How M2P’s AI-powered Fraud Risk Monitoring (FRM) is Transforming Fraud Detection for Financial Institutions

As technology continues to reshape finance, innovations like digital payments, advanced banking platforms, and user-friendly financial services have brought immense convenience, but also opened new doors for sophisticated fraud.

This rapid adoption of digital banking, online payments, and cryptocurrency has exposed financial institutions to highly coordinated and increasingly costly cyber-enabled fraud. Today’s fraudsters go far beyond phishing or stolen card details. They use deep-fake voice scams, synthetic identities built from stolen data, and automated account takeovers that can slip past traditional defenses.

Legacy fraud detection models, which rely mainly on rule-based checks, have demonstrated limitations in identifying complex fraudulent activities, especially those that involve dynamic, evolving, & adaptive patterns and behaviors.

Amid this shifting landscape, artificial intelligence (AI) has emerged as a game-changer in fraud prevention and compliance management. By combining machine learning (ML), deep learning (DL), and natural language processing (NLP), AI-driven systems deliver sharper insights and more accurate detection capabilities.

Recent research highlights that AI-powered fraud detection solutions not only can substantially reduce false positives but also enhance the precision of identifying fraudulent patterns, thereby improving operational efficiency and reinforcing regulatory compliance. for financial institutions.

In this blog, we decode how AI is revolutionizing fraud detection and why it’s becoming a cornerstone of modern risk management.

AI in Fraud Detection

From the simple rule-based fraud detection in the 90s to neural networks and statistical models that delve deeper into historical data to uncover intricate patterns in 2000s, to today’s influence in fraud detection and prevention, the role of Artificial Intelligence has become eminent.

According to recent research, machine learning models have demonstrated a significant improvement in fraud detection accuracy, with an average detection rate of 96.4% compared to traditional methods that hover around 78.2%.

Modern implementation frameworks can process and analyze up to 87.5% more data points per transaction compared to traditional rule-based systems. Thus, reducing the false positives rate to 5.2% compared to the 23.8% observed in traditional methods. This combination of adaptive algorithms and real-time monitoring helps financial institutions build stronger security frameworks and stay ahead of evolving fraud tactics.

Global Fraud Detection and Prevention Landscape

The global fraud detection and prevention (FDP) market is projected to grow from USD 32 billion in 2025 to USD 65.68 billion by 2030, at a CAGR of 15.5%. This expansion is driven by the accelerating shift of enterprise workloads to cloud environments, which has introduced more complex and distributed threat landscapes that traditional on-premises security systems struggle to manage effectively. As organizations increasingly adopt hybrid and multi-cloud infrastructures, the need for FDP solutions offering real-time monitoring, AI-driven anomaly detection, and seamless cloud integration is expected to surge.

Among regions, Asia-Pacific is anticipated to witness the fastest growth, supported by rapid digital adoption, a growing online user base, and the prevalence of mobile-first economies. Countries such as India, China, Indonesia, and Vietnam are experiencing rising incidents of online banking, e-wallet, and e-commerce fraud. Limited legacy infrastructure for fraud prevention, coupled with nationwide initiatives like Digital India and UPI

, is driving strong demand for AI-based, real-time, and adaptive identity fraud detection solutions across the region.

India’s Fraud Detection and Prevention Landscape

India’s banking sector recorded fraud-related losses of ₹36,014 crore in FY 2024–25, marking a steep 194 percent surge from the previous year, according to the Reserve Bank of India. Interestingly, while overall losses soared, the number of internet and card-related fraud cases fell by more than half during the same period. 

Public-sector banks bore the majority of the losses, around ₹25,667 crore (71%), primarily due to large-value loan frauds. Private banks, however, reported the highest number of incidents, exceeding 14,000 cases, mainly linked to card and online banking thefts. 

Although digital payment frauds accounted for 56 percent of the total incidents, they represented only a small share of the total loss amount. In contrast, loan-related scams led to the most significant financial damage. 

In response, the RBI has called on banks and financial institutions to adopt advanced fraud-detection systems powered by artificial intelligence and machine learning to strengthen customer security and minimize future fraud risks. 

Against this backdrop, financial institutions need fraud detection platforms that are real-time, AI-driven, and built to scale across products, channels, and regions. They must handle rising transaction volumes, adapt quickly to new fraud patterns, and balance strong security with a smooth customer experience. 

M2P’s AI-powered Fraud Risk Monitoring (FRM) platform is designed to address these demands head-on.

How M2P’s Fraud Risk Monitoring (FRM) Solves Today’s Most Complex Fraud Challenges?

Challenge: Skyrocketing Transaction Volumes, Slower Fraud Detection

As digital payments and embedded finance grow, institutions are processing unprecedented transaction volumes every second. Manual reviews and legacy rule engines simply can’t keep up. This results in delayed fraud detection, higher false negatives, and reputational risk for institutions. Even milliseconds can make the difference between a blocked fraud and a financial loss.

How M2P FRM Solves it:

• Processes high transaction volumes seamlessly, built for scale across large banks and fintechs.

• Enables sub-400 millisecond response times, applying ML-based risk assessment before the transaction completes.

• Detects anomalies in real time, not after the damage is done.

