Hidden Decision Trees combined with logistic regression or naive Bayes to score large data sets, with application to fraud detection, ad optimization and keyword scoring
India has been named the top country for sending spam emails in the world. An American businessman known as the "Spam King", Sanford Wallace, sent billions of spam emails and could face 16 years in prison for his actions. Wallace's wall of spam helped crown India as the largest originator of spam worldwide.
Regulations, compliance and overall risk management place a significant operational burden on financial services.
Online lenders are no different. You have to comply with multiple regulatory requirements, and you are- like any other financial service- very susceptible to fraud.
If you want to prevent and reduce loan application fraud, your strategy and fraud detection system should include a combination of identity verification, account onboarding protection, and account monitoring.
In this post, we’ll explain how identity verification and Know Your Customer processes are related, and how you can expand them for better fraud coverage.
We’ve also provided specific recommendations for identity verification security tests, and account origination protection strategies that can help you prevent fraud during the loan application process.
Fair Isaac is a leader in fraud detection and decision management solutions. Their Falcon Fraud Manager uses advanced analytics like neural networks and profiling to detect fraud across multiple channels. It has helped reduce credit card fraud losses significantly. The document discusses how debit fraud is evolving and Falcon Fraud Manager's capabilities for protecting debit transactions through profiling of cardholders, devices, and merchants.
Fraud continues to proliferate across financial institutions, through multiple lines of business and banking channels. Increasingly sophisticated criminal tactics and the proliferation of organized crime rings make detecting fraud difficult and preventing it nearly impossible. Adding to the complexity is increased globalization and growth through mergers and acquisition, which make it harder to effectively monitor multiple portfolios and business lines. The presentation discussus best practices and ideas around the prevention, investigation, and detection of possible fraudulent activities across multiple industries.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
ACFE Presentation on Analytics for Fraud Detection and MitigationScott Mongeau
This document discusses continuous fraud monitoring and detection through advanced analytics. It covers trends in analytics including diagnostics, network analytics, and issues with analytics. It also discusses descriptive, predictive, and prescriptive fraud analytics as an integrated process done at an industrial scale. Finally, it discusses advanced analytics methods like supervised modeling, unsupervised discovery, rules-based approaches, outlier detection, and more.
This document discusses using graphs for fraud detection. It begins with an overview of different types of fraud like credit card fraud, insurance fraud, and synthetic identities. It then discusses traditional analysis methods versus graph-based analysis. The document provides examples of modeling user behavior on graphs to understand normal behavior and detect anomalies. It discusses using recommendations and fraud detection as two sides of understanding user behavior on graphs. Finally, it discusses first-party fraud specifically and how fraudsters can fabricate networks of synthetic identities to aggregate smaller lines of credit into substantial value.
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise or 75 minutes of vigorous exercise per week to gain these benefits.
India has been named the top country for sending spam emails in the world. An American businessman known as the "Spam King", Sanford Wallace, sent billions of spam emails and could face 16 years in prison for his actions. Wallace's wall of spam helped crown India as the largest originator of spam worldwide.
Regulations, compliance and overall risk management place a significant operational burden on financial services.
Online lenders are no different. You have to comply with multiple regulatory requirements, and you are- like any other financial service- very susceptible to fraud.
If you want to prevent and reduce loan application fraud, your strategy and fraud detection system should include a combination of identity verification, account onboarding protection, and account monitoring.
In this post, we’ll explain how identity verification and Know Your Customer processes are related, and how you can expand them for better fraud coverage.
We’ve also provided specific recommendations for identity verification security tests, and account origination protection strategies that can help you prevent fraud during the loan application process.
Fair Isaac is a leader in fraud detection and decision management solutions. Their Falcon Fraud Manager uses advanced analytics like neural networks and profiling to detect fraud across multiple channels. It has helped reduce credit card fraud losses significantly. The document discusses how debit fraud is evolving and Falcon Fraud Manager's capabilities for protecting debit transactions through profiling of cardholders, devices, and merchants.
