How to Slash False Positives and False Negatives in AML Screening

05 Mar, 2025 10:00 EST | 15:00 GMT Zoom
Customer Profile: PEP

Join Ripjar’s experts for a practical webinar in optimizing your screening systems to reduce noise, improve accuracy, and enhance compliance outcomes.

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Struggling with False Positives and False Negatives?

In today’s fast-paced compliance landscape, achieving highly effective screening while managing vast datasets is a critical challenge. Balancing precision and recall can make or break your operational efficiency, and striking the right balance has never been more essential.

Join Ripjar’s experts for a practical webinar in optimizing your screening systems
to reduce noise, improve accuracy, and enhance compliance outcomes.

Meet the speakers

Ciara

Ciara Aitchison

DirectorFINTRAIL
Moderator

Ciara is a Director at FINTRAIL, with over 17 years’ experience in financial services in both relationship management and financial crime roles and over 8 years’ financial crime experience across CDD, EDD, AML policy and supporting capabilities. She has successfully led global anti-financial crime teams focusing on high risk sectors, private banking and wealth management, supporting the first line on addressing AML, sanctions, AB&C and tax evasion risks. She has been responsible for the implementation of financial crime target operating models, high risk advisory, policy implementation, risk assessment and training. Ciara joined FINTRAIL from ACAMS, where she was responsible for the development of regional anti-financial crime content, strategy and implementation. In her current role she performs a wide range of consulting engagements with clients and supports the development of FINTRAIL content, insights and research.

Simon Smith

Simon Smith

Head of AnalyticsRipjar

Simon is a highly experienced data scientist with a background in Theoretical Physics and over 10 years working at Ripjar. With expertise in tackling a wide range of screening problems, Simon built Ripjar’s original name matching engine, set up the operational data science unit to help customers to find the right balance between effectiveness and efficiency, and led the launch of Ripjar’s AI Risk Profiles data product. Prior to working in screening he worked on various problems in social media such as the automatic detection of Islamic State terrorism propaganda.

Dan Saunders

Dan Saunders

Data Science Team LeadRipjar

Heading up Ripjar’s data science team, Dan has extensive experience in both data science and product development across multiple industries, including crime intelligence, academia, and gaming. Dan’s expertise lies in applying advanced AI techniques to tackle complex challenges in data analysis, often working with immense data volumes and striving for peak efficacy. Prior to his roles in industry, he completed a PhD in Physics at CERN, where he worked on large-scale data experiments at the Large Hadron Collider, using AI to enhance statistical sensitivity in high-energy physics research.

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