Artificial Intelligence (AI) in Fraud Detection Market Component (Solution, and Services), By Application, By Organization Size, By Industry Vertical - Global Industry Outlook, Key Companies (IBM Corporation, Google LLC, SAS Institute Inc., and others), Trends and Forecast 2025-2034

 
AI in fraud detection plays a crucial role in addressing these challenges by offering dynamic and adaptive models that evolve alongside changing fraud techniques. Unlike traditional rule-based systems, AI models are capable of learning from historical data, continuously improving their detection capabilities, and reducing false positives. This market is witnessing strong growth due to heightened regulatory scrutiny and rising compliance demands placed on financial institutions and businesses. 

Governments and regulatory bodies globally are enforcing stricter guidelines to protect consumer data and ensure financial stability, making it imperative for organizations to invest in robust fraud detection technologies.

 
AI-based solutions not only help meet these compliance requirements but also enhance operational efficiency by automating fraud investigation and reducing manual intervention. The financial and reputational risks associated with fraud are substantial, prompting businesses across industries like banking, insurance, telecommunications, and retail to prioritize fraud prevention strategies. 

AI in fraud detection provides a scalable, cost-effective approach by enabling organizations to monitor millions of transactions in real time and identify potential threats with greater accuracy. This reduces financial exposure and strengthens consumer trust, a critical asset in today’s highly competitive and interconnected markets.

Additionally, advancements in AI models and the growing availability of cloud-based infrastructure are accelerating innovation within this market. Businesses can now deploy AI-powered fraud detection tools with greater ease, leveraging real-time data processing and predictive analytics to stay ahead of emerging threats. As cybercriminals continue to adopt sophisticated techniques, the demand for AI-driven solutions is expected to grow, positioning the market for sustained expansion over the coming years.

The US AI in Fraud Detection Market

The US AI in Fraud Detection Market is projected to be valued at USD 5.1 billion in 2025. It is further expected to witness subsequent growth in the upcoming period, holding USD 34.7 billion in 2034 at a CAGR of 23.8%.

 

 
The AI in Fraud Detection Market in the US refers to the growing industry segment that applies artificial intelligence technologies to identify, prevent, and mitigate fraudulent activities across key sectors such as banking, e-commerce, insurance, and healthcare. US businesses are leveraging AI-powered solutions, including machine learning, deep learning, and behavioral analytics, to process vast amounts of transactional and customer data. 

These tools are designed to detect anomalies and suspicious patterns in real time, allowing organizations to swiftly respond to fraud attempts and safeguard financial assets and customer trust. The rapid expansion of digital payment systems, mobile banking, and e-commerce in the U.S. has significantly increased the exposure to fraud-related threats. This has led to a surge in demand for AI-based fraud detection systems that can monitor and assess transaction data across multiple channels.

American enterprises are adopting AI to build more adaptive and intelligent fraud prevention frameworks that outperform traditional rule-based systems, offering quicker detection and fewer false positives by continuously learning from historical fraud patterns and user behavior. In the U.S., regulatory frameworks such as the Gramm-Leach-Bliley Act (GLBA), the Bank Secrecy Act (BSA), and data privacy laws like the California Consumer Privacy Act (CCPA) are major drivers compelling organizations to strengthen their fraud prevention capabilities. 

Financial institutions and corporations are under pressure to comply with these regulations by implementing advanced fraud detection systems capable of identifying, investigating, and reporting fraudulent activities. AI technologies are helping American businesses meet these regulatory standards while optimizing fraud management operations.