Fraud continues to proliferate across financial institutions, through multiple lines of business and banking channels. Increasingly sophisticated criminal tactics and the proliferation of organized crime rings make detecting fraud difficult and preventing it nearly impossible. Adding to the complexity is increased globalization and growth through mergers and acquisition, which make it harder to effectively monitor multiple portfolios and business lines. The presentation discussus best practices and ideas around the prevention, investigation, and detection of possible fraudulent activities across multiple industries.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
ACFE Presentation on Analytics for Fraud Detection and MitigationScott Mongeau
This document discusses continuous fraud monitoring and detection through advanced analytics. It covers trends in analytics including diagnostics, network analytics, and issues with analytics. It also discusses descriptive, predictive, and prescriptive fraud analytics as an integrated process done at an industrial scale. Finally, it discusses advanced analytics methods like supervised modeling, unsupervised discovery, rules-based approaches, outlier detection, and more.
This document discusses using graphs for fraud detection. It begins with an overview of different types of fraud like credit card fraud, insurance fraud, and synthetic identities. It then discusses traditional analysis methods versus graph-based analysis. The document provides examples of modeling user behavior on graphs to understand normal behavior and detect anomalies. It discusses using recommendations and fraud detection as two sides of understanding user behavior on graphs. Finally, it discusses first-party fraud specifically and how fraudsters can fabricate networks of synthetic identities to aggregate smaller lines of credit into substantial value.
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise or 75 minutes of vigorous exercise per week to gain these benefits.
Rapid Model Refresh (RMR) in Online Fraud Detection EngineWenSui Liu
This document discusses Rapid Model Refresh (RMR), an approach used by PayPal to continuously update fraud detection models in real-time. It describes:
1. Traditional fraud detection tactics like heuristic, scoring, and rule-based approaches and their limitations for an online setting.
2. PayPal's multi-level fraud detection engine that uses risk scoring, rule induction, and agent review to identify high-risk transactions.
3. The implementation challenges of PayPal's growing international footprint and dynamic fraud trends.
4. How RMR addresses these challenges through automatic model development, real-time deployment, and daily monitoring across data, algorithm, and deployment layers.
The document discusses aspects of autonomic computing applied to peer-to-peer (P2P) systems to manage quality of service. It describes using a monitoring mechanism called SkyEye.KOM to gather statistics on P2P systems in a scalable and self-organizing way. Based on the monitoring data, the system can analyze for deviations from preset quality levels, plan adaptations like changing routing table sizes, and execute adaptations to reach and maintain quality goals. Simulations showed the approach enables P2P systems to precisely reach and hold preset quality intervals through self-configuration.
This Presentation shows how business rules are graphically modeled, managed and delivered - with the business case of Master Data Management at Eurex. It also explains the business impact of this approach.
This presentation shows how to graphically model, manage and deliver business rules with the Eclipse-based tool Visual Rules. The business case used is Master Data Management at Eurex. The presentation also explains the business impact of this approach.
Banking Circle: Money Laundering Beware: A Modern Approach to AML with Machin...Neo4j
The document discusses Banking Circle's use of graph technology and a data-driven approach to improve its anti-money laundering efforts. It represents payment data as a network to extract features for machine learning models that detect suspicious activity. This approach generates fewer false alarms than rules-based systems while identifying more high-risk payments and accounts. Network-based investigations also help analysts explore connections more efficiently. The new system screens over 1 million payments daily and has increased alerts leading to compliance actions by 1300% while reducing total alerts by 30%.
Challenges in building a churn prediction model in different industries, presented by Jelena Pekez from Comtrade System Integration. Talk is focused on real-life use-case experience.
