Quick Response Fraud Detection

Webinar series from FraudResourceNet LLC on Preventing and Detecting Fraud Using Data Analytics. Recordings of these Webinars are available for purchase from our Website fraudresourcenet.com This Webinar focused on fraud detection using data analytic software (Excel, ACL, IDEA) FraudResourceNet (FRN) is the only searchable portal of practical, expert fraud prevention, detection and audit information on the Web. FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.

The document outlines the Business Analysis Body of Knowledge (BABOK), which provides best practices for business analysis. It details 36 techniques used in business analysis across 6 knowledge areas: business analysis planning & monitoring, elicitation & facilitation, requirements management & communication, enterprise analysis, requirements analysis, and solution assessment & validation. It also lists 6 underlying competencies needed for business analysis.

This document discusses using predictive analytics to detect, predict, and change behavior related to fraud, waste and abuse (F/W/A) in healthcare. It defines F/W/A and provides statistics on the large financial impact of F/W/A. A case study shows that a predictive modeling approach identified over $8 million in savings opportunities from a large Medicare program. The document recommends that healthcare organizations re-examine their current F/W/A practices, question if they are sufficiently reducing costs, and work proactively using predictive analytics to prevent losses from F/W/A.

Go Predictive Analytics, LLC is a premier data mining and predictive analytics consulting company. We remove the barriers that loom large with creating and deploying data mining solutions for high ROI.

Webinar series from FraudResourceNet LLC on Preventing and Detecting Fraud Using Data Analytics. Recordings of these Webinars are available for purchase from our Website fraudresourcenet.com This Webinar focused on fraud detection using data analytic software (Excel, ACL, IDEA) FraudResourceNet (FRN) is the only searchable portal of practical, expert fraud prevention, detection and audit information on the Web. FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.

This document provides an overview of quantitative analysis techniques for assessing relationships between variables. It discusses concepts related to relationships including presence, nature, direction, and strength of association. It also defines statistical techniques such as ANOVA, cluster analysis, conjoint analysis, discriminant analysis, factor analysis, logistic regression, and multiple regression. Examples are provided to demonstrate calculating explained and unexplained variance in regression, interpreting regression coefficients, and using dummy variables. Steps for conducting regression analysis are outlined including checking assumptions and interpreting residuals plots.

This document summarizes a webinar on using technology-assisted review like predictive coding to increase the speed and reduce the costs of document review for litigation. The webinar covered topics like how predictive coding works by using a seed set to train an algorithm and testing it with control sets. It also discussed how to understand precision and recall metrics and the importance of transparency and scalability. The goal of the webinar was to educate lawyers on leveraging technology-enhanced methods for reviewing increasingly large document collections in litigation more efficiently.

User behavior analytics and user activity monitoring can help organizations detect insider threats by analyzing patterns of user behavior and flagging anomalies. These tools collect user activity log data to monitor interactions with sensitive data and systems. They use algorithms and statistical analysis to identify meaningful anomalies that could indicate potential threats like data exfiltration. This provides a rich data source for investigations and helps focus an organization's security efforts on detecting insider threats, as internal actors often pose more risk than external ones.

A review of the elements and examples of ponzi schemes, pyramids, and market bubbles and how to avoid them.

Using Benford’s Law for Fraud Detection & Auditing Referred to as the First-Digit Law, Benford’s Law is a mathematical theory conceived over 70 years ago that has aided numerous anti-fraud professionals in solving embezzlement, insurance claims and money laundering cases. Benford's Law gives the expected patterns of the digits in unaltered data, and explains there is a large bias towards the lower digits, so much so that nearly one-half of all numbers are expected to start with the digits 1 or 2. In this webinar, we will explain the theory behind the law and how it can be used to find potential fraud and errors to help turn your internal audit or fraud investigation into a revenue generating center. In this session, you will learn: • How to apply Benford’s law analysis to find outliers in processes such as cash disbursement, general ledger, insurance claims, tax assessments, etc. • The types of data that do and do not conform to Benford’s Law • A practical guide to apply Benford’s tests using IDEA software (1st digit, 2nd digit testing, advanced analytics – fuzzy logic, etc.)

