Summary

Fraud management in banking refers to the processes, strategies, and technologies implemented by financial institutions to detect, prevent, and mitigate fraudulent activities and unauthorized transactions. Moreover, fraud management in banking helps to implement measures to deter fraud before it occurs, such as robust customer authentication, access controls, and fraud prevention policies. This includes educating customers about security best practices. Furthermore, fraudsters often keep changing their intrusion methods by using a combination of new and conventional methods to escape any detection traps. They also make sure to avoid repeating the processes of intrusion. Hence, advanced fraud management solutions should be capable of monitoring the data in real-time and analysing the historical data to identify the pattern of attacks. DataVisor’s Unsupervised Machine Learning (UML) Engine is one of the solution that can potentially identify the correlated group of malicious users sharing similar attributes. The traditional approach of fraud management typically includes rule-based and supervised machine learning that constantly updates the model and is found to be less effective in detecting various sophisticated fraudulent attacks. Hence, updated fraud management solutions based on the latest technologies are being aggressively adopted to arrest financial and reputational losses for individuals and enterprises. Although business intelligence (BI) tools have been used conventionally to summarize the large volumes of data churned every quarter in any business, these conventional analytics methods cannot provide in-depth insights on a granular level to predict possible outcomes. Hence, advanced analytics tools, such as predictive analytics, data mining, big data analytics, and machine learning, are increasingly adopted by financial institutions to examine large volumes of data and extract patterns showcasing various trends across various industries on regional and global levels. These tools are based on a mathematical approach to interpreting data and machine learning techniques, such as deep learning, to identify patterns, form correlations, and group the data sets. Arranging the historical data in an identifiable pattern allows enterprises to counter foreseeable threats by triggering required actions leveraging fraud management solutions.

The rise in the adoption of online banking applications & mobile banking services and the increase in incidences of financial fraud boost the growth of global fraud management in the banking market. In addition, the increase in the use of digital transformation technology positively impacts growth of the fraud management in the banking market. However, the rise in incidents of false positive rates and growing fraud complexity hamper fraud management in banking market growth. On the contrary, the rise in innovations in the fintech industry is expected to offer remunerative opportunities for the expansion of fraud management in the banking market during the forecast period.
The fraud management in the banking market is segmented on the basis of component, fraud type, application, and region. On the basis of component, the market is bifurcated into solution and service. On the basis of fraud type, the market is categorized into payment fraud, loan fraud, identity theft, money laundering, and others. By application, it is classified into fraud detection & prevention systems, identity & access management (IAM), customer authentication, transaction monitoring, and others. By region, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The key players that operate in the fraud management in banking market are IBM Corporation, SAS Institute Inc, SAP SE, NICE Actimize, ACI Worldwide Inc, Experian PLC, BAE Systems, FIS global, LexisNexis Risk Solutions, and BioCatch Ltd. These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

Key Benefits for Stakeholders
●The study provides an in-depth analysis of the global fraud management in banking market forecast along with the current and future trends to explain the imminent investment pockets.
●Information about key drivers, restraints, and opportunities and their impact analysis on global fraud management in banking market trend is provided in the report.
●The Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
●The quantitative analysis of the market from 2023 to 2032 is provided to determine the market potential.

Additional benefits you will get with this purchase are:
● Quarterly Update and* (only available with a corporate license, on listed price)
● 5 additional Company Profile of client Choice pre- or Post-purchase, as a free update.
● Free Upcoming Version on the Purchase of Five and Enterprise User License.
● 16 analyst hours of support* (post-purchase, if you find additional data requirements upon review of the report, you may receive support amounting to 16 analyst hours to solve questions, and post-sale queries)
● 15% Free Customization* (in case the scope or segment of the report does not match your requirements, 15% is equivalent to 3 working days of free work, applicable once)
● Free data Pack on the Five and Enterprise User License. (Excel version of the report)
● Free Updated report if the report is 6-12 months old or older.
● 24-hour priority response*
● Free Industry updates and white papers.

