Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization - Corey Lanum @ GraphConnect Boston 2013

Ernst and Young (EY) did a study showing how blockchain technology could revolutionize the job of the CFO. These beneficial advancements run the gamut, from enhancing cybersecurity to helping manage the supply chain better. Here are a few of the ways that blockchain development can impact CFO roles

Data lineage tracks how data flows through an enterprise by identifying where data comes from, where it goes, and what happens along the way. It can be difficult to achieve due to differences between business and technical views of data, scope, level of detail needed, and changes within a company over time from mergers, migrations, and high rates of change. Data lineage use cases include governance and regulatory compliance by meeting commitments faster with less manual effort, accurately defining governance initiatives, and exposing previously unknown privacy exposures. MANTA software documents lineage by analyzing SQL, ETL and BI code to visualize lineage maps or integrate with third-party governance solutions.

Big Data provides a source of competitive advantage if organizations can unlock its intrinsic value. To do so requires establishing a cost-effective, governed, and agile data foundation that enables self-service analytics. This allows data citizens to directly query data and extract insights with minimal IT support. Both cultural and technical challenges must be overcome through an enterprise-wide shift in mindset driven by top management and a phased approach to solving technical problems.

This document provides an overview and summary of occupational fraud risks, including: - A summary of key findings from the 2014 ACFE Global Fraud Study on typical fraud schemes, losses, and detection methods. - Definitions and examples of the main categories of fraud - asset misappropriation, corruption, and financial statement fraud. - Common red flags or warning signs of potential fraudulent behavior. - Steps for conducting a fraud risk assessment to identify risks and controls. - Examples of anti-fraud controls that can be implemented to prevent or detect various fraud schemes.

The document discusses using Neo4j and graph databases for fraud detection solutions. It describes how Neo4j allows for agile development, high productivity, and real-time response times when working with connected fraud data. The document outlines a fraud detection demo using Neo4j to load operational data, inject fraud cases, generate alerts, and export detected fraud data for investigation. It proposes using Neo4j as the foundation for a 360-degree fraud prevention solution integrated with other systems and data sources.

Talk at scikit-learn day at PyData Paris 2016.

This document discusses anomaly detection techniques in scikit-learn. It begins by defining anomalies and outliers, then describes the main types of anomaly detection as supervised, semi-supervised (novelty detection), and unsupervised. Popular density-based, kernel, nearest neighbors, and tree/partitioning approaches are covered. Examples are given using Gaussian mixture models, one-class SVM, local outlier factor, and isolation forest algorithms. Challenges in anomaly detection like parameter tuning and evaluation are also noted.

A walk through a common fraud case : how to use Neo4j and graph visualization to identify criminals and fight loan fraud.

A telecom company named as Bad Idea is expecting for fraudsters. They designed a weird rate plan called Praxis plan where only four calls are allowed during a day. Bad Idea has their call logs spanning over one and half months.We are using the Naive Bayesian Classification rule to predict the fraudsters for telecom company.

The document discusses revenue control procedures for food and beverage operations. It covers standard revenue definitions, manual and automated guest check systems, server and cashier banking systems, and using daily reports to monitor revenue. Automated systems simplify controls by eliminating duplicate checks and automating tasks like voiding items. Managers can use point-of-sale reports to compare actual to expected revenues.

The document discusses telecom fraud, including definitions, types, and detection techniques. It notes that telecom fraud results in significant global losses estimated at $40 billion annually by the Communications Fraud Control Association in 2011. The document outlines different categories of fraud, including technical (external and internal) frauds and non-technical frauds. It also summarizes two literature articles on data mining approaches to fraud detection and an overview of different types of telecom frauds such as subscription, clip on, and call forwarding frauds. Detection techniques discussed include data modeling of user behavior, social media monitoring, and strengthening customer identification controls.

This document discusses revenue control procedures for food and beverage operations. It covers manual and automated guest check systems, server and cashier banking for collecting revenue, and assessing standard revenue levels for food, beverage, and automated beverage operations. The goal is to understand how managers use various systems and reports to establish revenue standards and ensure actual revenue collected matches expectations.