세계의 은행용 부정 검사 및 방지 시장(2024-2029년)

Global Fraud Detection & Prevention in Banking Market: 2024-2029

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한글목차

주요 통계
부정 검사,방지 솔루션에의 지출액(2024년) :	173억 달러
부정 검사,방지 솔루션에의 지출액(2029년) :	322억 달러
시장 성장률(2024-2029년)	86%
예측 기간:	2024-2029년

본 보고서에서는 은행용 부정 검사 및 방지 시장을 조사했으며, 진화하는 부정 상황의 상세한 평가와 분석, 법규제 환경, 부정 검출,방지에 활용되는 기술,솔루션, 시장 성장 추진 인자 과 과제, 부정거래의 건수,피해액, 각종 금융기관에 의한 부정 검출,방지 솔루션에 대한 연간 총 지출, 지역별 상세 분석, 주요 벤더의 경쟁 리더보드 등을 정리했습니다.

SAMPLE VIEW

시장 데이터 및 예측 보고서

Juniper Research 시장 데이터 및 예측 보고서는 숫자로 현재 상태를 파악할 뿐만 아니라 그 이유를 조사 기법과 함께 자세히 설명하고 있습니다.

시장 동향 및 전략 보고서

현재 시장 상황을 종합적으로 분석하고 전략적 제안을 합니다.

시장 데이터 및 예측 보고서

• 부정 검사,방지 솔루션을 이용하는 은행 및 금융기관의 총수
• 은행 업무에 있어서의 부정 검사,방지에의 총 지출액
• 부정거래 총액

이러한 지표는 다음 주요 시장별로 제공됩니다.

• 은행 및 금융기관
• 신용조합
• 렌더
• 투자회사

목차

시장 동향,전략

제1장 중요 포인트,전략적 추천 사항

• 중요 포인트
• 전략적 권장 사항

제2장 시장 상황

• 정의와 범위
• 부정의 유형
  • 퍼스트 파티 부정
  • 자금세정
  • 지불 거절 부정
  • ATO
  • 합성 ID
• 발행자에 의한 부정의 검출,방지에 활용되는 솔루션
  • 부정 검출,방지 시스템
    • 생체인증
    • 토큰화
    • 행동분석
    • AML 소프트웨어

제3장 새로운 부정 시장

• 주요 테마와 관련 분야
• 주요 동향과 현재 시장 성장 촉진요인
• 지불 방법
  • 오픈뱅킹
  • BNPL
  • CBDC
  • 암호화폐
  • 실시간 결제
  • 송금
• 기술
  • AI
  • ML
  • API
• 규칙
  • 영국의 패스터 지불 규제
  • PSD2
  • RTS가 결제 서비스 제공업체에 미치는 영향

제4장 부문 분석

• 소개
  • 은행 및 신용조합
  • Fintech
  • 렌더
    • 부정피해 경감
    • 리스크 관리 강화
    • 대출 포트폴리오의 질 향상
    • 신뢰와 평판을 지키는
  • 투자회사
• 주요 과제

경쟁 리더보드

제1장 주니퍼 리서치 경쟁 리더보드

제2장 기업 프로파일

• 발행자의 부정 방지 벤더 프로파일
  • Accertify
  • ACI Worldwide
  • ComplyAdvantage
  • Discover
  • Feauturespace
  • Feedzai
  • Fiserv
  • Fraudio
  • GBG
  • LexisNexis Risk Solution
  • Mastercard
  • SEON
  • Thales
  • TransUnion
  • Visa
  • 주니퍼 리서치 리더보드 평가방법
• 제한과 해석

데이터 및 예측

제1장 시장 개요

• 정의 및 범위

제2장 조사 방법의 전제와 요약

• 예측 개요
• 조사 방법, 전제

제3장 예측 개요

• 발행자에 의한 부정 방지 예측 : 요약
  • 부정 검출,방지 솔루션을 사용하고 있는 은행 및 기타 금융기관의 수
  • 부정 검출,방지 솔루션에의 연간 총 지출
  • 뱅킹 및 송금에 있어서의 부정거래의 총수
  • 뱅킹 및 송금의 부정거래 총액

제4장 은행 및 기타 금융기관에 의한 지출

• 은행 및 신용조합의 부정 검출,방지 지출
  • 부정 검출,방지 솔루션을 사용하고 있는 은행 및 신용조합의 수
  • 은행과 신용조합에 의한 부정 검출,방지 솔루션에의 총 지출
• 핀테크의 부정 검출,방지의 지출
  • 부정 검출,방지 솔루션을 사용하고 있는 핀테크의 수
  • Fintech에 의한 부정 검출,방지 솔루션에의 총 지출
• 투자회사 부정 검출,방지 지출
  • 부정 검출,방지 솔루션을 사용하고 있는 투자 회사의 수
  • 투자회사에 의한 부정 검출,방지 솔루션에의 총 지출
• 렌더의 부정 검출,방지 지출
  • 부정 검출,방지 솔루션을 사용하고 있는 렌더의 수
  • 렌더링에 의한 부정 검출,방지 솔루션에의 총 지출