Possible Customization with this report (with additional cost and timeline, please talk to the sales executive to know more)
● End user preferences and pain points
● Industry life cycle assessment, by region
● Product Life Cycles
● Go To Market Strategy
● New Product Development/ Product Matrix of Key Players
● Regulatory Guidelines
● Strategic Recommedations
● Expanded list for Company Profiles
● Historic market data
● SWOT Analysis
● Volume Market Size and Forecast
Key Market Segments
By Component
● Solution
● Service
By Fraud Type
● Payment Fraud
● Loan Fraud
● Identity Theft
● Money Laundering
● Others
By Application
● Fraud Detection and Prevention Systems
● Identity and Access Management (IAM)
● Customer Authentication
● Transaction Monitoring
● Others
By Region
● North America
○ U.S.
○ Canada
● Europe
○ UK
○ Germany
○ France
○ Italy
○ Spain
○ Rest of Europe
● Asia-Pacific
○ China
○ Japan
○ India
○ South Korea
○ Australia
○ Rest of Asia-Pacific
● LAMEA
○ Latin America
○ Middle East
○ Africa
● Key Market Players
○ SAS Institute Inc
○ SAP SE
○ ACI Worldwide Inc
○ BAE Systems
○ FIS Global
○ NICE Actimize
○ Experian PLC
○ BioCatch Ltd.
○ IBM Corporation
○ LexisNexis Risk Solutions