Global Artificial Intelligence (AI) in Fraud Detection Market: Key Takeaways

• Market Value: The Global Artificial Intelligence (AI) in fraud detection market size is expected to reach a value of USD 119.9 billion by 2034 from a base value of USD 15.6 billion in 2025 at a CAGR of 25.4%.
• By Component Type Segment Analysis: Solution components are poised to consolidate their dominance in the component type segment capturing 67.0% of the total market share in 2025.
• By Application Type Segment Analysis: Payment Fraud applications are expected to maintain their dominance in the application type segment capturing 49.2% of the total market share in 2025.
• By Organization Size Type Segment Analysis: Large Enterprises are anticipated to lead in the organization size segment, capturing 68.2% of the market share in 2025.
• By Industry Vertical Type Segment Analysis: BFSI is poised to consolidate its market position in the industry vertical type segment capturing 26.9% of the total market share in 2025.
• Regional Analysis: North America is anticipated to lead the Global Artificial Intelligence (AI) in fraud detection market landscape with 38.7% of total global market revenue in 2025.
• Key Players: Some key players in the Global Artificial Intelligence (AI) in fraud detection market are IBM Corporation, Google LLC, SAS Institute Inc., SAP SE, FICO, ACI Worldwide, Experian plc, Fiserv, Inc., Verisk Analytics, Inc., NICE Ltd., Veriff, Matellio Inc., and Other Key Players.

Global Artificial Intelligence (AI) in Fraud Detection Market: Use Cases

• Real-time Payment Fraud Detection in Global Banking: International banks such as HSBC and Citibank use AI to protect against real-time payment fraud, especially with the rise of instant payment systems like SWIFT gpi and SEPA. AI models continuously monitor cross-border transactions for suspicious patterns, such as sudden fund transfers to high-risk countries or accounts with no prior relationship. AI systems can quickly flag and halt suspicious transactions, reducing exposure to wire transfer fraud and money laundering.
• Insurance Claim Fraud Detection Globally: Global insurance companies like Allianz and AXA are applying AI to detect fraudulent insurance claims. AI models analyze historical claims data, customer profiles, and external data sources to identify red flags such as exaggerated losses or recurring claim patterns across different geographies. This helps insurers automate claim reviews and reduce false claims, improving operational efficiency and lowering payout costs.
• AI-based Anti-Money Laundering (AML) in Fintech and Financial Services: Fintech companies and financial institutions around the world, including in regions like Europe and Asia-Pacific, use AI-driven AML solutions to comply with international regulations such as FATF guidelines. AI systems analyze customer transactions, detect hidden relationships between accounts, and flag suspicious behavior such as structuring (smurfing) or layering transactions. These tools assist compliance teams in generating more accurate suspicious activity reports (SARs) while reducing false positives.
• AI in E-commerce and Digital Marketplace Fraud Prevention: Global e-commerce giants like Alibaba and MercadoLibre use AI to combat marketplace fraud, such as fake seller accounts, counterfeit goods, and payment fraud. AI algorithms monitor user behavior, product listings, and transactional data to identify fraudulent sellers and fake reviews. They also help detect anomalies in customer purchases, such as sudden high-value orders from new accounts or the use of stolen payment credentials, thereby securing the entire e-commerce ecosystem.