제5장 뱅킹 및 송금의 부정거래

• 디지털 뱅킹의 부정거래
  • 디지털 뱅킹의 부정거래 총수
  • 디지털 뱅킹의 부정거래 총액
• 송금의 부정거래율
  • 송금의 부정거래 총수
  • 송금의 부정거래 총액

BJH

영문 목차

영문목차

KEY STATISTICS
Spend on fraud detection and prevention solutions in 2024:	$17.3 billion
Spend on fraud detection and prevention solutions in 2029:	$32.2 billion
2024 to 2029 market growth:	86%
Forecast period:	2024-2029

Overview

Our "Fraud Detection & Prevention in Banking" research report provides a detailed evaluation and analysis of the evolving fraud landscape when it comes to banking and the financial industry, including the impact of evolving payment types such as instant payments, blockchain and CBDCs (central bank digital currencies). The fraud analytics and prevention techniques study examines other initiatives and fraudulent schemes disrupting the market, such as the changing scope of government regulations and the use of artificial intelligence by both good and bad actors.

The banking fraud detection research also considers future challenges within fraud detection and prevention for banking, such as false positives and emerging trends in the banking sector space, including the increased use of behavioural analysis and transaction monitoring.

In addition, this fraud detection and prevention market report covers fraud risks market segment opportunities; providing a comprehensive approach with strategic insights into the development of advanced methods of fraud detection and prevention capabilities for banks and other financial institutions, in line with new technologies, such as AI and machine learning.

Through advanced analytics, It highlights future opportunities and technologies that are important for fraud detection and prevention vendors, banks and financial institutions to consider when adapting fraud detection and prevention solutions for the future, incorporating advanced fraud detection aspects such as AI and real-time data.

The report positions 15 fraud detection in banking and prevention vendors in the financial services sector across the Juniper Research Competitor Leaderboard; delivering an invaluable resource for stakeholders seeking to understand the competitive landscape in the financial fraud market.

The research suite contains a detailed dataset; providing forecasts for 60 countries across a wide range of different metrics and instances of fraud, including total number of banks, credit unions, lenders and investment companies using types of fraud detection and prevention solutions, total annual spend on fraud detection and prevention solutions, total number of fraudulent transactions in banking and money transfer and total fraudulent transaction value.

Key Features

• Market Dynamics: A strategic analysis of the major drivers, challenges, and innovations shaping the adoption and development of the fraud detection and prevention in banking industry, including:
• Key Takeaways & Strategic Recommendations: In-depth analysis of key development opportunities and key findings within the fraud detection and prevention in banking market, accompanied by key strategic recommendations for stakeholders.
• Benchmark Industry Forecasts: Includes forecasts for the total money transfer for both domestic and international money movement, as well as the total money sent through consumer instant payments. This data is split by our 8 key forecast regions and 60 countries.
• Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 15 vendors in the fraud detection and fraud prevention solutions space, via a Juniper Research Competitor Leaderboard.

SAMPLE VIEW

Market Data & Forecasting Report

The numbers tell you what's happening, but our written report details why, alongside the methodologies.

Market Trends & Strategies Report

A comprehensive analysis of the current market landscape, alongside strategic recommendations.

Market Data & Forecasting Report

The market-leading research suite for the "Fraud Detection and Prevention in Banking" market includes access to the full set of forecast data of 54 tables and over 24,000 datapoints. Metrics in the research suite include:

• Total number of Banks and Financial Institutions Using Fraud Detection and Prevention Solutions
• Total Spend on Fraud Detection and Prevention in Banking
• Total Fraudulent Transaction Value

These metrics are provided for the following key market verticals:

• Banks and Financial Institutions
• Credit Unions
• Lenders
• Investment Companies

Juniper Research Interactive Forecast Excel contains the following functionality:

• Statistics Analysis: Users benefit from the ability to search for specific metrics, displayed for all regions and countries across the data period. Graphs are easily modified and can be exported to the clipboard.
• Country Data Tool: This tool lets users look at metrics for all regions and countries in the forecast period. Users can refine the metrics displayed via a search bar.
• Country Comparison Tool: Users can select and compare specific countries. The ability to export graphs is included in this tool.
• What-if Analysis: Here, users can compare forecast metrics against their own assumptions, via 5 interactive scenarios.

