Using AI to Detect Frauds: The Smart Defence

Using AI to Detect Frauds: The Smart Defence

Fraud is a mounting global problem. According to the Association of Certified Fraud Examiners, organisations typically lose around 5 % of annual revenue to fraud, equating to trillions of dollars globally. With digital systems proliferating, traditional rule-based defences are no longer sufficient. AI (artificial intelligence) is rapidly becoming the strategic weapon of choice across sectors, including financial services, healthcare, insurance, and even fields like dentistry.


Why Fraud Detection Matters Across Sectors

Fraud isn’t confined to one industry. In healthcare alone, the United States Department of Justice announced in June 2025 that a major takedown charged 324 defendants and identified over US$14.6 billion in alleged losses. Meanwhile, in payments fraud, the Association for Financial Professionals 2025 survey found that 79% of organisations were victims of payments fraud attacks or attempts in 2024. Even within dentistry, where claims data and treatment networks are complex, fraud poses a real risk that can be mitigated by AI.

How AI Detects Fraud: The Mechanisms

AI fraud detection goes far beyond static rules. It leverages:

  • Machine Learning (ML): Learns from historical fraud patterns to detect anomalies instantly.
  • Graph Analytics: Maps connections between devices, accounts, users, and transactions to reveal hidden fraud rings.
  • Behavioural Biometrics: Analyses patterns such as keystrokes, mouse movement, login behaviour, and device fingerprints.
  • Deep Learning: Detects synthetic identities, manipulated images, forged documents, and deepfake-based scams.

The US AI in Fraud Detection market is projected to reach USD 5.1 billion in 2025, according to Dimension Market Research.


Real-World Applications & Data

1. Enterprise Fraud Management (EFM): Large organisations face internal and external fraud risks, from expense manipulation and procurement fraud to rogue-employee collusion.

AI helps by:

  • Monitoring high-risk transactions
  • Flagging abnormal user behaviour
  • Detecting duplicate invoices or falsified vendor details
  • Preventing account takeover within internal systems

With enterprises managing vast data volumes, AI automates fraud detection at scale.


2. Telecom Fraud Detection: The telecom sector experiences some of the fastest-evolving fraud schemes, including:

• SIM swap attacks

• International revenue-share fraud (IRSF)

• Subscription fraud using fake identities

• Roaming and interconnect fraud

AI enables telecom operators to analyse billions of network and subscriber events in real time. By detecting anomalies in call patterns, location data, device signatures, and onboarding behaviour, AI significantly reduces telecom revenue leakage.


3. Insurance Fraud Detection: Insurance companies face persistent challenges such as:

• Exaggerated or staged claims

• Duplicate claims

• Identity fraud during policy onboarding

• Fake documentation

AI image-forensics and ML models can validate accident photos, flag manipulated documents, and identify high-risk claim behaviour instantly, reducing loss ratios and investigation time.


4. Financial Services: Banks have been early adopters of AI-based fraud systems.

A 2025 industry report notes that over 60% of fraud-detection systems now incorporate AI/ML to detect anomalous card transactions, money laundering patterns, and synthetic identities.

AI helps banks:

• Score transactions in milliseconds

• Reduce false positives

• Detect new fraud patterns emerging globally

• Strengthen digital onboarding with biometric validation


5. Government & Public Sector: Governments use AI to combat tax evasion, benefit fraud, procurement fraud, and cyber-enabled scams.

AI-driven analytics help flag suspicious refund claims, identify procurement red flags, and detect identity misuse across welfare systems.


Key Benefits of AI-Driven Fraud Detection

• Real-time monitoring to stop fraud before it causes damage.

• Scalability to analyse millions of records across industries.

• Reduced false positives, improving customer experience.

• Lower operational costs through automated analysis.

• Cross-sector adaptability, usable in finance, telecom, insurance, and public services.


Challenges & Considerations

Implementing AI is powerful but requires:

• High-quality data for model accuracy

• Explainability to justify why an event was flagged

• Regulatory compliance across financial and public sectors

• Continuous retraining as fraudsters adopt AI-driven tactics

• Internal change management to integrate new workflows


Roadmap for Organisations

1. Assess your current fraud-detection systems and data quality

2. Identify specific fraud risks by industry

3. Choose an AI model or platform that fits your operational environment

4. Run a pilot with historical data to benchmark results

5. Integrate AI into existing workflows with clear escalation rules

6. Continuously retrain models to adapt to new fraud patterns


Conclusion

Fraud is evolving at a pace traditional systems can’t match. In 2025, AI stands as the most effective, scalable, and proactive defence available. With its ability to analyse massive datasets, detect anomalies, and uncover hidden networks, AI empowers organisations across finance, telecom, insurance, healthcare, and government to protect their revenue, maintain customer trust, and stay ahead of emerging threats.

AI is no longer optional; it is the smart defence every industry need in the digital age.