AI-driven automation and real-time transaction monitoring are the top priorities for organizations seeking to combat fraud, the 2025 Digital Fraud Outlook report published by SEON has revealed.
Fraud Budgets Grow, But ROI is Complicated
According to the report, 85% of organizations have increased their fraud prevention over the past year, 88% are actively expanding their fraud teams, and 88% spend over 3% of their revenue on fraud prevention. However, SEON warns that organizations must invest strategically to maximize ROI, and existing ROI calculation methodologies may be flawed.
The report highlights that 33% of organizations measure ROI based on reduced fraud losses, while others assess improvements in customer experience and detection accuracy. SEON argues that they may be overlooking other hidden costs resulting from fraud. For example, businesses also face costs from customer churn, compliance fines, and operational inefficiencies, which can erode up to 5% of their revenue.
AI and Data Analytics are Transforming Fraud Prevention
Emerging technologies, particularly AI and data analytics, play an increasingly important role in fraud prevention. 76% of companies now prioritize AI, machine learning, and data analytics as essential skills in fraud prevention. Moreover, 51% of companies report that AI provides real, measurable value in fraud prevention.
However, AI alone isn’t a silver bullet. While 84% of survey respondents believe AI will reduce the need for human oversight, 96% expressed confidence in AI vs. traditional fraud tools, and 57% expressed hesitancy, particularly about its effectiveness in countering AI-generated fraud.
Real-Time Transaction Monitoring Enters the Spotlight
With the rise of instant payment networks and borderless digital transactions, real-time fraud detection has become the number one investment priority for businesses. The report finds that 62% of companies are shifting away from traditional batch-based monitoring in favor of real-time transaction monitoring, allowing them to block fraudulent activity before it impacts revenue.
The report emphasizes that “fraudsters operate in real-time – so should fraud prevention. ” By combining real-time data with AI-driven risk decision-making, organizations can proactively detect and mitigate threats before they escalate.
The Top Emerging Fraud Threats of 2025
The SEON report also lists the top fraud threats of 2025. The key trend is that fraud is becoming remarkably sophisticated, and organizations need equally sophisticated solutions to protect themselves.
- AI-Powered Fraud – Deepfakes, AI-generated phishing, and voice cloning are making social engineering scams harder to detect.
- Synthetic Identity Fraud – Criminals are using AI to create entirely fake identities, bypassing traditional security measures.
- Account Takeover & Credential Stuffing – Automated attacks using stolen credentials remain a top concern.
- Social Engineering & Business Email Compromise (BEC) – Sophisticated scams targeting employees and executives continue to rise.
- Exploitation of Instant Payments & Real-Time Transactions – Fraudsters are taking advantage of the irreversible nature of instant digital payments.
Different Sectors, Different Strategies
Interestingly, organizations in different industries take different approaches to fraud prevention. For example:
- iGaming companies are investing heavily in fraud detection technology and expanding fraud teams, likely due to the high-risk nature of online gambling and the prevalence of multi-accounting and bonus abuse.
- eCommerce and payment processors prioritize real-time transaction monitoring to keep up with the volume of digital transactions.
- Fintech and financial services are focusing on AI-driven fraud prevention tools to strengthen defenses against evolving threats, as they handle sensitive financial data and face complex fraud risks like identity theft and money laundering.
Clearly, there is no one-size-fits-all approach to fraud prevention. Industries tailor their strategies to their specific vulnerabilities, whether that means hiring more specialists, improving detection speed, or integrating AI-driven solutions.
Looking Ahead
SEON argues that while AI is transforming fraud prevention, fully autonomous fraud prevention is still a way off. AI models require centralized, real-time data to be truly effective. Without it, businesses risk inconsistent risk scoring, overlooking emerging fraud trends, and increased false positives and negatives.
The key takeaway from the report is that the future of fraud prevention lies in adaptive, AI-driven risk orchestration, where AI continuously monitors, learns, and refines fraud detection in real-time—but always with a human layer to ensure accuracy and ethical oversight.
Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He's written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
The opinions expressed in this post belongs to the individual contributors and do not necessarily reflect the views of Information Security Buzz.
