Constellation's Sneak Peak Into Social Business TrendsR "Ray" Wang
The document discusses trends in social business and technology. It summarizes that activity streams are dominating collaborative user experiences, social analytics is forming the foundation for social business, and gamification is entering early enterprise adopters. The document also discusses challenges companies face at different phases of social business maturity and key challenges in 2010 and 2011.
Definitive guide to keyword research and selection. Learn the best practices for gathering, sorting and organizing your keywords into high-performance campaigns.
Behavior-driven development (BDD) is an evolution of test-driven development (TDD). It shifts the vocabulary from being test-based to behavior-based and positions itself as a design philosophy. JBehave is a Java framework for BDD, and this session explores how to write integration tests for your Java EE apps with JBehave. It also demonstrates how to leverage the Context and Dependency Injection (CDI) APIs to implement your tests.
This session is a must-see for all Java EE developers who want a better way to write integration tests aligned with the intended behavior.
S1. Understand Expectations
1. Understand the marketplace and target customers for the system
2. Identify the needs and expectations of end-users based on the features required to fulfill their tasks
3. Consider how the system will be deployed and the different types of end-users it will address
The document outlines a framework for developing personal marketing plans for professionals. It discusses five keys to an effective plan: understanding capabilities and limitations, being realistic, having worthwhile goals, addressing details upfront, and being able to measure outcomes. The framework involves three steps - clarifying the purpose, detailing the approach or tactics, and documenting expectations. For the approach, each marketing tactic is defined along with the timeline for implementation. The overall framework is designed to help professionals develop a doable and measurable personal marketing plan.
Insurance Outsourcing – Ensuring a Brighter Future - September 2011 Everest Group
Everest Group will highlight the findings of its reports on the insurance IT Application Outsourcing (AO) and insurance Business Process Outsourcing (BPO) market covering the following aspects:
1) Insurance outsourcing – Market size and growth: Define insurance outsourcing industry size, review growth trends, understand the key factors driving the growth of insurance outsourcing, and discuss the specific AO and BPO value propositions in light of these drivers
2) Key transaction and adoption trends in Insurance AO & Insurance BPO: Analyze the current IT-BP activity in the insurance sector covering ‘large, multi-year AO relationships’ within IT, and ‘insurance industry specific and non-voice BPO’
3) Emerging priorities and future outlook: Highlight themes that are influencing the sourcing priorities of global insurance majors; and discuss how these changes will impact the AO and BPO outsourcing landscape evolution in the future
Constellation's Sneak Peak Into Social Business TrendsR "Ray" Wang
The document discusses trends in social business and technology. It summarizes that activity streams are dominating collaborative user experiences, social analytics is forming the foundation for social business, and gamification is entering early enterprise adopters. The document also discusses challenges companies face at different phases of social business maturity and key challenges in 2010 and 2011.
Definitive guide to keyword research and selection. Learn the best practices for gathering, sorting and organizing your keywords into high-performance campaigns.
Behavior-driven development (BDD) is an evolution of test-driven development (TDD). It shifts the vocabulary from being test-based to behavior-based and positions itself as a design philosophy. JBehave is a Java framework for BDD, and this session explores how to write integration tests for your Java EE apps with JBehave. It also demonstrates how to leverage the Context and Dependency Injection (CDI) APIs to implement your tests.
This session is a must-see for all Java EE developers who want a better way to write integration tests aligned with the intended behavior.
S1. Understand Expectations
1. Understand the marketplace and target customers for the system
2. Identify the needs and expectations of end-users based on the features required to fulfill their tasks
3. Consider how the system will be deployed and the different types of end-users it will address
The document outlines a framework for developing personal marketing plans for professionals. It discusses five keys to an effective plan: understanding capabilities and limitations, being realistic, having worthwhile goals, addressing details upfront, and being able to measure outcomes. The framework involves three steps - clarifying the purpose, detailing the approach or tactics, and documenting expectations. For the approach, each marketing tactic is defined along with the timeline for implementation. The overall framework is designed to help professionals develop a doable and measurable personal marketing plan.
