Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
Estelle Scifo is a Machine Learning Engineer at GraphAware and presented at NODES2022. Leverage Cypher map projections and Python dynamic typing to build an Object Graph Mapper for Neo4j. In this step-by-step session, you’ll learn how to get started on such a project, from defining the framework API to automatically building Cypher queries.
With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
Graphs can be truly transformational for law enforcement agencies. Learn how a cutting-edge graph solution removes obstacles from the criminal intelligence process and increases its efficiency.
A Fortune 500 Retailer saved $6M using Hume to prevent scams. The soft production was deployed in 3 months, and within 6 months it exceeded the scam detection KPIs by 300%.
Hume is a mission-critical graph analytics solution that allows analysts in financial institutions to easily visualise and monitor complex flows of money and detect patterns of suspicious activities.
Learn more about how to quickly act to disrupt fraudulent behaviour and protect your clients and your business.
Dive deeper into Hume Orchestra, our data-driven orchestration tool, with our CTO, Christophe Willemsen.
Limbik is an AI-powered system that surfaces potentially impactful mis- and disinformation activities and informs effective response options. Unlike other technologies and consultative services that are purely reactionary, Limbik utilizes proprietary predictive analytics to enable customers to proactively mitigate the scale and speed of today's information threats.
Graphs are commonplace in investigative, intelligence, and law enforcement work. One of the primary advantages of a graph is to connect data from various data sources, digital and human, and maximize insights across deep and complex networks of connections, bringing them together in fusion centers for a centralized view of suspicious activities. For analysts, data quality and trust is key. The reliability, validity, and general consistency of data sources that contribute to forming real world fused entities is a factor that influences the analysts’ interpretation of events. This session talks about the challenges related to surfacing these aspects of data provenance and various approaches that can be employed to address them using Neo4j. We will touch on graph modeling, implications for data security, and how sources and information ratings can be effectively shared with analysts who need access to them.
Learn how graphs and Hume can help you tackle logistics challenges.
Learn how Hume, graph analytics and machine learning can be powerful tools to help you detect, investigate and prevent money laundering activities.