Leading Detection

In this episode, we are joined by Tamas Kadar, CEO of SEON, who shares insights on how AI and automation are transforming fraud detection, the evolving role of fraud analysts, and best practices for implementing AI responsibly in fraud prevention.

The report we reference in the session: https://seon.io/landing/2026-fraud-and-aml-leaders-report/?utm_source=leading-detection&utm_medium=referral&utm_campaign=content-release_fraud-aml-survey-horizontal_fy26-q1&utm_content=podcast_fraud-aml-survey-horizontal_fy26-q1Key

Topics

• AI and automation in fraud detection

• Evolution of fraud analyst roles

• Best practices for AI implementation in fraud prevention

Chapters

00:00 Introduction to Fraud Detection and AI

02:37 Evolution of Fraud Detection Algorithms

05:15 The Role of AI in Fraud Analysis

07:52 Challenges in Implementing AI Solutions

10:36 The Human Element in Fraud Detection

13:07 Setting Realistic Expectations for AI in Fraud

15:46 Future of AI in Fraud Detection

20:46 Integrating AI with Compliance and Historical Insights

22:48 Proactive Fraud Prevention Strategies

25:20 Testing and Evaluating Fraud Detection Tools

27:08 The Challenge of Automation in Fraud Management

31:06 The Rarity of Skilled Fraud Practitioners

34:32 Building Effective Fraud Teams and Tools

38:19 Future Trends in Fraud Prevention and Vendor Dynamics

#FraudPrevention, #AIinFraud #FraudAnalytics #Tech

This episode with host Matt Brady explores HoneyBotting, a defensive cybersecurity technique that uses AI decoys to combat scams and fraud. Joe Tallett, co-founder and CEO of Aviel Intelligence, shares insights on how HoneyBotting is evolving in response to the growing sophistication of scammers, the technical infrastructure involved, and the ethical considerations.

Key Topics

• Honeybotting as a defensive technique against scams

• The evolution of social engineering and AI in fraud

• Technical infrastructure for HoneyBotting and scam detection

• Ethical considerations in deploying HoneyBots

• The role of AI in the cybersecurity arms race

Chapters

00:00 Introduction to HoneyBotting and Its Importance

03:11 The Evolution of Scamming and Cybersecurity

06:36 The Infrastructure of HoneyBotting

08:49 Methodology of HoneyBotting

10:54 Understanding Hooks in Scamming

13:02 Differentiating Between Legitimate and Illegitimate Marketing

15:13 Ethical Considerations in HoneyBotting

17:51 Utilising Intelligence to Combat Scams

19:15 The Intersection of Financial Crime and Cybersecurity

21:27 The Arms Race: AI vs. AI in Scamming

25:26 Future of Aviel Intelligence and Closing Thoughts

#CyberSecurity #AIsecurity #SocialEngineering #CyberDefense #Deepfake

In this conversation, Shay Yinusa discusses the critical shift towards real-time fraud detection and the importance of leveraging technology, particularly AI and machine learning, to stay ahead of increasingly sophisticated fraudsters. The discussion covers the current state of fraud detection, the challenges faced in implementing advanced technologies, and the necessity for collaboration and information sharing within the financial crime prevention community. Shay emphasises the need for organisations to be proactive and prepared for future challenges in fraud detection.

Key Takeaways

• We need to rely on more technology to detect fraud.

• Fraud detection has evolved from static rules to real-time monitoring.

• Real-time means making decisions at the moment of the transaction.

• AI and machine learning are crucial for reducing false positives.

• Collaboration and information sharing are essential in financial crime prevention.

• Organisations must keep up with the evolving landscape of fraud.

• Technology can help analysts focus on high-risk cases.

• There are challenges in implementing AI effectively.

• The future of fraud detection requires proactive thinking.

• We must ensure that technology is used properly to prevent fraud.