This document discusses various types of fraud, famous fraudsters, how fraud is detected, and ways to prevent fraud. It provides details on common fraud schemes like asset misappropriation, corruption, and financial statement fraud. The largest fraudsters discussed are Bernie Madoff, whose Ponzi scheme resulted in $65 billion in losses, and Ken Lay of Enron, which led to a $100 billion loss for investors. Fraud is most often detected through tips, internal controls, and audits. Strong internal controls like surprise audits and job rotation were found to significantly reduce median fraud losses.

The document discusses using machine learning for predictive maintenance in IoT applications compared to traditional approaches. It describes using publicly available aircraft engine data to build models in Azure ML to predict remaining useful life. Models tested include regression, binary classification, and multi-class classification. An end-to-end pipeline is demonstrated, from data preparation through deploying web services with different machine learning models.

This document discusses HR analytics and workforce analytics. It provides an overview of the history and current state of HR analytics. Some key uses of HR analytics include problem solving, decision making, and improving HR process efficiency. The document outlines various HR activities that can be analyzed, such as reporting, benchmarking, and predictive analysis. It emphasizes that HR analytics should focus on relevant metrics and provide context to be most useful. Overall, the document promotes the use of workforce analytics to gain insights that can provide competitive advantages and more effective organizational functioning.

The document discusses various principles of fraud including: 1) Definitions of fraud, corporate fraud, management fraud, and financial statement fraud. 2) The fraud triangle consisting of pressure/motivation, opportunity, and rationalization as the three elements common to every fraud. 3) Characteristics of typical fraudsters including that they are usually someone trusted and not initially suspected, and profiles of high-level and low-level thieves. 4) Taxonomies used to classify fraud including against customers/investors, criminal/civil, for/against the company, and internal/external fraud. 5) The "fraud tree" categorizing fraud into fraudulent statements, asset

Trendwise Analytics provides three levels of HR analytics and reporting capabilities to organizations. Level 1 involves descriptive analysis and dashboards to report basic metrics and percentages. Level 2 derives operational metrics like turnover and promotion ratios to track HR function efficiency. Level 3 utilizes predictive modeling like attrition forecasting, segmentation, and compensation analysis to inform decisions regarding retention, hiring, and compensation.

IT Fraud Series: IT Fraud and Countermeasures - July 20, 2017 Description Webinar Series Overview: In today’s world, fraud investigations have become an everyday part of corporate life and the auditor must gain expertise in this area. The 8 part series will cover the tasks of the fraud auditor, Forensic techniques and tools and the abilities required of the fraud auditor, the type and nature of common frauds, investigating fraud, computer fraud and control, white collar crime, the auditor in court. This session IT Fraud and Countermeasures • Investigating by computer • Document collection and analysis • Interviewing skills • Documenting evidence • Testifying as a witness

FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again. White-Collar Crime Fighter Newsletter Subscribe Now at No Cost! FraudResourceNet has made the premier Anti-Fraud newsletter, White-Collar Crime Fighter freely available to all. All this is required is to complete the registration form with your work email address! The widely read newsletter, White-Collar Crime Fighter brings you expert strategies and actionable advice from the most prominent experts in the fraud-fighting business. Every two months you'll learn about the latest frauds, scams and schemes... and the newest and most effective fraud-fighting tools, techniques and technologies to put to work immediately to protect your organization. When it comes to fraud, knowledge of the countless schemes, how they work and red flags to look for will help keep you, your organization and your clients safe. At FraudResourceNet we understand this and take great pride in providing our FREE White Collar Crime Fighter newsletter -- filled with exclusive articles and tips to provide the knowledge you need. Make sure you stay informed. Sign up for White Collar Crime Fighter newsletter and we’ll keep you up-to-date on special promos, training opportunities, and other news and offers from FraudResourceNet! Signing up is easy and FREE. If you have not already subscribed to our newsletter, please sign up to get started! Sign up for the White Collar Crime Fighter Newsletter (a $99 value ... now completely FREE)

A Retrospective in Analytic Auditing and What’s Ahead Description The speaker will outline salient best practices in establishing an analytic program based on lessons learned looking back on the past two and a half decades. Specific learning objectives include: o Review key dates in the last two decade’s timing that led to the advancement of audit data analytic programs. o Highlight lessons learned over the years through case study examples. o Outline the effective culture around the analytics program to serve as its foundation. o Learn to apply analytics across the entire lifecycle from risk assessment, to planning, fieldwork, and reporting. o Present analytic best practices being deployed by top performing organizations.

