i. Corporate . 27

ii. Geographic Spread . 28

iii. Key Clients & Strategic Partnerships . 28

iv. High-level View of Offering . 28

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 29

2.1.12 Microsoft . 29

i. Corporate . 29

ii. Geographical Spread . 29

iii. Key Clients & Strategic Partnerships . 29

iv. High-level View of Offering . 30

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 31

2.1.13 NuData Security . 31

i. Corporate . 31

ii. Geographical Spread . 31

iii. Key Clients & Strategic Partnerships . 31

iv. High-level View of Offering . 32

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 32

2.1.14 Riskified . 32

i. Corporate . 32

ii. Geographical Spread . 33

iii. Key Clients & Strategic Partnerships . 33

iv. High-level View of Offering . 33

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 34

2.1.15 RSA Security. 34

i. Corporate . 34

ii. Geographical Spread . 34

iii. Key Clients & Strategic Partnerships . 34

iv. High-level View of Offering . 34

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 36

2.1.16 SAS . 36

i. Corporate . 36

ii. Geographical Spread . 36

iii. Key Clients & Strategic Partnerships . 36

iv. High-level View of Offering . 36

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 37

2.1.17 Signifyd . 37

i. Corporate . 37

ii. Geographical Spread . 37

iii. Key Clients & Strategic Partnerships . 37

iv. High-level View of Offering . 37

Figure 2.2: Visualisation Displaying Signifyd’s Commerce Protection Platform . 38

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 38

2.1.18 TransUnion . 38

i. Corporate . 38

ii. Geographical Spread . 38

iii. Key Clients & Strategic Partnerships . 39

iv. High-level View of Offering . 39

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 39

2.1.19 Vesta . 40

i. Corporate . 40

ii. Geographical Spread . 40

iii. Key Clients & Strategic Partnerships . 40

iv. High-level View of Offering . 40

Figure 2.: Visualisation Displaying Vestas Payment Guarantee . 41

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 41

2.1.20 Visa Acceptance Solutions. 41

i. Corporate . 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 . 42

2.1.21 Worldpay . 43

i. Corporate . 43

ii. Geographical Spread . 43

iii. Key Clients & Strategic Partnerships . 43

iv. High-level View of Offering . 43

v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 44

2.2 Juniper Research Leaderboard Assessment Methodology . 45

2.2.1 Limitations & Interpretations . 45

Table 2.: Juniper Research Competitor Leaderboard Scoring Criteria . 46

 

3. Data & Forecasting

Table of Contents

 

1. Market Overview

1.1 Introduction . 4

1.2 Types of Fraud . 4

1.2.1 First-party Fraud . 4

i. Fronting . 4

ii. Address Fronting . 4

iii. Friendly Fraud . 4

iv. Chargeback Fraud . 5

1.2.2 Third-party Fraud . 5

i. Identity Fraud and KYC (Know Your Customer) Synthetic Identity . 5

ii. Account Takeover . 5

iii. Silent Fraud . 5

1.2.3 Phishing . 5

i. Phishing . 5

ii. Whaling . 5

iii. Pharming . 6

1.2.4 Other Types of Fraud . 6

i. Clean Fraud . 6

ii. Affiliate Fraud . 6

iii. Re-shipping . 6

iv. Botnets . 6

v. Triangulation . 6

vi. Pagejacking . 7

1.3 Introduction . 8

1.4 Methodology & Assumptions . 8

Figure 1.1: FDP Forecast Methodology . 10

1.5 Forecast Summary . 11

1.5.1 CNP Fraud . 11

Figure & Table 1.2: Total CNP Fraud Value ($m), Remote Goods Purchases,

Airline Tickets, Split by Goods Type 2023-2028 . 11

1.5.2 Total Value of Fraudulent Transactions . 12

Figure 1.3: Total Value of Fraudulent Transactions ($m), Split by eCommerce

Segment, 2023-2028 . 12

2. Airline eTicketing Fraud: Market Forecasts

2.1 Airline eTicketing Fraud Transaction Value . 14

Figure & Table 2.1: Total Airline eTicket Fraudulent Value ($m), Split by 8 Key

Regions, 2023-2028 . 14

2.2 Airline eTicketing Fraud: Online VS Mobile . 15

Figure & Table 2.2: Total Airline eTicket Fraudulent Value ($m), Split by Sales

Channel, 2023-2028 . 15

2.3 Airline eTicketing Fraud Rates . 16

Figure & Table 2.3: Total Airline eTicketing Fraud Rate by Value (%), Split by 8 Key

Regions, 2023-2028 . 16

3. Remote Digital Goods Purchases Fraud: Market Forecasts

3.1 Remote Digital Goods Fraud Transaction Value . 18

Figure 3.1: Total Remote Digital Goods Fraudulent Value ($m), Split by 8 Key

Regions, 2023-2028 . 18

3.2 Remote Digital Goods Fraud: Online vs Mobile . 19

Figure & Table 3.2: Total Remote Digital Goods Fraudulent Value ($m), Split by

Sales Channel, 2023-2028 . 19

3.3 Remote Digital Goods Fraud Rates . 20

Figure & Table 3.3: Total Remote Digital Goods Fraud Rates by Value (%), Split by

8 Key Regions, 2023-2028 . 20

4. Remote Physical Goods Purchases Fraud: Market Forecasts

4.1 Remote Physical Fraud Transaction Value . 22

Figure & Table 4.1: Total Remote Physical Goods Fraudulent Value ($m), Split by

8 Key Regions, 2023-2028 . 22

4.2 Remote Physical Goods Fraud Online vs Mobile . 23

Figure & Table 4.2: Total Remote Physical Goods Fraudulent Value ($m), Split by

Sales Channel, 2023-2028 . 23

4.3 Remote Physical Fraud Rates . 24

Figure & Table 4.3: Total Remote Physical Goods Fraud Rate by Value (%), Split

by 8 Key Regions, 2023-2028 . 24

5. Money Transfer Fraud: Market Forecasts

5.1 Money Transfer Fraud Transaction Value . 26

Figure & Table 5.1: Total Money Transfer Value ($m), Split by 8 Regions, 2023-

2028 . 26

5.1.1 Money Transfer Fraud: Online vs Mobile . 27

Figure & Table 5.2: Total Money Transfer Fraudulent Value ($m), Split by Sales

Channel, 2023-2028 . 27

5.2 Money Transfer Fraud Rates . 28

Figure & Table 5.3: Total Money Transfer Fraud Rate by Value (%), Split by 8 Key

Regions, 2023-2028 . 28

6. Digital Banking Fraud: Market Forecasts

6.1 Digital Banking Fraud Transaction Value . 30

Figure & Table 6.1: Total Digital Banking Fraudulent Value ($m), Split by 8 Key

Regions, 2023-2028 . 30

6.2 Digital Banking Fraud: Online vs Mobile . 31

Figure & Table 6.2: Total Digital Banking Transaction Value ($m), Split by Sales

Channel, 2023-2028 . 31

6.3 Digital Banking Fraud Rates . 32

Figure & Table 6.3: Total Digital Banking Fraud Rate by Value (%), Split by 8 Key

Regions, 2023-2028 . 32

7. Fraud Detection and Prevention Solutions

7.1 Fraud Detection and Prevention Solutions Market Size . 34

Figure & Table 7.1: Total Annual FDP Spend ($m), Split by 8 Key Regions 2023-

2028 . 34