Director in the Washington D.C. Metro Digital Risk Solutions practice, PwC
Harrison is a Director in the Washington D.C. Metro Digital Risk Solutions practice, specializing in corporate intelligence and sanctions screening technologies. Harrison joined the firm in 2014 and has experience working on compliance projects within the Financial Services industry for Banks and Broker/Dealers. Prior to PwC, Harrison worked for Navy Federal Credit Union as a Project Manager, focused on cross-organizational efforts. Harrison has over 10 years of experience with project management, business transformation, strategic analysis, management consulting, operations, process modeling, compliance and controls management. Experienced with developing innovative solutions, organization transformations, consultative, and operational management efforts. Harrison received his B.S. Political Science from the United States Naval Academy and his MBA from Olin Business School, Washington University in St. Louis. Harrison is a Certified Anti-Money Laundering Specialist and Project Management Professional (PMP).
Track: Supply Chain
Monday, April 15, 3:40–4:30pm
Mitigating Supply Chain Risk by Combining Internal, External, and Open Source Data into a Single User Experience
Supply chain risk can be mitigated by fusing a myriad (internal, external, and open source) of data sources in a single user experience. Big data analytics solutions can detect and monitor various supply chain risks including counterfeiting and fraud in known vendor networks and beyond.
This session will focus on:
- Counterfeit and fraud in supply chain network — The insertion of counterfeit goods into licit supply chain networks is a growing multi-billion dollar threat to all commercial and governmental sectors. Counterfeiters have been exploiting the internet, especially social media and the dark web, to introduce their products into the market. This threat amplifies when goods are produced in developing countries. Big data analytic solutions leverage natural language processing, machine learning, and data science to detect potential instances of fraud and / or counterfeiting and create decision-oriented, actionable insights for leaders at all levels and across all business functions.
- Counterfeiting and its associated role with Threat Convergence — Changing geopolitical dynamics and the interconnected supply chains present a significant risk to all organizations. Understanding how counterfeiting pays a crucial role in the alliance of: espionage, criminal, opportunists, terrorists, state sponsored entities and cartel syndicates; is paramount in the endeavor of using NLP, machine learning, and other data science techniques to actively assist in identifying these risks.
- Counterfeit detection using natural language processing — If a supply chain stretches to the developing world, particularly to factories that produce small, unsophisticated components, it is potentially more susceptible to risk of counterfeit and/or fraudulent activities. With multi-lingual NLP and translation capabilities big data analytics solutions support risk tagging in original language and English.
- Introduction to advanced supply chain analytics — Four fundamental attributes of counterfeit detection and monitoring technology are contributing to a robust and efficient solution today: fuses multiple data sources; automates activities; improves through machine learning; presents information for effective decision making.
- Client cases and examples
- Other use cases