Anti-Money Laundering (AML) , ATM / POS Fraud , Card Not Present Fraud

Better Defense Against Identity Theft and Application Fraud

Celia Martín and Carl Eastwood of SAS on Adapting to Evolving Consumer Demand
Celia Martín, senior data scientist, customer experience team for fraud and security intelligence, banking and insurance, SAS EMEA, and Carl Eastwood, fraud and security intelligence practice, fraud solutions advisory lead, SAS

In the aftermath of the pandemic and global political unrest, the risks of identity and credential theft have surged, and a deluge of scams are exploiting the crisis. Consumers facing disrupted incomes seek credit solutions, and fraudsters seek to exploit them by using application fraud tactics.

Celia Martín and Carl Eastwood explored how traditional and emerging techniques and technologies can facilitate the swift detection of fraud, minimising costs and bolstering the defense mechanisms of financial institutions. By harnessing the power of artificial intelligence, machine learning and data analytics, businesses can identify fraudulent patterns faster and with heightened accuracy, shielding themselves from financial losses and reputational harm.

In a video interview with Information Security Media Group, Martín and Eastwood discussed:

  • How consumer demands have changed in recent times and what impact this shift has had on the landscape of identity theft and application fraud;
  • How organisations can harness the power of AI, ML and data analytics to identify fraudulent patterns;
  • Long-term strategies businesses can adopt to stay resilient in the face of emerging identity theft and application fraud challenges.

Martín provides analytical expertise and advises current and potential customers how to apply advanced analytics and machine learning methods that can highly improve the performance of their fraud detection engines. As a certified anti-money laundering specialist, she works with clients on implementing multiple techniques such as network analytics, supervised models for fraud detection in real time, anomaly detection and early detection, prioritisation models and interpretability methods - to name a few, all with proven results in detecting and investigating fraud.

Eastwood has spent his career in fraud and financial crimes, working in the U.K., Australia and New Zealand. He originally worked in banking and then in telecommunications, and he joined SAS in 2016. One of his primary focal areas is application fraud, as well as the broader onboarding process that also covers credit risk and KYC/CDD. Eastwood has spent the past 15 years building and deploying machine learning models to detection application fraud, using a variety of complimentary techniques such as link analysis.

About the Author

Anna Delaney

Anna Delaney

Director, Productions, ISMG

An experienced broadcast journalist, Delaney conducts interviews with senior cybersecurity leaders around the world. Previously, she was editor-in-chief of the website for The European Information Security Summit, or TEISS. Earlier, she worked at Levant TV and Resonance FM and served as a researcher at the BBC and ITV in their documentary and factual TV departments.

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