Challenge: Fragmented Systems and Data Silos 

Most institutions use multiple platforms for fraud prevention: one for card transactions, another for digital payments, and separate systems for lending or BNPL. This fragmented setup limits visibility, slows detection, and allows fraudsters to exploit gaps between systems.

How M2P EFRM Solves it: 

• Unifies fraud detection across multiple channels and use cases.

• Delivers a single source of truth for risk and fraud insights across the institution.

• Adapts easily to new channels and data feeds, ensuring full ecosystem coverage.

Challenge: High False Positives and Poor Customer Experience

A persistent pain point for most institutions is balancing security with customer experience. Traditional systems tend to flag legitimate transactions as fraudulent, leading to declined payments, manual reviews, and frustrated customers. High false-positive rates (sometimes up to 40%) also drain risk teams, forcing them to chase false alerts instead of focusing on real threats.

How M2P FRM Solves it:  

• Uses advanced machine learning models to reduce false positives significantly.

• Continuously tunes rules through real-time dashboards and analytics.

• Prioritizes genuine risk, so legitimate customers aren’t inconvenienced. 

Challenge: Manual Case Tracking and Delayed Resolution

Fraud case management often sits outside the core detection system, spread across emails, Excel sheets, or isolated CRM tools. This creates inefficiency, poor accountability, and slow response to fraud cases.

How M2P FRM Solves it:

• Includes an integrated Case Management module for end-to-end tracking.

• Automates case allocation, blocking actions, and resolution workflows.

• Provides visibility into case status, SLAs, and performance through intuitive dashboards. 

Challenge: Weak Authentication Layers for High-Risk Transactions

Even as fraud detection improves, weak authentication remains a vulnerability, particularly for high-value or high-risk transactions. Without additional validation layers, fraudsters can exploit stolen credentials or device spoofing.

How M2P FRM Solves it: 

• Incorporates Multi-Factor Authentication (MFA) to add context-based verification.

• Dynamically adjusts security checks depending on risk level, tightening controls without disrupting the user journey.

Challenge: Inflexible Rule Management

Legacy fraud systems require vendor dependencies for even the smallest rule change, making them slow to adapt to emerging fraud patterns. Risk teams lack agility and visibility, relying on IT or third-party vendors for updates.

How M2P FRM Solves it:

• Empowers risk teams with a self-service Business Rules Engine (BRE). 

• Enables instant rule design, modification, and deployment, without coding.

• Provides real-time monitoring to test and fine-tune rules as fraud evolves. 

Challenge: Limited Insight into Fraud Trends and Performance

Without comprehensive reporting, risk leaders often rely on fragmented MIS reports or delayed data, making it hard to identify trends, measure rule effectiveness, or prepare for audits.

How M2P FRM Solves it:

• Delivers actionable dashboards and reports covering rule performance, fraud trends, and operational metrics.

• Offers automated report downloads or SFTP transfers for compliance and internal reporting.

• Helps institutions make data-backed fraud management decisions faster.

M2P’s Fraud Risk Monitoring platform equips financial institutions with the speed, intelligence, and adaptability needed to outsmart evolving fraud tactics. By bringing together AI-driven analytics, customizable rule engines, and real-time case management, the platform helps risk teams move from reactive fraud detection to proactive, system-wide protection, ensuring that every transaction remains secure, seamless, and trusted.

Get in touch with us to explore how M2P’s AI-driven fraud detection platform helps financial institutions stay secure and a step ahead.

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References:

1. https://www.researchgate.net/publication/389830686_The_Evolution_of_Fraud_Detection_A_Comprehensive_Analysis_of_AI-Powered_Solutions_in_Financial_Security 

2.  https://www.ey.com/en_in/insights/forensic-integrity-services/navigating-the-risk-landscape-to-build-fraud-resilient-frameworks  

3. https://www.ibm.com/think/topics/ai-fraud-detection-in-banking 

4. https://gsconlinepress.com/journals/gscarr/sites/default/files/GSCARR-2025-0086.pdf  

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8. https://appinventiv.com/blog/ai-agents-in-financial-fraud-prevention/  

9. https://www.ey.com/en_gr/insights/financial-services/how-artificial-intelligence-is-reshaping-the-financial-services-industry 

10. https://www.researchgate.net/publication/389556946_AI_in_Financial_Services_Revolutionizing_Fraud_Detection_and_Risk_Management 

11. https://www.fortunebusinessinsights.com/industry-reports/fraud-detection-and-prevention-market-100231#:~:text=The%20global%20fraud%20detection%20and,to%20increase%20their%20product%20offerings. 

12. https://www.marketsandmarkets.com/Market-Reports/fraud-detection-prevention-market-1312.html 

13. https://government.economictimes.indiatimes.com/amp/news/rbi-reports-over-50-drop-in-digital-banking-frauds-in-fy25/121791537?utm_source=chatgpt.com 

14. https://rmaindia.org/bank-fraud-losses-surge-to-%e2%82%b936014-cr-despite-drop-in-cases-rbi/  

15. https://www.researchgate.net/publication/391659033_AI_in_Detecting_Financial_Fraud_Challenges_and_Limitations