Join us for an easy-to-use playbook with detailed steps to help you get your AI project funded. Intelligent applications to deal with fraud and money laundering are essential for modern risk management in financial services, banking and retail. However, AI projects dealing with fraud often miss out on funding because stakeholders span many organizations and need very different levels and types of information; from technical to financial evaluation, nuanced details can accelerate or stall a projects depending on how they are presented.
- Graph-enhanced AI projects – Discuss why these projects have different requirements and how to gauge stakeholders’ needs
- State assessment – Find out how to evaluate your current and future state in financial terms.
- Business cases – Understand how to calculate your ROI, identify competitive landscape and present a compelling argument.
Scott got his start in high tech as an engineer at NASA and quickly found his calling in sales and marketing. He has worked in the deep end of SaaS, enterprise software sales and large expert services consulting. Having held several senior-level positions with custom software products, Scott turned to consulting to broaden the scope of the technologies he evangelizes.
Rik Van Bruggen brings 10 years experience in sales, specifically in web-based application development and security/identity management to his work at Neo Technology. Previously with Courion and Imprivata, Rik has managed sales and strived for customer success across Europe. Rik is a Belgian technology addict with a passion for sales, family, friends, music, travel, and of course, good food and even better, beer. Rik also runs the Graphistania podcast on graph databases.
Dr. G loves data. His favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.