This session examines how global collaboration and emerging corporate cultures are creating a new type of
innovative enterprise: one that is based on services. Discussed will be why this new service-focused approach to business considers not only the service culture, but the technology as well. Each party or participant in service orientation sees themselves as service provider as well as a service consumer, in an increasingly
well-connected global economy. This session focuses on both the cultural service dimension, as well the emerging architecture of service orientation. This session delves deep into the components of the enabling technologies of service-orientation, as well as the cultural aspects of services orientation, where enterprises reach and serve various target communities: their customers, yes, but also their trading partners, employees,
and shareholders. Taught by one of BPM’s early pioneers, Dr. Setrag Khoshafian, this session is intended for both business managers and IT, covering Web Services architectures, service oriented maturity models, ESBs, BPM suites, standards for quality of service, and servant leadership.
This document provides an overview of a SQL Server 2008 for Business Intelligence short course. It discusses the course instructor's background and specialties. The course will cover creating a data warehouse, OLAP cubes, and reports. It will also discuss data mining concepts like why it's used, common algorithms, and include a hands-on lab. Data mining algorithms that will be covered include classification, clustering, decision trees, and neural networks.
Ibm financial crime management solution 3Sunny Fei
The document discusses IBM's financial crime management solution which uses an integrated platform to help banks prevent and manage financial crime more effectively. It analyzes real-time transaction data using customized rules to detect potential fraud cases and monitor high-risk activities. When issues are identified, cases are created and investigators can collaborate to investigate further and take appropriate actions. The solution aims to help banks address the growing challenges of financial crime and optimize fraud prevention.
This document summarizes a presentation given by Revolution Analytics on using R for marketing analytics. It discusses challenges like needing to make decisions faster based on more data and predictive models. It provides examples of companies using Revolution's R software to improve results, such as increasing lift for a client by 14% and saving another $270k. The presentation promotes Revolution's R software for handling big data and analytics faster through techniques like parallel processing and distributed computing. It argues Revolution R is the leading commercial provider of high performance R software.
In today's digital world, credit card fraud is a growing concern. This project explores machine learning techniques for credit card fraud detection. We delve into building models that can identify suspicious transactions in real-time, protecting both consumers and financial institutions. for more detection and machine learning algorithm explore data science and analysis course: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This project showcases an AI-driven approach to detecting credit card fraud using machine learning algorithms. The project utilizes a dataset containing transactions with various features such as transaction amount, location, and time. The goal is to build a predictive model that can accurately identify fraudulent transactions and minimize financial losses for banks and customers. The presentation covers data preprocessing techniques, feature engineering, and the application of machine learning algorithms such as logistic regression or random forests. It also discusses model evaluation metrics and the importance of fraud detection in the banking industry. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
The document discusses four styles of customer service interactions that vary in terms of process complexity and analytics capabilities: low process complexity with low analytics representing commodity CRM; low process complexity with high analytics focusing on process efficiency; high process complexity with low analytics prioritizing process effectiveness; and high process complexity with high analytics automating end-to-end processes through customer intelligence and intelligent dialogue. It also lists representative vendors for each style.
Complex Event Processing (CEP) for Next-Generation Security Event Management,...Tim Bass
Complex Event Processing (CEP) for Next-Generation Security Event Management, Fraud and Intrusion Detection , April 17, 2007 (First Draft), London, Tim Bass, CISSP, Director, Principal Global Architect
Emerging Technologies Group
Big Data vs. Big Risk: Real-Time Trade Surveillance in Financial MarketsArcadia Data
Who’s winning the deep forensic analysis ‘arms race’ for compliance?
Real-time trade surveillance in global financial markets has created a data tsunami.
With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water?
Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations.
Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen.
Hadoop based applications are becoming critical in the financial services arena for the analysis and correlation of large volumes of structured and unstructured data. In addition, the Dodd-Frank Act signifies the largest US financial regulatory change in several decades and requires much greater transparency on financial data. In this session, we will answer common questions and demonstrate use cases in how Hadoop and Datameer help with asset management and risk management, fraud detection and data security.
Leave this session knowing about:
Financial data and Hadoop. What data lends itself to Hadoop? What doesn’t?
Benchmarks from real-world uses of Hadoop in finance
How to effectively migrate, manage, and analyze financial data using Hadoop
The document discusses various topics related to electronic commerce including forms of e-commerce like business-to-business and business-to-consumer, applications, technology infrastructure requirements, electronic payment systems, and strategies for successful e-commerce. It also covers traditional transaction processing methods and systems as well as enterprise resource planning systems.