Global Artificial Intelligence (AI) in Fraud Detection Market: Stats & Facts

• According to the U.S. Federal Trade Commission (FTC), consumers reported losing over USD 10 billion to fraud in 2023, marking the highest amount ever recorded.
• According to Europol, AI-driven social engineering attacks, including deepfakes and voice cloning, were identified as emerging fraud trends in their Internet Organised Crime Threat Assessment 2023 report.
• According to the European Central Bank (ECB), payment fraud involving cards issued within SEPA dropped by 9.3% in 2022 compared to 2021, largely due to AI-based fraud detection tools.
• According to the U.S. Federal Reserve, AI and machine learning models are being increasingly deployed in real-time payment fraud detection systems, enhancing transaction monitoring effectiveness.
• According to the Monetary Authority of Singapore (MAS), AI-powered fraud detection was a key factor in reducing phishing-related financial scams by 45% between 2021 and 2023.
• According to the Reserve Bank of India (RBI), AI-based fraud detection systems contributed to a 25% reduction in online banking fraud cases in India in fiscal year 2023.
• According to the U.K.’s National Crime Agency (NCA), AI-supported analytics have played a crucial role in uncovering large-scale fraud networks across the financial and telecom sectors.
• According to the U.S. Department of Justice (DOJ), AI-enabled fraud detection tools were instrumental in identifying and prosecuting several high-profile wire fraud and cybercrime cases in 2023.
• According to the Australian Competition & Consumer Commission (ACCC), the Scamwatch platform reported that AI-based systems helped reduce consumer losses from online fraud by approximately 30% in 2023.
• According to Canada’s Financial Transactions and Reports Analysis Centre (FINTRAC), AI-enhanced systems led to a significant uptick in suspicious transaction reports related to fraud in 2023.
• According to the U.S. Government Accountability Office, federal agencies lost as much as USD 521 billion due to programmatic fraud between fiscal years 2018 and 2022.
• According to the U.S. Department of the Treasury, its enhanced fraud detection processes, including machine learning AI, prevented and recovered over USD 4 billion in fiscal year 2024. Building on this success, the U.S. Department of the Treasury's Office of Payment Integrity reported that AI recovered over USD 375 million at the beginning of Fiscal Year 2023, demonstrating the growing impact of AI-driven tools.
• Moreover, AI processes have been instrumental in addressing increased fraud and improper payments since the pandemic, showcasing their critical role in modern fraud prevention frameworks. In addition to their direct financial benefits, AI solutions are now a key recommendation by the U.S. Department of the Treasury for managing AI-specific cybersecurity risks across the financial services sector.
• The Office of Payment Integrity further emphasized that AI played a significant role in recovering over USD 375 million in the early part of Fiscal Year 2023 alone. As a result, AI technologies are increasingly being highlighted as essential components in managing cybersecurity risks and safeguarding financial institutions from evolving fraud threats.
• According to Nextgov, AI tools helped the U.S. Treasury prevent and recover over USD 4 billion in fraudulent payments in fiscal year 2024, a significant increase from USD 652.7 million in the previous year. This sharp rise highlights the growing effectiveness of AI technologies in combating financial crimes at a federal level.
• In alignment with these findings, AI tools deployed by the U.S. Treasury played a pivotal role in preventing and recovering billions in fraudulent and improper payments throughout fiscal year 2024. The application of machine learning and predictive analytics allowed for faster detection and interception of suspicious transactions.
• Additionally, AI tools at the U.S. Treasury were responsible for preventing and recovering more than USD 4 billion in fraud-related recoveries during the same period, underscoring the critical role that AI plays in securing public funds and enhancing governmental financial integrity.

Global Artificial Intelligence (AI) in Fraud Detection Market: Market Dynamic

Global Artificial Intelligence (AI) in Fraud Detection Market: Driving Factors

Proliferation of Digital Identities and IoT Devices Globally
The widespread adoption of digital identities and the rapid growth of connected devices, including smartphones, wearables, and IoT-enabled systems, is fueling the potential exposure to fraudulent activities. As consumers and businesses rely on digital platforms for authentication and transactions, AI-driven fraud detection tools are essential to monitor diverse data points from various endpoints.

Additionally, AI solutions can detect fraudulent activities across multiple touchpoints by correlating device, behavioral, and network data. This growing complexity in digital ecosystems is pushing organizations to invest in AI to protect against sophisticated fraud schemes targeting interconnected systems.

Increased Collaboration between Public and Private Sectors
Globally, governments and private enterprises are collaborating more closely to tackle financial crimes and cyber fraud. Initiatives such as public-private partnerships, information-sharing networks, and joint task forces have created a stronger emphasis on deploying AI-based fraud detection solutions. 

These alliances enable the creation of standardized frameworks and shared databases that improve the efficiency and scalability of AI-driven systems. Through this collaboration, businesses gain access to cross-industry fraud intelligence, allowing AI models to recognize emerging threats and fraud tactics that might otherwise go unnoticed within individual organizations.

Global Artificial Intelligence (AI) in Fraud Detection Market: Restraints

High Implementation and Integration Costs
Many organizations, particularly small and mid-sized enterprises (SMEs) and firms operating in developing economies struggle to justify the upfront costs associated with AI adoption. Beyond purchasing AI tools, companies often face the challenge of upgrading legacy infrastructure, integrating AI platforms with disparate internal systems, and ensuring interoperability with existing fraud management workflows. 

Moreover, AI implementation requires specialized expertise, such as data scientists and machine learning engineers, which adds to operational expenses. Ongoing costs related to model maintenance, performance optimization, and cybersecurity enhancements further strain financial resources. These barriers can lead to a slower adoption curve in sectors with limited digital maturity, hindering the market’s ability to achieve rapid, widespread deployment across all regions and industries.

