Fraud Management in Banking Market 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), Custome

Fraud Management in Banking Market 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): Global Opportunity Analysis and Industry Forecast, 2023-2032

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.

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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