Insurance Outsourcing – Ensuring a Brighter Future - September 2011 Everest Group
Everest Group will highlight the findings of its reports on the insurance IT Application Outsourcing (AO) and insurance Business Process Outsourcing (BPO) market covering the following aspects:
1) Insurance outsourcing – Market size and growth: Define insurance outsourcing industry size, review growth trends, understand the key factors driving the growth of insurance outsourcing, and discuss the specific AO and BPO value propositions in light of these drivers
2) Key transaction and adoption trends in Insurance AO & Insurance BPO: Analyze the current IT-BP activity in the insurance sector covering ‘large, multi-year AO relationships’ within IT, and ‘insurance industry specific and non-voice BPO’
3) Emerging priorities and future outlook: Highlight themes that are influencing the sourcing priorities of global insurance majors; and discuss how these changes will impact the AO and BPO outsourcing landscape evolution in the future
As the technical skills and costs associated with the deployment of phishing attacks decrease, we are witnessing an unprecedented level of scams that push the need for better methods to proactively detect phishing threats. In this work, we explored the use of URLs as input for machine learning models applied for phishing site prediction. In this way, we compared a feature-engineering approach followed by a random forest classifier against a novel method based on recurrent neural networks. We determined that the recurrent neural network approach provides an accuracy rate of 98.7% even without the need of manual feature creation, beating by 5% the random forest method. This means it is a scalable and fast-acting proactive detection system that does not require full content analysis.
Analytics: Compitiendo en la era de la información
En años recientes el mundo ha entrado en la era de la información, la evolución de la tecnología y el desarrollo de las redes sociales ha permitido a las compañías obtener más información sobre el comportamiento de sus clientes. Adicionalmente, los sistemas se han vuelto mas eficientes y económicos, dando la oportunidad a las empresas de almacenar gran cantidad de datos. Sin embargo, toda esta información solo almacenada no genera valor agregado para las empresas; entonces la pregunta es como obtener beneficios y tomar decisiones mas informadas usando los datos recolectados? La respuesta es analytics.
Analytics es el uso de métodos y herramientas para entender la información y tomar decisiones más precisas. Permite a las compañías hacer predicciones de comportamientos, identificar potenciales clientes, crear segmentaciones inteligentes, dirigir de una manera eficiente campañas publicitarias, identificar riesgos y anticipar cambios en el mercado. Así, por medio de analytics las compañías logran diferenciarse más de sus competidores y entender mejor las necesidades de sus clientes.
Alejandro Correa Bahnsen
Ingeniero Industrial con Maestría en Ingeniería Industrial de la Universidad de los Andes. Candidato a Doctorado en inteligencia artificial de la Universidad de Luxemburgo. Actualmente se encuentra trabajando en Cetrel, el operador de tarjeta de crédito mas grande de Europa, desarrollando un sistema inteligente para la prevención de fraude.
Experiencia como profesor de analytics y econometría en las universidades de Luxemburgo y de los Andes, respectivamente. Conferencista de analytics en SAS Analytics (Orlando, Las Vegas, Londres), SAS Global Forum (Orlando, San Francisco), IEEE International Conference on Data Mining (Vancouver, Bruselas), IEEE International Conference on Machine Learning and Applications (Miami) y European Conference on Data Analysis (Luxemburgo). Fundador de la comunidad Data Science Luxembourg y organizador del workshop IEEE Data Mining Case Studies (Dallas).
Fraud Analytics: Detección y prevención de fraudes en la era del BigData
Durante el 2012 el nivel de fraude en tarjeta de crédito llego a 11.3 billones de dólares, un aumento de casi un 15% comparado con el 2011, esto demuestra el problema que el fraude representa no solo a las instituciones financieras sino también para la sociedad. Tradicionalmente la prevención del fraude consistía en proteger físicamente la infraestructura, sin embargo con cada vez más medios y canales de pago, la información financiera se ha vuelto cada vez más susceptible a ser hurtada. La siguiente opción para prevenir y controlar el fraude consiste en determinar si una transacción está siendo realizada por el cliente de acuerdo con sus patrones históricos de comportamiento. Este es el enfoque de Fraud Analytics.
En esta presentación se mostrara cómo es posible por medio de Fraud Analytics, determinar la probabilidad que una transacción sea o no realizada por el cliente, utilizando la información de compra de los clientes, sus interacciones con la entidad financiera, y por medio de análisis de redes sociales. Adicionalmente, se discutirán y compararan los resultados de las comúnmente utilizadas reglas de decisión y modelos avanzados de inteligencia artificial.
-------------------------------------
Alejandro Correa Bahnsen
-------------------------------------
Ingeniero Industrial con Maestría en Ingeniería Industrial de la Universidad de los Andes. Candidato a Doctorado en inteligencia artificial de la Universidad de Luxemburgo. Actualmente se encuentra trabajando en SIX, uno de los operadores de tarjeta de crédito más grande de Europa, desarrollando un sistema inteligente para la prevención de fraude.
Experiencia como profesor de analytics y econometría en las universidades de Luxemburgo y de los Andes, respectivamente. Conferencista de analytics en SAS Analytics (Orlando, Las Vegas, Londres, Frankfurt), SAS Global Forum (Orlando, San Francisco), IEEE International Conference on Data Mining (Vancouver, Bruselas, Dallas), IEEE International Conference on Machine Learning and Applications (Miami, Detroit) y European Conference on Data Analysis (Luxemburgo). Fundador de la comunidad Data Science Luxembourg y organizador del workshop IEEE Data Mining Case Studies (Dallas).