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Table of Contents

CHAPTER 1: INTRODUCTION
1.1. Report description
1.2. Key market segments
1.3. Key benefits to the stakeholders
1.4. Research methodology
1.4.1. Primary research
1.4.2. Secondary research
1.4.3. Analyst tools and models
CHAPTER 2: EXECUTIVE SUMMARY
2.1. CXO Perspective
CHAPTER 3: MARKET OVERVIEW
3.1. Market definition and scope
3.2. Key findings
3.2.1. Top impacting factors
3.2.2. Top investment pockets
3.3. Porter’s five forces analysis
3.3.1. Low bargaining power of suppliers
3.3.2. Low threat of new entrants
3.3.3. Low threat of substitutes
3.3.4. Low intensity of rivalry
3.3.5. Low bargaining power of buyers
3.4. Market dynamics
3.4.1. Drivers
3.4.1.1. Growing adoption of online banking applications and mobile banking services
3.4.1.2. Increase in incidences of financial fraud
3.4.1.3. Increase in use of digital transformation technology
3.4.2. Restraints
3.4.2.1. Growing incidents of false positive rates
3.4.2.2. Rise in fraud complexity
3.4.3. Opportunities
3.4.3.1. Rise in Innovations in the Fintech Industry
CHAPTER 4: FRAUD MANAGEMENT IN BANKING MARKET, BY COMPONENT
4.1. Overview
4.1.1. Market size and forecast
4.2. Solution
4.2.1. Key market trends, growth factors and opportunities
4.2.2. Market size and forecast, by region
4.2.3. Market share analysis by country
4.3. Service
4.3.1. Key market trends, growth factors and opportunities
4.3.2. Market size and forecast, by region
4.3.3. Market share analysis by country
CHAPTER 5: FRAUD MANAGEMENT IN BANKING MARKET, BY FRAUD TYPE
5.1. Overview
5.1.1. Market size and forecast
5.2. Payment Fraud
5.2.1. Key market trends, growth factors and opportunities
5.2.2. Market size and forecast, by region
5.2.3. Market share analysis by country
5.3. Loan Fraud
5.3.1. Key market trends, growth factors and opportunities
5.3.2. Market size and forecast, by region
5.3.3. Market share analysis by country
5.4. Identity Theft
5.4.1. Key market trends, growth factors and opportunities
5.4.2. Market size and forecast, by region
5.4.3. Market share analysis by country
5.5. Money Laundering
5.5.1. Key market trends, growth factors and opportunities
5.5.2. Market size and forecast, by region
5.5.3. Market share analysis by country
5.6. Others
5.6.1. Key market trends, growth factors and opportunities
5.6.2. Market size and forecast, by region
5.6.3. Market share analysis by country
CHAPTER 6: FRAUD MANAGEMENT IN BANKING MARKET, BY APPLICATION
6.1. Overview
6.1.1. Market size and forecast
6.2. Fraud Detection and Prevention Systems
6.2.1. Key market trends, growth factors and opportunities
6.2.2. Market size and forecast, by region
6.2.3. Market share analysis by country
6.3. Identity and Access Management (IAM)
6.3.1. Key market trends, growth factors and opportunities
6.3.2. Market size and forecast, by region
6.3.3. Market share analysis by country
6.4. Customer Authentication
6.4.1. Key market trends, growth factors and opportunities
6.4.2. Market size and forecast, by region
6.4.3. Market share analysis by country
6.5. Transaction Monitoring
6.5.1. Key market trends, growth factors and opportunities
6.5.2. Market size and forecast, by region
6.5.3. Market share analysis by country
6.6. Others
6.6.1. Key market trends, growth factors and opportunities
6.6.2. Market size and forecast, by region
6.6.3. Market share analysis by country
CHAPTER 7: FRAUD MANAGEMENT IN BANKING MARKET, BY REGION
7.1. Overview
7.1.1. Market size and forecast By Region
7.2. North America
7.2.1. Key market trends, growth factors and opportunities
7.2.2. Market size and forecast, by Component
7.2.3. Market size and forecast, by Fraud Type
7.2.4. Market size and forecast, by Application
7.2.5. Market size and forecast, by country
7.2.5.1. U.S.
7.2.5.1.1. Market size and forecast, by Component
7.2.5.1.2. Market size and forecast, by Fraud Type
7.2.5.1.3. Market size and forecast, by Application
7.2.5.2. Canada
7.2.5.2.1. Market size and forecast, by Component
7.2.5.2.2. Market size and forecast, by Fraud Type
7.2.5.2.3. Market size and forecast, by Application
7.3. Europe
7.3.1. Key market trends, growth factors and opportunities
7.3.2. Market size and forecast, by Component
7.3.3. Market size and forecast, by Fraud Type
7.3.4. Market size and forecast, by Application
7.3.5. Market size and forecast, by country
7.3.5.1. UK
7.3.5.1.1. Market size and forecast, by Component
7.3.5.1.2. Market size and forecast, by Fraud Type
7.3.5.1.3. Market size and forecast, by Application
7.3.5.2. Germany
7.3.5.2.1. Market size and forecast, by Component
7.3.5.2.2. Market size and forecast, by Fraud Type
7.3.5.2.3. Market size and forecast, by Application
7.3.5.3. France
7.3.5.3.1. Market size and forecast, by Component
7.3.5.3.2. Market size and forecast, by Fraud Type