Data Privacy Concerns and Regulatory Challenges
Laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and other local data protection frameworks limit how organizations collect, process, and store sensitive customer data. AI models require large datasets to detect complex fraud patterns accurately, but restrictions on data sharing, consent management, and cross-border data transfers significantly reduce the volume and variety of data available for training and monitoring.
Additionally, companies must navigate regulatory uncertainty, as evolving privacy laws continue to tighten control over data usage practices. Compliance risks and potential penalties force businesses to adopt conservative approaches, often limiting the effectiveness of AI systems.

Global Artificial Intelligence (AI) in Fraud Detection Market: Opportunities

Growing Adoption of AI in Emerging Economies
As financial inclusion accelerates in these regions, there is a sharp rise in digital payments, online banking, and mobile commerce, making them more susceptible to cyber threats and fraudulent activities.

With a surge in internet penetration and smartphone usage, businesses and financial institutions in these markets are seeking advanced technologies to secure transactions and protect customers. AI-based fraud detection tools offer scalable and cost-effective solutions that can adapt to local fraud trends and behavioral patterns. 

Furthermore, governments and regulatory bodies in emerging economies are prioritizing cybersecurity investments and digital trust frameworks, creating a fertile ground for AI vendors to expand their footprint and offer customized fraud detection services designed for these dynamic markets.

Advancements in Explainable AI (XAI) and Transparent Algorithms
The rise of Explainable AI (XAI) is creating new avenues for AI in fraud detection, particularly in industries such as banking, insurance, and healthcare, where regulatory transparency and auditability are essential. Traditional AI models, especially deep learning systems, often function as "black boxes" with limited visibility into how fraud detection decisions are made, which can limit their adoption in highly regulated sectors. 

However, the development of XAI technologies is addressing this issue by enabling organizations to better interpret and explain AI-driven fraud alerts to regulators, internal compliance teams, and customers. This growing focus on transparency and interpretability is helping to bridge the trust gap between AI tools and human decision-makers. As XAI solutions mature, companies will be more inclined to adopt AI-based fraud detection platforms that meet both operational and regulatory requirements while providing clear justifications for flagged transactions or fraudulent activities.

Global Artificial Intelligence (AI) in Fraud Detection Market: Trends

Integration of AI with Blockchain for Enhanced Fraud Prevention

While AI excels at identifying anomalies and suspicious patterns in real time, blockchain provides an immutable and decentralized ledger that reduces the risk of data tampering and unauthorized access.
Organizations across sectors such as finance, supply chain, and insurance are experimenting with AI-blockchain integration to strengthen their fraud detection frameworks. 

This synergy allows AI systems to analyze and verify transaction records on decentralized ledgers, enhancing trust and reducing the time required to investigate fraudulent activities. The combination of AI’s predictive capabilities with blockchain’s security and transparency is expected to gain traction as businesses seek to modernize fraud prevention practices and improve data integrity.

Rising Adoption of AI-Powered Behavioral Biometrics
Unlike traditional biometrics that rely on static identifiers such as fingerprints or facial recognition, behavioral biometrics use AI to analyze unique user behaviors, including typing speed, mouse movements, swipe patterns, and device usage habits. This non-intrusive method allows AI systems to continuously authenticate users in real time and detect account takeovers or fraudulent access attempts with greater accuracy. 

Companies are integrating behavioral biometrics into multi-layered security architectures to complement other fraud prevention tools. As fraudsters develop more sophisticated social engineering and identity theft techniques, AI-powered behavioral biometrics are becoming a vital part of adaptive authentication strategies globally.

Global Artificial Intelligence (AI) in Fraud Detection Market: Research Scope and Analysis

By Component

Solution components are expected to solidify their leadership within the component type segment, accounting for approximately 67.0% of the total market share by 2025. The dominance of solution components can be attributed to the growing demand for advanced fraud detection platforms that incorporate cutting-edge AI technologies such as machine learning, deep learning, and natural language processing. 

These AI-powered solutions are being widely adopted across banking, insurance, e-commerce, and telecom sectors to identify, monitor, and prevent fraudulent activities in real time.
With the proliferation of digital transactions, financial crimes are becoming sophisticated, prompting enterprises to prioritize robust fraud detection solutions that can automate the detection process and reduce false positives. These solutions provide key functionalities such as anomaly detection, transaction monitoring, customer behavior analysis, and fraud scoring models, helping businesses mitigate financial losses while improving compliance and operational efficiency.