Chapters

00:00 The Shift to Real-Time Fraud Detection

04:57 Understanding Real-Time Monitoring

09:15 The Role of Technology in Fraud Prevention

14:01 Challenges in Implementing AI and Machine Learning

18:46 The Future of Fraud Detection

23:22 Collaboration and Information Sharing in Financial Crime

28:06 Looking Ahead: Opportunities and Concerns

#fintech #ai #cybersecurity #machinelearning #fraudprevention

In this episode, Radhika Khatod, Group Product Manager for Machine Learning at Alloy, shares insights into how machine learning is revolutionising fraud prevention in financial services. She discusses the complexities of modern fraud, the limitations of rule-based systems, and the future of AI-driven detection methods.

Key Topics

• The complexity of modern fraud and adversarial actors
• Limitations of static rule-based fraud detection systems
• How machine learning models improve accuracy and reduce false positives
• The rise of synthetic identities and deepfake scams
• The importance of explainability and trust in AI models

Chapters

00:00 Introduction to Fraud and Machine Learning

01:48 The Evolution of Fraud Tactics

05:38 The Shift from Rules to Machine Learning

08:27 Challenges in Adopting Machine Learning

10:38 Understanding AI and Machine Learning Terminology

12:16 Building Trust in Machine Learning Models

14:33 The Role of AI in Fraud Analysis

18:06 The Importance of Data in Fraud Detection

19:24 Distinguishing Between Normal Fraud and Coordinated Attacks

25:48 Global Perspectives on Fraud Prevention

29:14 Fintechs vs. Traditional Banks in Fraud Prevention

31:21 Challenges in Building Fraud Prevention Tools

33:21 The Future of Fraud Detection and Prevention

#ai #fraudprevention #fintech #machinelearning #frauddetection

In this conversation, Matt Brady and Chaitanya Sarda delve into the complexities of Know Your Business (KYB) processes, highlighting the challenges faced in standardisation, compliance, and fraud detection. They discuss the current state of KYB, the impact of AI on the industry, and the importance of effective vendor management. The conversation also touches on the evolving landscape of fraud and compliance communities, emphasising the need for robust defences and innovative solutions in the face of increasing fraud risks.

Key Takeaways

• KYB is a relatively new term and still evolving.
• There is a lack of standardisation in KYB processes across industries.
• The explosion of new businesses necessitates real-time KYB checks.
• AI can significantly enhance the efficiency of KYB processes.
• Onboarding fraud is a growing concern that requires immediate attention.
• Compliance, risk, and fraud must be managed together for effective onboarding.
• Cross-border KYB presents unique challenges due to varying regulations.
• Data quality and access are critical for effective KYB.
• The industry is seeing a rise in fraud detection communities and resources.
• AI tools can help streamline the KYB process, but should not replace human judgment.

Chapters

00:00 Introduction to KYB and Its Importance

02:51 Current Challenges in KYB Processes

05:27 Standardisation and Best Practices in KYB

07:45 The Role of AI in Revolutionising KYB

10:32 Cross-Border KYB: Navigating Global Compliance

13:10 Fraud Detection and Risk Management in KYB

15:28 The Future of KYB: Technology and Trends

17:51 Building a Global KYB System

20:23 Community and Collaboration in KYB

22:55 Conclusion and Future Outlook

#kyb #compliance #frauddetection #AIincompliance #businessverification #crossborderpayments #onboardingprocess #riskmanagement #vendormanagement #fintech

In this conversation, Matt Brady and Andrew La Marca explore the evolving landscape of cyber fraud and the necessity of integrating cyber and fraud detection efforts. They discuss the historical separation of these fields, the impact of technology and data on fraud strategies, the importance of data governance, and the role of synthetic data in fraud prevention. The conversation also touches on the culture of data sharing among businesses, the challenges of sharing sensitive information, and the future of careers in fraud detection as technology continues to advance.

Key Takeaways

• Fraud and cyber threats are increasingly interconnected.
• Organisations must shift from siloed approaches to integrated strategies.
• The rise of technology has changed the landscape of fraud detection.
• Data governance is crucial for effective fraud prevention.
• Synthetic data can enhance model training and fraud detection.
• Sharing data among businesses can improve overall fraud prevention efforts.
• The culture around data sharing is evolving but still faces challenges.
• Career paths in fraud detection are changing with technological advancements.
• AI is becoming a critical tool in identifying fraud patterns.
• Continuous learning and adaptation are essential in the fight against fraud.

