As data volume and complexity become more troublesome, data-science tools become ever more vital to the anti-fraud community. To compound matters, unstructured data is the largest and most challenging area of "big-data." Specific tools found in data science come to the rescue.
In this session, you will learn the synergy between open-source tools in information retrieval (e.g. search-engine technology), natural-language processing and graph databases. We will discuss how Information retrieval can be used to remove unwanted information and to key in on relevant topics and events. From there, natural-language processing tools can be used to extract named entities (e.g. people, places, associations, etc.), keywords, phrases and concepts.
Graph databases can be built from this data to find important relationships and patterns. These three areas, leveraged in various permutations, can further refine results. In an investigation, we are most interested in summarizing data to find what is going on, to look for useful correlations and relationships and to identify red flags that require further review. Learn how these indispensable data-science tools provide powerful and efficient results to the anti-fraud community.
Speaker: Paul Starrett
Location: GraphConnect NYC 2017