While solution components dominate the market, service components also play a crucial and complementary role in driving the overall growth of the fraud detection ecosystem. Services include consulting, system integration, training, support, and managed services that help organizations seamlessly deploy and optimize AI-based fraud detection platforms. In many cases, businesses, particularly those with complex or legacy infrastructures, require specialized expertise to tailor AI solutions to their specific risk profiles and regulatory environments. 

Additionally, ongoing services such as model tuning, real-time monitoring, and threat intelligence updates are essential for maintaining the performance and adaptability of fraud detection systems in the face of evolving cyber threats. The growing reliance on external service providers, integrated with the trend toward outsourcing fraud detection operations, is expected to further boost the services segment, ensuring long-term system effectiveness and customer trust across industries.

By Application

Payment fraud applications are projected to retain their dominant position within the application type segment, securing approximately 49.2% of the total market share by 2025. The rising use of digital and real-time payments across sectors such as banking, retail, e-commerce, and fintech is driving the need for advanced fraud prevention solutions that can safeguard payment ecosystems. 

The growing popularity of online shopping, mobile wallets, peer-to-peer payment platforms, and contactless transactions has led to an expanded attack surface, exposing both consumers and businesses to sophisticated payment fraud schemes such as card-not-present (CNP) fraud, phishing, account takeovers, and fake merchant fraud. 

AI-driven payment fraud detection tools leverage machine learning algorithms to analyze transaction patterns, customer behavior, and contextual data in real time to flag anomalies and unauthorized transactions, allowing organizations to minimize financial losses and reduce the reputational damage associated with payment fraud incidents.

Identity fraud detection applications also play a critical role in strengthening the overall fraud prevention landscape. As more organizations rely on digital onboarding and remote authentication, the threat of identity fraud has surged globally. AI-powered identity fraud detection applications leverage advanced techniques such as biometric analysis, behavioral analytics, and document verification to accurately verify user identities and detect suspicious activities. 

These systems can identify inconsistencies in customer data, detect deepfake attempts, and flag synthetic identities that are often used to open fraudulent accounts or access sensitive information. In sectors like banking, healthcare, and telecom, identity fraud detection solutions are becoming an integral part of multi-factor authentication frameworks, helping organizations comply with regulatory requirements such as Know Your Customer (KYC) and Anti-Money Laundering (AML) mandates.

By Organization Size

Large enterprises are expected to dominate the organization size segment within the Global Artificial Intelligence (AI) in fraud detection market, capturing approximately 68.2% of the total market share by 2025. The dominance of large enterprises in this segment is driven by their significant financial resources, higher transaction volumes, and complex operational structures that make them more susceptible to diverse and large-scale fraud threats. 

These organizations, which include multinational banks, global insurance providers, and major retail and e-commerce corporations, often operate in multiple jurisdictions and handle vast amounts of sensitive customer and financial data. As a result, they require sophisticated AI-powered fraud detection platforms capable of processing and analyzing enormous datasets in real time to prevent financial crimes and minimize operational risks. 

Additionally, large enterprises have well-established IT infrastructures and dedicated risk management teams, allowing them to implement cutting-edge AI technologies that can automate fraud detection workflows and reduce dependency on manual intervention.

Moreover, small and medium enterprises (SMEs) also play a growing and crucial role in the fraud detection market, despite representing a smaller share of the segment. SMEs are becoming targets for fraudsters due to their relatively limited cybersecurity defenses and lack of in-house expertise. To address these vulnerabilities, SMEs are adopting AI-based fraud detection tools that are scalable, cost-effective, and offered via cloud-based platforms or fraud detection-as-a-service models. 

These solutions provide SMEs with access to advanced AI capabilities, such as automated threat detection and real-time risk scoring, without the need for heavy infrastructure investments. Furthermore, as SMEs embrace digital transformation and expand into online sales, mobile payments, and remote customer onboarding, the need for AI-enabled fraud detection systems has become more urgent. Vendors are developing AI fraud detection solutions to meet the specific requirements and budget constraints of SMEs, helping them enhance security, build customer trust, and compete with larger organizations in the global digital economy.