Chapters

00:00 The Fusion of Cyber and Fraud

02:38 Understanding the Historical Separation

05:14 The Role of Technology in Fraud Detection

07:35 The Impact of Data on Fraud Strategies

10:08 Governance and Data Integrity

12:34 The Importance of Synthetic Data

14:55 Data Sharing Culture in Business

17:41 The Challenges of Peer-to-Peer Sharing

20:12 Career Development in Fraud Prevention

22:36 Future Trends in Fraud and Cybersecurity

#CyberFraud #DataSharing #SyntheticData #FraudDetection #TechnologyInFraud #AIInFraud #Cybersecurity #FraudPrevention #DataGovernance

In this conversation, Matt Brady and Dustin Eaton delve into the complexities of orchestrating fraud detection. They discuss the evolution from siloed point solutions to a more integrated approach that leverages data and AI. The conversation highlights the importance of understanding customer journeys, the challenges of building effective fraud detection systems, and the need for continuous learning and adaptation in the face of evolving fraud tactics. They also touch on regulatory perspectives regarding AI and the future of fraud prevention strategies.

Key Takeaways

• Orchestration in fraud detection allows for a holistic view of customer risk.
• Point solutions are critical but should not be the only focus.
• Data quality is essential for effective fraud detection.
• Fraud does not recognise organisational boundaries, necessitating integrated defences.
• AI can enhance fraud detection, but requires proper governance and oversight.
• Building systems in large banks is often slowed by bureaucracy and technical debt.
• Understanding customer behavior is key to identifying fraud patterns.
• The fraud detection market is crowded, leading to potential consolidation.
• Continuous learning and adaptation are crucial in the fraud industry.
• Strategic investments in fraud prevention can reduce costs and improve efficiency.

Chapters

00:00 Introduction to Orchestrating Fraud Detection

02:04 Understanding Point Solutions in Fraud Detection

05:05 The Importance of Data in Fraud Prevention

08:44 Challenges in Building Internal Fraud Systems

11:32 The Evolution from Point Solutions to Comprehensive Solutions

14:14 Navigating the Vendor Landscape in Fraud Detection

17:23 The Future of Fraud Detection Tools

20:13 The Intersection of Fraud and Cybersecurity

23:49 The Role of AI in Fraud Detection

28:20 Regulatory Perspectives on AI in Fraud Prevention

33:09 Strategic Investments in Fraud Prevention

37:42 Final Thoughts and Industry Insights

#FraudDetection #FraudPrevention #Cybersecurity #FinTech #AIinFraud #FinancialTechnology #RegulatoryCompliance #DataChallenges #Orchestration #IndustryInsights #FraudTech #PointSolutions #AI #InfoSec #DigitalBanking

In this conversation, Yuliya Kazakevich discusses the growing issue of merchant fraud, its evolution over the years, and the role of AI in detecting and preventing fraud. She emphasises the importance of continuous monitoring and the need for collaboration and data sharing within the industry to combat fraud effectively. The conversation also touches on regulatory challenges, the rising costs of fraud prevention, and the future of risk and compliance in a rapidly changing landscape.

Key Takeaways

• Merchant fraud is often overlooked compared to consumer fraud.
• AI technologies have made it easier for fraudsters to operate.
• Continuous monitoring is essential for identifying fraudulent activities.
• Collusion among fraudsters is becoming more common.
• Data sharing between companies is crucial for effective fraud detection.
• Regulatory mandates may be necessary to improve data sharing.
• The cost of fighting fraud is increasing as fraudsters become more sophisticated.
• AI can enhance fraud detection but requires proper governance.
• The future of risk and compliance will involve more complex decision-making.
• Companies need to adapt to the evolving landscape of fraud and compliance.