The document discusses various topics related to electronic commerce including forms of e-commerce like business-to-business and business-to-consumer, key technology infrastructure components for e-commerce like web servers and hosting, electronic payment systems and security, and challenges and strategies for successful e-commerce. It also provides an overview of transaction processing systems, their objectives to process and maintain accurate data, and examples of traditional transaction processing applications.
A graphic language for articulating the state and boundaries of complex systems at a high level. The goal is high level abstraction so that false reductionist data or approximation isn't allowed. High Level Metaphor is used to convey meaning which can then be expanded on.
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Rapid Model Refresh (RMR) in Online Fraud Detection EngineWenSui Liu
This document discusses Rapid Model Refresh (RMR), an approach used by PayPal to continuously update fraud detection models in real-time. It describes:
1. Traditional fraud detection tactics like heuristic, scoring, and rule-based approaches and their limitations for an online setting.
2. PayPal's multi-level fraud detection engine that uses risk scoring, rule induction, and agent review to identify high-risk transactions.
3. The implementation challenges of PayPal's growing international footprint and dynamic fraud trends.
4. How RMR addresses these challenges through automatic model development, real-time deployment, and daily monitoring across data, algorithm, and deployment layers.
The document discusses aspects of autonomic computing applied to peer-to-peer (P2P) systems to manage quality of service. It describes using a monitoring mechanism called SkyEye.KOM to gather statistics on P2P systems in a scalable and self-organizing way. Based on the monitoring data, the system can analyze for deviations from preset quality levels, plan adaptations like changing routing table sizes, and execute adaptations to reach and maintain quality goals. Simulations showed the approach enables P2P systems to precisely reach and hold preset quality intervals through self-configuration.
This Presentation shows how business rules are graphically modeled, managed and delivered - with the business case of Master Data Management at Eurex. It also explains the business impact of this approach.
This presentation shows how to graphically model, manage and deliver business rules with the Eclipse-based tool Visual Rules. The business case used is Master Data Management at Eurex. The presentation also explains the business impact of this approach.
Banking Circle: Money Laundering Beware: A Modern Approach to AML with Machin...Neo4j
The document discusses Banking Circle's use of graph technology and a data-driven approach to improve its anti-money laundering efforts. It represents payment data as a network to extract features for machine learning models that detect suspicious activity. This approach generates fewer false alarms than rules-based systems while identifying more high-risk payments and accounts. Network-based investigations also help analysts explore connections more efficiently. The new system screens over 1 million payments daily and has increased alerts leading to compliance actions by 1300% while reducing total alerts by 30%.
Challenges in building a churn prediction model in different industries, presented by Jelena Pekez from Comtrade System Integration. Talk is focused on real-life use-case experience.
This session examines how global collaboration and emerging corporate cultures are creating a new type of
innovative enterprise: one that is based on services. Discussed will be why this new service-focused approach to business considers not only the service culture, but the technology as well. Each party or participant in service orientation sees themselves as service provider as well as a service consumer, in an increasingly
well-connected global economy. This session focuses on both the cultural service dimension, as well the emerging architecture of service orientation. This session delves deep into the components of the enabling technologies of service-orientation, as well as the cultural aspects of services orientation, where enterprises reach and serve various target communities: their customers, yes, but also their trading partners, employees,
and shareholders. Taught by one of BPM’s early pioneers, Dr. Setrag Khoshafian, this session is intended for both business managers and IT, covering Web Services architectures, service oriented maturity models, ESBs, BPM suites, standards for quality of service, and servant leadership.
This document provides an overview of a SQL Server 2008 for Business Intelligence short course. It discusses the course instructor's background and specialties. The course will cover creating a data warehouse, OLAP cubes, and reports. It will also discuss data mining concepts like why it's used, common algorithms, and include a hands-on lab. Data mining algorithms that will be covered include classification, clustering, decision trees, and neural networks.