By Industry Vertical

The BFSI (Banking, Financial Services, and Insurance) sector is set to solidify its leadership position in the industry vertical type segment, accounting for approximately 26.9% of the global market share by 2025. The BFSI industry has always been a prime target for financial crimes, including payment fraud, identity theft, account takeovers, and money laundering, which makes fraud prevention a strategic priority for financial institutions globally. With the growing volume of digital transactions, online banking activities, and real-time payment systems, BFSI organizations are adopting AI-powered fraud detection platforms to safeguard customer assets and ensure regulatory compliance. 

AI-based solutions in BFSI not only provide real-time transaction monitoring and risk assessment but also leverage machine learning algorithms to detect anomalies in customer behavior and flag high-risk activities.

Alongside BFSI, the IT and telecommunications industry is emerging as a vital player in the AI in fraud detection market, driven by its role as both a service provider and a target for fraud-related risks. Telecom companies, in particular, face growing challenges related to subscription fraud, SIM swap attacks, and account manipulation, which threaten customer security and corporate revenues. 

The massive customer base and the high frequency of digital transactions in telecom operations create an environment where fraudsters actively exploit vulnerabilities in customer identity verification and transaction processes. In response, IT and telecom firms are deploying AI-driven fraud detection platforms that can analyze customer behavior patterns, device information, and network activities in real time to identify suspicious activities and prevent fraud incidents before they escalate. Moreover, telecom companies are leveraging AI-powered identity verification tools to secure customer onboarding processes and enhance fraud prevention in mobile payment and digital wallet ecosystems.

The AI in Fraud Detection Market Report is segmented on the basis of the following:

By Component

• Solution
• Services

By Application

• Payment Fraud
• Identity Fraud
• Insurance Fraud
• Money Laundering
• Others

By Organization Size

• Large Enterprises
• Small and Medium-Sized Enterprises

By Industry Vertical

• BFSI
• IT and Telecommunications
• Healthcare
• Manufacturing
• Retail and E-commerce
• Government and Public Sector
• Others

Global Artificial Intelligence (AI) in Fraud Detection Market: Regional Analysis

Region with the Largest Revenue Share

North America is projected to dominate the Global Artificial Intelligence (AI) in fraud detection market, accounting for approximately 38.7% of total global market revenue by 2025. The region’s dominance is primarily driven by its highly developed financial services sector, advanced digital infrastructure, and the early adoption of cutting-edge AI technologies across industries. 

Major economies such as the United States and Canada are home to some of the world’s largest banking institutions, fintech firms, e-commerce platforms, and technology providers, all of which face exposure to complex cyber threats and sophisticated fraud schemes. The rapid shift toward digital payments, online banking, and mobile transactions has heightened the demand for AI-powered fraud detection systems that offer real-time monitoring, predictive analytics, and automated threat identification. 

Additionally, North America has one of the most stringent regulatory landscapes globally, with laws such as the Gramm-Leach-Bliley Act (GLBA), Payment Card Industry Data Security Standard (PCI DSS), and Anti-Money Laundering (AML) regulations requiring organizations to implement robust fraud prevention measures.

Region with the Highest CAGR

Asia Pacific is expected to record the highest compound annual growth rate (CAGR) in the Global Artificial Intelligence (AI) fraud detection market during the forecast period. This robust growth trajectory is fueled by the rapid digital transformation taking place across key economies such as China, India, Japan, Australia, and Southeast Asian countries. 

The accelerated adoption of online banking, digital wallets, e-commerce, and mobile payment platforms across the region has significantly expanded the digital transaction ecosystem, leading to a corresponding rise in cyber threats and fraudulent activities. As millions of consumers and businesses migrate to digital channels, the risk of fraud including identity theft, phishing scams, and payment fraud has surged, prompting both private and public sectors to invest heavily in AI-driven fraud detection solutions. 

Governments across the region are also implementing stronger regulatory frameworks and data protection laws, such as India’s Personal Data Protection Bill and Singapore’s Cybersecurity Act, further encouraging organizations to adopt advanced AI-based fraud prevention tools to comply with regulatory mandates.