Chapters

00:00 Introduction to Merchant Fraud

02:34 The Evolution of Merchant Fraud

05:08 AI in Fraud Detection

07:28 Continuous Monitoring of Merchants

10:06 Collusion and Teamwork in Fraud

12:25 The Role of AI in Risk and Compliance

15:04 Challenges in Detecting Merchant Fraud

17:38 Data Sharing and Collaboration in Fraud Prevention

19:53 The Cost of Fighting Fraud

22:05 The Future of Risk and Compliance Teams

24:38 Conclusion and Future Events

#MerchantFraud #AIInFraudDetection #ContinuousMonitoring #FraudCollusion #RiskManagement #DataSharing #RegulatoryChallenges #FraudPrevention #Fintech #Compliance

In this conversation, Julia Andreeva discusses the evolution of modern fraud, highlighting the increasing sophistication of fraud techniques, particularly with the rise of AI. She explains how fraudsters are now using AI to create synthetic identities and adapt their tactics in real-time, making it more challenging for traditional fraud prevention methods to keep up. The discussion also covers the importance of continuous risk assessment in fraud prevention, emphasising that verification should not be a one-time process but an ongoing evaluation of user behaviour. Finally, the conversation touches on the potential future of fraud, including the industrialisation of fraud techniques and the need for organisations to adapt their defences accordingly.

Key Takeaways

• Fraud has evolved to become more sophisticated and abstract.
• AI is enabling fraudsters to create synthetic identities.
• The barrier to entry for committing fraud is decreasing.
• Fraudsters are manipulating telemetry and behaviour to evade detection.
• Continuous risk assessment is essential for effective fraud prevention.
• Traditional document checks are no longer sufficient on their own.
• AI fraud agents can learn and adapt their tactics in real-time.
• The industrialisation of fraud is a realistic worst-case scenario.
• Organisations need to merge fraud prevention and compliance teams.
• AI can be used defensively to enhance fraud detection capabilities.

Chapters

00:00 The Evolution of Modern Fraud

03:09 AI's Role in Fraud Sophistication

06:12 Understanding Telemetry and Behaviour in Fraud

09:17 The Rise of Synthetic Identities

12:10 AI Fraud Agents: A New Threat

15:08 Continuous Risk Assessment in Fraud Prevention

18:14 The Multi-Layered Approach to Fraud Prevention

21:14 The Importance of Post-Onboarding Monitoring

24:05 The Future of Fraud: Risks and Adaptations

26:53 The Role of AI in Defence Strategies

30:00 Identifying Technical Blind Spots in Fraud Prevention

33:13 The Worst-Case Scenario for Fraud

#fraud #AI #identitytheft #fraudprevention #cybersecurity #digitalidentity #frauddetection #riskassessment #syntheticidentity #fraudtrends

In this episode, Matt speaks with Cecilie Fjellhoy, a victim of romance fraud who has transformed her experience into a mission to combat fraud.

Cecilie discusses her journey from being a victim to becoming an advocate for fraud prevention, emphasising the need for friction in financial transactions to protect consumers. She highlights the importance of digital identity, the response of financial institutions, and the role of technology and AI in detecting and preventing fraud. The conversation delves into human behaviour, the challenges faced by victims, and the future of fraud prevention strategies.

Key Takeaways

• Cecilie became a victim of romance fraud, losing over £200,000.

• She went public to raise awareness and help other victims.

• The current financial system lacks the necessary friction to prevent fraud.

• Digital identity systems can be both beneficial and risky.

• Financial institutions are beginning to take fraud more seriously.

• There is a difference in how countries handle fraud cases.

• Victims often feel unsupported by banks and law enforcement.

• AI has the potential to improve fraud detection and prevention.

• Human behaviour plays a crucial role in fraud susceptibility.

• Awareness alone is not enough to prevent fraud.

Chapters

00:00 Introduction to Cecilia's Journey

01:19 The Impact of Romance Fraud

03:31 Bridging Fraud and User Experience

06:23 Digital Identity and Systemic Issues

07:44 Banking Responses to Fraud

11:37 Virtue Signaling vs. Genuine Interest

14:43 Reintroducing Friction in Financial Transactions

19:04 Rethinking Financial Institutions' Role in Fraud Prevention

22:43 The Human Element in Fraud and Abuse

26:07 AI's Potential in Combating Fraud

29:47 The Evolving Landscape of Fraud and Awareness

33:27 Digital Identity and Its Implications

#LeadingDetection #fraudprevention #digitalidentity #fintech #userexperience #podcast #fraud