Stop the fraudster! Pennsylvania Treasury, Industry Expert Chris Doxey and FulcrumWay at Oracle Open World 2014

Webinar series from FraudResourceNet LLC on Preventing and Detecting Fraud Using Data Analytics. Recordings of these Webinars are available for purchase from our Website This Webinar focused on fraud detection using data analytic software (Excel, ACL, IDEA) FraudResourceNet (FRN) is the only searchable portal of practical, expert fraud prevention, detection and audit information on the Web. FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.

The Future of Auditing and Fraud Detection – Re-imagining the art and science of auditing and fraud detection is coming to the forefront of risk management functions. What was seen as a “nice to have” a few years ago has become a “must have” as digital transformation and data surrounds all aspects of the organization. Specific learning objectives include: o See how analytics can maximize the annual audit plan and better ensure focus is placed on top organizational risks. o Establish a framework to using analytics and automation across the entire audit lifecycle. o Use the general ledger as a case study to provide a digital road map for analytics for detecting fraud (and errors) within the organization. o Define the top company areas for data integration from structured, unstructured and external data sources. o Highlight culturally what audit and fraud detection functions must do to embrace continuous embedded analytic reviews.

Selecting the right Computer Assisted Audit Tool may appear to be a huge undertaking; however, following a systematic approach eases the burden. The right approach minimizes the risk of selecting a product that might not fit into your organization, which could impair your function as it sits underutilized or on the shelf. While point and click visual style tools are settling into the market, many auditors rely on the legacy step-by-step software tools such as ACL, IDEA, Excel and “add-on” tools. Many chief auditors pursue opportunities to increase the frequency and intensity of interactions with management and realize nothing gets attention faster than finding previously undetected anomalies in company data. Finding the right issues quickly and timely improves the value of audit and can assist audit in winning more work. Attending this webinar you will learn: · Identify analysis and financial constraints · Scoping and defining audit strategic objectives · Reviewing selection field based on Technical needs · Building a short/long term on-boarding roadmap · Realize the lost opportunity of not including all auditors (no auditor left behind)

Sampling has existed as a standard for controls testing since controls testing began. We’ve developed algorithms to tell us how many samples we should pull and how many errors we can have and still pass the control. We’ve even developed algorithms to tell us how many more samples we can test if the control didn’t pass the first time. If your goal is simply to do the minimum to pass a SOX audit, then these behaviors should probably continue. If your goals also include really improving the operations of the organization to make it stronger then a more holistic approach is needed, such as analysis on 100% of the population, rather than a small sample. Most controls analytics do not require a degree in data science, but they do require the controls team begin changing its behaviors. Join us to understand what it takes to begin this change, it’s not as challenging as you might think. Learning Objectives Understanding the advantages of analytics vs sampling How to Identify controls where analytics can be applied Real life examples of controls and their associated analytics How to effect a change

Sampling has existed as a standard for controls testing since controls testing began. We’ve developed algorithms to tell us how many samples we should pull and how many errors we can have and still pass the control. We’ve even developed algorithms to tell us how many more samples we can test if the control didn’t pass the first time. If your goal is simply to do the minimum to pass a SOX audit, then these behaviors should probably continue. If your goals also include really improving the operations of the organization to make it stronger then a more holistic approach is needed, such as analysis on 100% of the population, rather than a small sample. Most controls analytics do not require a degree in data science, but they do require the controls team begin changing its behaviors. Join us to understand what it takes to begin this change, it’s not as challenging as you might think. Learning Objectives Understanding the advantages of analytics vs sampling How to Identify controls where analytics can be applied Real life examples of controls and their associated analytics How to effect a change