Market Trends & Strategies Report

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.

Competitor Leaderboard Report

Juniper Research's Competitor Leaderboard provides detailed evaluation and market positioning for 15 leading vendors in the payment fraud detection and prevention space. The vendors are positioned either as established leaders, leading challengers or disruptors and challengers, based on capacity and capability assessments.

The vendors in the Leaderboard include:

Backed by a robust and comprehensive scoring methodology, Juniper Research's Competitor Leaderboard allows readers to gain greater insight into leading market players; enabling them to view which companies have the highest market prospects and the strategies being implemented.

Table of Contents

Market Trends & Strategies

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, 2024-2029
    • 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. PSD2
  • 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 ($m), Globally, Split by 8 Key Regions, 2024-2029
  • 4.1.2. Fintechs
    • Figure 4.2: Total Number of Fintechs Using Fraud Detection and Prevention Solutions, 2024-2029
  • 4.1.3. Lenders
    • i. Reducing Exposure to Fraud
    • ii. Enhance Risk Management
    • iii. Improved Quality of Loan Portfolio
    • iv. Protect Trust and Reputation
      • Figure 4.3: Total Spend on Fraud Detection and Prevention by Lenders Globally ($m), Split by 8 Key Regions, 2024-2029
  • 4.1.4. Investment Companies
    • Figure 4.4: Total Spend on Fraud Detection and Prevention from Investment Companies ($m), Split by 8 Key Regions, 2024-2029
• 4.2. Key Challenges

Competitor Leaderboard

1. Juniper Research Competitor Leaderboard

• 1.1. Why Read This Report
  • Table 1.1: Juniper Research Competitor Leaderboard Vendors: Fraud Detection & Prevention in Banking
  • Figure 1.2: Juniper Research Competitor Leaderboard - Fraud Detection & Prevention in Banking
  • Table 1.3: Juniper Research Competitor Leaderboard: Fraud Detection & Prevention in Banking Vendor Ranking
  • Table 1.4: Juniper Research Competitor Leaderboard Fraud Detection & Prevention in Banking - Heatmap

2. Company Profiles

• 2.1. Issuer Fraud Prevention Vendor Profiles
  • 2.1.1. Accertify
    • i. Corporate Information
    • ii. Geographical Spread
    • iii. Key Clients & Strategic Partnerships
    • iv. High-level View of Offering
      • Figure 2.1: Accertify Financial Institution Fraud Prevention Solution
    • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.1.2. ACI Worldwide
    • i. Corporate
    • ii. Geographical Spread
    • iii. Key Clients & Strategic Partnerships
    • iv. High-level View of Offering
    • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.1.3. ComplyAdvantage
    • 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
  • 2.1.4. Discover
    • 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
  • 2.1.5. Feauturespace
    • 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
  • 2.1.6. Feedzai
    • 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
  • 2.1.7. Fiserv
    • 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
  • 2.1.8. Fraudio
    • 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
  • 2.1.9. GBG
    • 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
  • 2.1.10. LexisNexis Risk Solution
    • 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
  • 2.1.11. Mastercard
    • 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
  • 2.1.12. SEON
    • 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
  • 2.1.13. Thales
    • i. Corporate
    • ii. Geographical Spread
    • iii. Key Clients & Strategic Partnerships
    • iv. High-level View of Offering
    • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.1.14. TransUnion
    • i. Corporate
    • ii. Geographical Spread
    • iii. Key Clients & Strategic Partnerships
    • iv. High-level View of Offering
    • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.1.15. Visa
    • i. Corporate
    • ii. Geographical Spread
    • iii. Key Clients & Strategic Partnerships
    • iv. High-level View of Offering
    • v. Juniper Research's View: Key Strengths & Strategic Opportunities
• 2.2. Juniper Research Leaderboard Assessment Methodology
  • 2.2.1. Limitations & Interpretations
    • Table 2.4: Juniper Research Fraud Detection & Prevention in Banking Assessment Criteria

Data & Forecasting

1. Market Overview

• 1.1. Introduction
• 1.2. Definitions and Scope
  • Figure 1.1: Visualisation of Fraud

2. Methodology Assumptions and Summary

• 2.1. Forecast Introduction
• 2.2. Methodology & Assumptions
  • Figure 2.1: Spend on Fraud Detection & Prevention Methodology
  • Figure 2.2: Fraudulent Transaction Value Forecast

