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市場調査レポート
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1494937

銀行業向け不正検知・防止の世界市場：2024-2029年

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

出版日
ページ情報
英文
納期
即日から翌営業日
価格
価格表記: GBPを日本円(税抜)に換算
本日の銀行送金レート: 1GBP=196.98円
銀行業向け不正検知・防止の世界市場：2024-2029年
出版日: 2024年06月17日
発行: Juniper Research Ltd
ページ情報: 英文
納期: 即日から翌営業日
GIIご利用のメリット
  • 全表示
  • 概要
  • 目次
概要
主要統計
不正検知・防止ソリューションへの支出額 (2024年)： 173億米ドル
不正検知・防止ソリューションへの支出額 (2029年)： 322億米ドル
市場成長率 (2024～2029年) 86%
予測期間： 2024-2029年

当レポートでは、銀行業向け不正検知・防止の市場を調査し、進化する不正の状況の詳細な評価と分析、法規制環境、不正の検出・防止に活用される技術・ソリューション、市場成長推進因子と課題、不正取引の件数・被害額、各種金融機関による不正検出・防止ソリューションへの年間総支出、地域別の詳細分析、主要ベンダーの競合リーダーボードなどをまとめています。

サンプルビュー

市場データ・予測レポート

Juniper Researchの市場データ＆予測レポートは、数字で現状を把握するだけでなく、その理由を調査手法とともに詳しく解説しています。

市場動向＆戦略レポート

現在の市場情勢を包括的に分析し、戦略的な提言を行います。

市場データ＆予測レポート

  • 不正検知・防止ソリューションを利用する銀行・金融機関の総数
  • 銀行業務における不正検知・防止への総支出額
  • 不正取引総額

これらの指標は、以下の主要市場別で提供されています：

  • 銀行および金融機関
  • 信用組合
  • レンダー
  • 投資会社

目次

市場動向・戦略

第1章 重要ポイント・戦略的推奨事項

  • 重要ポイント
  • 戦略的推奨事項

第2章 市場情勢

  • 定義と範囲
  • 不正の種類
    • ファーストパーティ不正
    • 資金洗浄
    • チャージバック不正
    • ATO
    • 合成ID
  • 発行者による不正の検出・防止に活用されるソリューション
    • 不正検出・防止システム
      • 生体認証
      • トークン化
      • 行動分析
      • AMLソフトウェア

第3章 新たな不正市場

  • 主なテーマと関連分野
  • 主な動向と現在の市場促進要因
  • 支払い方法
    • オープンバンキング
    • BNPL
    • CBDC
    • 暗号通貨
    • リアルタイム決済
    • 送金
  • 技術
    • AI
    • ML
    • API
  • 規則
    • 英国のファスターペイメント規制
    • PSD2
    • RTSが決済サービスプロバイダーに与える影響

第4章 セグメント分析

  • イントロダクション
    • 銀行および信用組合
    • フィンテック
    • レンダー
      • 不正被害の軽減
      • リスク管理の強化
      • 融資ポートフォリオの質の向上
      • 信頼と評判を守る
    • 投資会社
  • 主な課題

競合リーダーボード

第1章 Juniper Researchの競合リーダーボード

第2章 企業プロファイル

  • 発行者の不正防止ベンダープロファイル
    • Accertify
    • ACI Worldwide
    • ComplyAdvantage
    • Discover
    • Feauturespace
    • Feedzai
    • Fiserv
    • Fraudio
    • GBG
    • LexisNexis Risk Solution
    • Mastercard
    • SEON
    • Thales
    • TransUnion
    • Visa
    • Juniper Researchリーダーボード評価手法
  • 制限と解釈

データ・予測

第1章 市場概要

  • 定義・範囲

第2章 調査手法の前提と要約

  • 予測概要
  • 調査手法・前提

第3章 予測の概要

  • 発行者による不正防止の予測：サマリー
    • 不正検出・防止ソリューションを使用している銀行およびその他の金融機関の数
    • 不正検出・防止ソリューションへの年間総支出
    • バンキングおよび送金における不正取引の総数
    • バンキングおよび送金における不正取引の総額

第4章 銀行やその他の金融機関による支出

  • 銀行および信用組合の不正検出・防止の支出
    • 不正検出・防止ソリューションを使用している銀行および信用組合の数
    • 銀行と信用組合による不正検出・防止ソリューションへの総支出
  • フィンテックの不正検出・防止の支出
    • 不正検出・防止ソリューションを使用しているフィンテックの数
    • フィンテックによる不正検出・防止ソリューションへの総支出
  • 投資会社不正検出・防止支出
    • 不正検出・防止ソリューションを使用している投資会社の数
    • 投資会社による不正検出・防止ソリューションへの総支出
  • レンダーの不正検出・防止支出
    • 不正検出・防止ソリューションを使用しているレンダーの数
    • レンダーによる不正検出・防止ソリューションへの総支出

第5章 バンキングおよび送金における不正取引

  • デジタルバンキングにおける不正取引
    • デジタルバンキングにおける不正取引総数
    • デジタルバンキングにおける不正取引総額
  • 送金における不正取引率
    • 送金における不正取引総数
    • 送金における不正取引総額
目次
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:

  • Accertify
  • ACI Worldwide
  • Comply Advantage
  • Discover
  • Featurespace
  • Feedzai
  • Fiserv
  • Fraudio
  • GBG
  • LexisNexis Risk Solutions
  • Mastercard
  • SEON
  • Thales
  • TransUnion
  • Visa

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