Ibm financial crime management solution 3Sunny Fei
The document discusses IBM's financial crime management solution which uses an integrated platform to help banks prevent and manage financial crime more effectively. It analyzes real-time transaction data using customized rules to detect potential fraud cases and monitor high-risk activities. When issues are identified, cases are created and investigators can collaborate to investigate further and take appropriate actions. The solution aims to help banks address the growing challenges of financial crime and optimize fraud prevention.
This document summarizes a presentation given by Revolution Analytics on using R for marketing analytics. It discusses challenges like needing to make decisions faster based on more data and predictive models. It provides examples of companies using Revolution's R software to improve results, such as increasing lift for a client by 14% and saving another $270k. The presentation promotes Revolution's R software for handling big data and analytics faster through techniques like parallel processing and distributed computing. It argues Revolution R is the leading commercial provider of high performance R software.
In today's digital world, credit card fraud is a growing concern. This project explores machine learning techniques for credit card fraud detection. We delve into building models that can identify suspicious transactions in real-time, protecting both consumers and financial institutions. for more detection and machine learning algorithm explore data science and analysis course: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This project showcases an AI-driven approach to detecting credit card fraud using machine learning algorithms. The project utilizes a dataset containing transactions with various features such as transaction amount, location, and time. The goal is to build a predictive model that can accurately identify fraudulent transactions and minimize financial losses for banks and customers. The presentation covers data preprocessing techniques, feature engineering, and the application of machine learning algorithms such as logistic regression or random forests. It also discusses model evaluation metrics and the importance of fraud detection in the banking industry. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
The document discusses four styles of customer service interactions that vary in terms of process complexity and analytics capabilities: low process complexity with low analytics representing commodity CRM; low process complexity with high analytics focusing on process efficiency; high process complexity with low analytics prioritizing process effectiveness; and high process complexity with high analytics automating end-to-end processes through customer intelligence and intelligent dialogue. It also lists representative vendors for each style.
Complex Event Processing (CEP) for Next-Generation Security Event Management,...Tim Bass
Complex Event Processing (CEP) for Next-Generation Security Event Management, Fraud and Intrusion Detection , April 17, 2007 (First Draft), London, Tim Bass, CISSP, Director, Principal Global Architect
Emerging Technologies Group
Big Data vs. Big Risk: Real-Time Trade Surveillance in Financial MarketsArcadia Data
Who’s winning the deep forensic analysis ‘arms race’ for compliance?
Real-time trade surveillance in global financial markets has created a data tsunami.
With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water?
Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations.
Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen.
Hadoop based applications are becoming critical in the financial services arena for the analysis and correlation of large volumes of structured and unstructured data. In addition, the Dodd-Frank Act signifies the largest US financial regulatory change in several decades and requires much greater transparency on financial data. In this session, we will answer common questions and demonstrate use cases in how Hadoop and Datameer help with asset management and risk management, fraud detection and data security.
Leave this session knowing about:
Financial data and Hadoop. What data lends itself to Hadoop? What doesn’t?
Benchmarks from real-world uses of Hadoop in finance
How to effectively migrate, manage, and analyze financial data using Hadoop
The document discusses various topics related to electronic commerce including forms of e-commerce like business-to-business and business-to-consumer, applications, technology infrastructure requirements, electronic payment systems, and strategies for successful e-commerce. It also covers traditional transaction processing methods and systems as well as enterprise resource planning systems.
The document discusses various topics related to electronic commerce including forms of e-commerce like business-to-business and business-to-consumer, key technology infrastructure components for e-commerce like web servers and hosting, electronic payment systems and security, and challenges and strategies for successful e-commerce. It also provides an overview of transaction processing systems, their objectives to process and maintain accurate data, and examples of traditional transaction processing applications.
A graphic language for articulating the state and boundaries of complex systems at a high level. The goal is high level abstraction so that false reductionist data or approximation isn't allowed. High Level Metaphor is used to convey meaning which can then be expanded on.
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