3. Forecast Summary

• 3.1. Issuer Fraud Prevention Forecast Summary
  • 3.1.1. Number of Bank and Other Financial Institutions Using Fraud Detection & Prevention Solutions
    • Figure & Table 3.1: Total Number of Banks and Financial Institutions Using Fraud Detection & Prevention Solutions (m), Split by 8 Key Regions, 2024-2029
  • 3.1.2. Total Annual Spend on Fraud Detection & Prevention Solutions
    • Figure & Table 3.2: Total Spend on Fraud Detection & prevention Solutions by Banks and Other Financial Institutions ($m), Split by 8 Key Regions, 2024-2029
    • Table 3.3: Total Spend from Banks and Credit Unions Using Fraud Detection & Prevention Solutions ($m), Split by Company Vertical, 2024-2029
  • 3.1.3. Total Number of Fraudulent Transactions across Banking and Money Transfer
    • Figure & Table 3.4: Total Number of Fraudulent Transactions across Banking and Money Transfer (m), Split by 8 Key Regions 2024-2029
  • 3.1.4. Total Value of Fraudulent Transactions across Banking and Money Transfer
    • Figure & Table 3.5: Total Value of Fraudulent Transactions across Digital Banking and Money Transfer ($m), Split by 8 Key Regions, 2024-2029

4. Banks and Other Financial Institutions Spend

• 4.1. Banks and Credit Unions Fraud Detection & Prevention Spend
  • 4.1.1. Number of Banks and Credit Unions Using Fraud Detection & Prevention Solutions
    • Figure & Table 4.1: Total Number of Banks and Credit Unions Using Fraud Detection & Prevention Solutions (m), Split by 8 Key Regions, 2024-2029
  • 4.1.2. Total Spend by Banks and Credit Unions on Fraud Detection & Prevention Solutions
    • Figure & Table 4.2: Total Spend from Banks and Credit Unions Using Fraud Detection & Prevention Solutions ($m), Split by 8 Key Regions 2024-2029
• 4.2. Fintechs Fraud Detection & prevention Spend
  • 4.2.1. Number of Fintechs Using Fraud Detection & Prevention Solutions
    • Figure & Table 4.3: Total Number of Fintechs Using Fraud Detection & Prevention Solutions (m), Split by 8 Key Regions, 2024-2029
  • 4.2.2. Total Spend by Fintechs on Fraud Detection & Prevention Solutions
    • Figure & Table 4.4: Total Spend from Fintechs Using Fraud Detection & Prevention Solutions ($m), Split by 8 Key Regions 2024-2029
• 4.3. Investment Companies Fraud Detection & prevention Spend
  • 4.3.1. Number of Investment Companies Using Fraud Detection & Prevention Solutions
    • Figure & Table 4.5: Total Number of Investment Companies Using Fraud Detection & Prevention Solutions (m), Split by 8 Key Regions, 2024-2029
  • 4.3.2. Total Spend by Investment Companies on Fraud Detection & Prevention Solutions
    • Figure & Table 4.6: Total Spend from Investment Companies Using Fraud Detection & Prevention Solutions ($m), Split by 8 Key Regions, 2024-2029
• 4.4. Lenders Fraud Detection & prevention Spend
  • 4.4.1. Number of Lenders Using Fraud Detection & Prevention Solutions
    • Figure & Table 4.7: Total Number of Lenders Using Fraud Detection & Prevention Solutions (m), Split by 8 Key Regions, 2024-2029
  • 4.4.2. Total Spend by Lenders on Fraud Detection & Prevention Solutions
    • Figure & Table 4.8: Total Spend from Lenders Using Fraud Detection & Prevention Solutions ($m), Split by 8 Key Regions, 2024-2029

5. Fraudulent Transactions in Banking and Money Transfer

• 5.1. Fraudulent Transactions in Digital Banking
  • 5.1.1. Total Number of Fraudulent Transactions in Digital Banking
    • Figure & Table 5.1: Total Number of Fraudulent Transactions within Digital Banking (m), Split by 8 Key Regions, 2024-2029
  • 5.1.2. Total Fraudulent Transaction Values in Digital Banking
    • Figure & Table 5.2: Total Fraudulent Transaction Values in Digital Banking ($m), Split by 8 Key Regions, 2024-2029
• 5.2. Fraudulent Transaction Rates in Money Transfer
  • 5.2.1. Total Number of Fraudulent Transactions in Money Transfer
    • Figure & Table 5.3: Total Number of Fraudulent Transactions within Money Transfer (m), Split by 8 Key Regions, 2024-2029
  • 5.2.2. Total Fraudulent Transaction Values in Money Transfer
    • Figure & Table 5.4: Total Fraudulent Transaction Values in Money Transfer ($m), Split by 8 Key Regions, 2024-2029

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