By Region

North America

• The U.S.
• Canada

Europe

• Germany
• The U.K.
• France
• Italy
• Russia
• Spain
• Benelux
• Nordic
• Rest of Europe

Asia-Pacific

• China
• Japan
• South Korea
• India
• ANZ
• ASEAN
• Rest of Asia-Pacific

Latin America

• Brazil
• Mexico
• Argentina
• Colombia
• Rest of Latin America

Middle East & Africa

• Saudi Arabia
• UAE
• South Africa
• Israel
• Egypt
• Rest of MEA

Global Artificial Intelligence (AI) in Fraud Detection Market: Competitive Landscape

The global competitive landscape of the AI in fraud detection market is highly dynamic and characterized by the presence of a mix of established technology giants, specialized fraud detection solution providers, fintech innovators, and niche AI startups. Large multinational corporations such as IBM Corporation, Google LLC, SAS Institute Inc., and SAP SE continue to lead the market by offering comprehensive fraud detection platforms that leverage advanced AI technologies, including machine learning, deep learning, and natural language processing. 

These companies often maintain a global footprint and collaborate with financial institutions, government bodies, and enterprises across diverse industries to offer end-to-end fraud prevention solutions that are scalable and customizable based on specific use cases.

In addition, the market is witnessing the rise of agile, innovation-driven companies such as Veriff and Matellio Inc., which are focusing on identity verification, behavioral biometrics, and AI-powered authentication technologies. These players are carving out niche segments by addressing the growing demand for secure digital onboarding, customer authentication, and fraud prevention services, particularly in the e-commerce, fintech, and telecom sectors. 

Mergers, acquisitions, and strategic partnerships are also playing a vital role in shaping the competitive dynamics, as both established players and emerging firms aim to expand their technological capabilities, regional presence, and customer base. For instance, leading companies are forming alliances with fintech startups and cybersecurity firms to co-develop AI solutions that address emerging fraud trends and regulatory demands. 

• IBM Corporation
• Google LLC
• SAS Institute Inc.
• SAP SE
• FICO
• ACI Worldwide
• Experian plc
• Fiserv, Inc.
• Verisk Analytics, Inc.
• NICE Ltd.
• Veriff
• Matellio Inc.
• Microsoft Corporation
• Amazon Web Services, Inc. (AWS)
• BAE Systems plc
• LexisNexis Risk Solutions (part of RELX Group)
• Darktrace Holdings plc
• Kount, Inc. (an Equifax Company)
• Feedzai
• Zest AI
• ThreatMetrix (a LexisNexis company)
• Palantir Technologies Inc.
• Other Key Players

• February 2025: Alset AI Ventures acquired a majority equity stake in Cedarcross International to expand its portfolio of AI-powered fraud detection and advanced analytics solutions, focusing on enhancing security and fraud prevention capabilities across financial services and technology sectors.
• January 2025: Bank of America acquired Zest AI, a leading provider of AI-powered credit underwriting and risk assessment solutions. This acquisition enables the bank to strengthen its fraud risk modeling and improve credit decisions using AI-driven models for enhanced fraud detection and mitigation.
• September 2024: Mastercard announced the acquisition of Recorded Future, a threat intelligence and fraud prevention company leveraging AI for cyber risk analysis. This acquisition aims to bolster Mastercard’s fraud detection and cybersecurity capabilities across its payment ecosystem.
• September 2024: Visa acquired Featurespace, an AI enterprise focused on real-time fraud detection and financial crime prevention. The deal enhances Visa’s ability to leverage AI for identifying transaction anomalies and proactively combatting payment fraud.
• September 2024: Socure acquired Effectiv, a fraud detection, and risk decisioning platform. The integration of Effectiv’s AI-powered orchestration technology helps Socure enhance fraud prevention and risk management across financial services and fintech sectors.
• August 2024: Socure expanded its fraud prevention portfolio through the acquisition of PerimeterX, a bot mitigation and automated fraud Protection Company. This acquisition supports Socure in building stronger AI-powered defenses against credential stuffing and bot-driven fraud.
• February 2024: DNV merged its cybersecurity division with Nixu and Applied Risk to strengthen its AI-driven fraud detection and security services. The integration focuses on protecting critical infrastructure and industrial sectors from sophisticated cyber and fraud threats.