7.3.5.3.3. Market size and forecast, by Application
7.3.5.4. Italy
7.3.5.4.1. Market size and forecast, by Component
7.3.5.4.2. Market size and forecast, by Fraud Type
7.3.5.4.3. Market size and forecast, by Application
7.3.5.5. Spain
7.3.5.5.1. Market size and forecast, by Component
7.3.5.5.2. Market size and forecast, by Fraud Type
7.3.5.5.3. Market size and forecast, by Application
7.3.5.6. Rest of Europe
7.3.5.6.1. Market size and forecast, by Component
7.3.5.6.2. Market size and forecast, by Fraud Type
7.3.5.6.3. Market size and forecast, by Application
7.4. Asia-Pacific
7.4.1. Key market trends, growth factors and opportunities
7.4.2. Market size and forecast, by Component
7.4.3. Market size and forecast, by Fraud Type
7.4.4. Market size and forecast, by Application
7.4.5. Market size and forecast, by country
7.4.5.1. China
7.4.5.1.1. Market size and forecast, by Component
7.4.5.1.2. Market size and forecast, by Fraud Type
7.4.5.1.3. Market size and forecast, by Application
7.4.5.2. Japan
7.4.5.2.1. Market size and forecast, by Component
7.4.5.2.2. Market size and forecast, by Fraud Type
7.4.5.2.3. Market size and forecast, by Application
7.4.5.3. India
7.4.5.3.1. Market size and forecast, by Component
7.4.5.3.2. Market size and forecast, by Fraud Type
7.4.5.3.3. Market size and forecast, by Application
7.4.5.4. South Korea
7.4.5.4.1. Market size and forecast, by Component
7.4.5.4.2. Market size and forecast, by Fraud Type
7.4.5.4.3. Market size and forecast, by Application
7.4.5.5. Australia
7.4.5.5.1. Market size and forecast, by Component
7.4.5.5.2. Market size and forecast, by Fraud Type
7.4.5.5.3. Market size and forecast, by Application
7.4.5.6. Rest of Asia-Pacific
7.4.5.6.1. Market size and forecast, by Component
7.4.5.6.2. Market size and forecast, by Fraud Type
7.4.5.6.3. Market size and forecast, by Application
7.5. LAMEA
7.5.1. Key market trends, growth factors and opportunities
7.5.2. Market size and forecast, by Component
7.5.3. Market size and forecast, by Fraud Type
7.5.4. Market size and forecast, by Application
7.5.5. Market size and forecast, by country
7.5.5.1. Latin America
7.5.5.1.1. Market size and forecast, by Component
7.5.5.1.2. Market size and forecast, by Fraud Type
7.5.5.1.3. Market size and forecast, by Application
7.5.5.2. Middle East
7.5.5.2.1. Market size and forecast, by Component
7.5.5.2.2. Market size and forecast, by Fraud Type
7.5.5.2.3. Market size and forecast, by Application
7.5.5.3. Africa
7.5.5.3.1. Market size and forecast, by Component
7.5.5.3.2. Market size and forecast, by Fraud Type
7.5.5.3.3. Market size and forecast, by Application
CHAPTER 8: COMPETITIVE LANDSCAPE
8.1. Introduction
8.2. Top winning strategies
8.3. Product mapping of top 10 player
8.4. Competitive dashboard
8.5. Competitive heatmap
8.6. Top player positioning, 2022
CHAPTER 9: COMPANY PROFILES
9.1. IBM Corporation
9.1.1. Company overview
9.1.2. Key executives
9.1.3. Company snapshot
9.1.4. Operating business segments
9.1.5. Product portfolio
9.1.6. Business performance
9.1.7. Key strategic moves and developments
9.2. SAS Institute Inc
9.2.1. Company overview
9.2.2. Key executives
9.2.3. Company snapshot
9.2.4. Operating business segments
9.2.5. Product portfolio
9.2.6. Key strategic moves and developments
9.3. SAP SE
9.3.1. Company overview
9.3.2. Key executives
9.3.3. Company snapshot
9.3.4. Operating business segments
9.3.5. Product portfolio
9.3.6. Business performance
9.3.7. Key strategic moves and developments
9.4. NICE Actimize
9.4.1. Company overview
9.4.2. Key executives
9.4.3. Company snapshot
9.4.4. Operating business segments
9.4.5. Product portfolio
9.4.6. Business performance
9.4.7. Key strategic moves and developments
9.5. ACI Worldwide Inc
9.5.1. Company overview
9.5.2. Key executives
9.5.3. Company snapshot
9.5.4. Operating business segments
9.5.5. Product portfolio
9.5.6. Key strategic moves and developments
9.6. Experian PLC
9.6.1. Company overview
9.6.2. Key executives
9.6.3. Company snapshot
9.6.4. Operating business segments
9.6.5. Product portfolio
9.6.6. Key strategic moves and developments
9.7. BAE Systems
9.7.1. Company overview
9.7.2. Key executives
9.7.3. Company snapshot
9.7.4. Operating business segments
9.7.5. Product portfolio
9.7.6. Business performance
9.8. FIS Global
9.8.1. Company overview
9.8.2. Key executives
9.8.3. Company snapshot
9.8.4. Operating business segments
9.8.5. Product portfolio
9.8.6. Key strategic moves and developments
9.9. LexisNexis Risk Solutions
9.9.1. Company overview
9.9.2. Key executives
9.9.3. Company snapshot
9.9.4. Operating business segments
9.9.5. Product portfolio
9.9.6. Business performance
9.10. BioCatch Ltd.
9.10.1. Company overview
9.10.2. Key executives
9.10.3. Company snapshot
9.10.4. Operating business segments
9.10.5. Product portfolio