The study, which surveyed nearly 1,000 senior fraud leaders across nine countries including India, highlights a widening mismatch between increasingly sophisticated fraud techniques and the preparedness of organisations.
Technology gaps slow response to rising threats
A majority of Indian organisations acknowledge that their current systems are falling short. About 69% of respondents said their existing technology stack is inadequate to counter rapidly evolving fraud risks, even as attacks become faster, cheaper and more scalable.
The report notes that identity verification systems have emerged as the most targeted vulnerability, particularly as fraudsters deploy more advanced tools, including generative AI. Despite this, many firms remain caught in prolonged “build versus buy” decisions, delaying critical upgrades.
At the same time, 64% of organisations are moving toward integrating fraud prevention with credit risk assessment into a unified risk management approach, signalling a shift toward more holistic frameworks.
AI and machine learning show measurable gains
While challenges persist, businesses that have adopted artificial intelligence and machine learning are seeing tangible benefits. Around 54% of respondents reported improved fraud detection accuracy after implementing AI, while half said machine learning helped identify fraud cases that traditional rules-based systems would have missed.
Real-time detection and the ability to continuously retrain models were cited as key advantages. Additionally, 65% of fraud leaders said machine learning improves prioritisation for manual reviews, enhancing operational efficiency.
However, capability gaps remain significant. Nearly 76% of Indian organisations said they lack sufficient in-house expertise to build or manage machine learning models, underscoring a growing dependence on external solutions and partnerships.
GenAI emerges as a major threat vector
The rise of generative AI is intensifying concerns across industries. About 65% of respondents described GenAI as the most significant fraud threat to date, with 74% reporting a noticeable increase in AI-driven fraud attempts.
Existing safeguards appear ill-equipped to handle this shift. Nearly 69% of organisations said their current KYC and identity verification systems cannot effectively detect AI-generated documents. More than half also admitted difficulty in determining whether GenAI was involved in specific fraud incidents, making the scale of the problem harder to assess.
Sector-specific risks and rising complexity
The report points to a surge in social engineering and identity theft in financial services and telecom sectors, while e-commerce businesses are grappling with rising instances of friendly fraud and refund abuse.
These trends reflect a broader shift toward more organised, transnational fraud networks that leverage automation and AI tools to scale operations.
Collaboration and shared intelligence gain importance
As fraud grows more complex, businesses are increasingly looking beyond internal capabilities. Around 63% of respondents said demonstrable returns from peer networks would accelerate adoption of shared fraud intelligence systems.
Interest is also rising in low-friction solutions such as behavioural analytics and device intelligence, with 83% of organisations indicating a willingness to adopt passive fraud detection methods that do not disrupt customer experience.
A structural shift in fraud prevention
The findings suggest that fraud prevention is moving from a reactive, rules-based approach to a more dynamic, intelligence-led model. With 71% of businesses now investing more in fraud technology than human analysts, the emphasis is shifting toward automation, real-time insights and collaborative ecosystems.
The report says that as digital adoption accelerates, particularly in markets like India, the ability to modernise fraud defences will be critical to maintaining trust and sustaining growth in the digital economy
