Fraud Detection And Prevention Market: Regulatory Changes And Their Implications
Fraud Detection And Prevention Industry Overview
The global fraud detection and prevention market size was valued at USD 25.67 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 17.6% from 2023 to 2030. Some of the major factors driving the growth of the market include increased sophistication of cyberattacks, increased online fraud, and a growing preference for advanced analytics for the identification of threat patterns, among others. Online fraud risks have been increasing in line with the rising popularity of B2C, B2B, and C2C e-commerce transactions, which often involve a mutual exchange of critical information, identity information, and personal data of individuals and enterprises.
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Furthermore, the boom in mobile gaming and the growing use of social media also adds to the risks of subsequent fraud. While frauds are getting more sophisticated, the losses from these attacks are also increasing. An analysis of over 44 billion events across 800 million active users globally by Help Net Security revealed a rapid increase in the complexity of emerging attacks. This has triggered the need for a strong emphasis on contextual detection strategies capable of proactively thwarting intrusions before any potential losses.
Financial services remain the most vulnerable to online fraud, such as account takeovers, carried out using a complex combination of manual and bot-powered methods. Cases of social media fraud are also increasingly detected in the form of fake registrations and fake installs, which are difficult to track due to the large volumes involved. The looming glitches in detecting and preventing fraud often lead to reputational and financial losses, such as eroded trust, increased operational costs, and revenue and asset losses for individuals and enterprises. The rising complexity and the increasing number of frauds have triggered the demand for advanced fraud detection and prevention solutions that can potentially render a detailed understanding of user behavior patterns. At the same time, a 360-degree view of data can also prove effective in confronting online fraud at a time when internet services and digital data are increasing.
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 detection solutions should be capable of monitoring the data in real time and analyzing the historical data to identify the pattern of attacks. DataVisor’s Unsupervised Machine Learning (UML) Engine is one such solution that can potentially identify the correlated group of malicious users sharing similar attributes. The traditional approach of fraud detection 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 detection and prevention solutions based on the latest technologies are being aggressively adopted to arrest financial and reputational losses for individuals and enterprises.
Fraud detection and prevention rely, to a large extent, on the ability to identify threat patterns and develop suitable solutions capable of protecting enterprises from recurring attacks. Although business intelligence (BI) tools have been used conventionally to summarize the large volumes of data churned every quarter in any particular 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 preferred 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 detection and prevention solutions.
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Fraud Detection And Prevention Market Segmentation
Grand View Research has segmented the global fraud detection and prevention market based on component, solution, service, application, organization size, vertical, and region:
Fraud Detection And Prevention Component Outlook (Revenue, USD Billion, 2018 - 2030)
Solution
Service
Fraud Detection And Prevention Solution Outlook (Revenue, USD Billion, 2018 - 2030)
Fraud Analytics
Authentication
Governance, Risk and Compliance (GRC)
Fraud Detection And Prevention Service Outlook (Revenue, USD Billion, 2018 - 2030)
Professional Services
Managed Services
Fraud Detection And Prevention Application Outlook (Revenue, USD Billion, 2018 - 2030)
Identity Theft
Money Laundering
Payment Fraud
Others
Fraud Detection And Prevention Organization Size Outlook (Revenue, USD Billion, 2018 - 2030)
SMEs
Large Enterprises
Fraud Detection And Prevention Vertical Outlook (Revenue, USD Billion, 2018 - 2030)
BFSI
Government & Defense
Healthcare
IT & Telecom
Industrial & Manufacturing
Retail & E-commerce
Others
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Key Companies profiled:
ACI Worldwide, Inc.
AltexSoft
BAE Systems
Dell Inc.
Equifax, Inc.
Experian plc
Fiserv, Inc.
IBM
NICE Ltd.
Oracle
SAP SE
SAS Institute Inc.
SEON Technologies Ltd.
Signifyd
Software AG
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