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
This document summarizes a presentation on maximizing profit from customer churn prediction models using cost-sensitive machine learning techniques. It discusses how traditional evaluation measures like accuracy do not account for different costs of prediction errors. It then covers cost-sensitive approaches like cost-proportionate sampling, Bayes minimum risk, and cost-sensitive decision trees. The results show these cost-sensitive methods improve savings over traditional models and sampling approaches when the business costs are incorporated into the predictive modeling.
Presentation on Modern Data Science
Data scientists are in high demand. There is simply not enough talent to fill the jobs. Why? Because the sexiest job of 21th century requires a mixture of broad, multidisciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business. Finding a data scientist is hard. Finding people who understand who a data scientist is, is equally hard.
Check the video in spanish here :https://www.youtube.com/watch?v=R3jeBHLLiiM
Slides from my PhD defense
Example-Dependent Cost-Sensitive Classification
Applications in Financial Risk Modeling and Marketing Analytics
https://github.com/albahnsen/phd-thesis
Presentation at SAS Analytics conference 2014
Predictive analytics has been applied to solve a wide range of real-world problems. Nevertheless, current state-of-the-art predictive analytics models are not well aligned with business needs since they don't include the real financial costs and benefits during the training and evaluation phases. Churn modeling does not yield the best results when it's measured by investment per subscriber on a loyalty campaign and the financial impact of failing to detect a churner versus wrongly predicting a non-churner. This presentation will show how using a cost-sensitive modeling approach leads to better results in terms of profitability and predictive power – and is applicable to many other business challenges.
Slides of my Pycon 2017 short talk "Demystifying machine learning using lime"
Jupyter Notebook with code in https://github.com/albahnsen/Talk_Demystifying_Machine_Learning
Slides of the paper http://arxiv.org/abs/1505.04637
source code is available at https://github.com/albahnsen/CostSensitiveClassification/blob/master/costcla/models/cost_tree.py#L15
Abstract:
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
THE CUSTOMER IS YOUR MOST IMPORTANT ASSET. HAVING A FANTASTIC PRODUCT OR EXCELLENT PROCESSES WILL NOT RESULT IN MORE REVENUE WHEN THE CUSTOMERS DON’T SEE THE NEED FOR IT. THAT’S WHY YOU NEED TO UNDERSTAND YOUR CUSTOMER AND ALIGN YOUR BUSINESS TOWARDS THEM. COMPANIES THAT SUCCEED IN THIS ARE THE ONES THAT TRULY STAND OUT, BUT EVEN FOR THEM, THIS DIDN’T COME OVERNIGHT.
LET’S EXPLAIN THE DIFFERENT STEPS IN BECOMING MORE CUSTOMER CENTRIC.
The document discusses SAS High-Performance Analytics, a product that leverages in-memory architecture through a dedicated software and hardware appliance to drive high-performance analytics. It highlights how the product addresses the entire analytical lifecycle from data exploration to model deployment to achieve insights at breakthrough speed. Key differentiators of the product include being the only in-memory offering that can develop and deploy high-end analytics models.
An exclusive presentation by Mr. Mazhar Leghari, Business Development Solution Manager, SAS Middle East FZ LLC; on ‘Building for Success: The Foundation for Achievable MDM’. The presentation was made at SAS Forum India 2013.
The document discusses next generation strategies for improving top-line growth. It recommends defining unique value for target customers, executing and measuring lead generation and nurturing programs, and ensuring supply chain and workforce sustainability. The final section encourages defining value, measuring marketing activities, and planning for strong growth in the coming year.
Tony Spelkens is a customer intelligence consultant at SAS Institute. He discusses steps companies can take to become more customer-centric, from basic customer data to predictive models that power personalized campaigns. Customer intelligence maturity progresses from segmentation and targeted newsletters to event-based marketing and offers optimized for individual customers. Case studies show how Zappos focuses on culture but also handles high customer contact volumes, and how Tesco in the UK increased coupon redemption five-fold using targeted vouchers for products customers bought or may buy. Taking a customer-centric approach requires using data intelligence to understand individuals rather than just culture.
Given the recent financial crisis and the extended impact on global credit market and liquidity, it is imperative that financial institutions strengthen their market risk management capabilities to effectively meet compelling business objectives and challenges which include portfolio pricing and portfolio exposure management
Communication principles for complex loyaltyDerek Martin
This document discusses principles for effective communication strategies to drive loyalty in complex loyalty programs. It outlines 5 common sense principles: 1) Keep communication simple with a clear format, function, and brand narrative. 2) Different channels have different costs, complexity, usage and characteristics that must be considered. 3) It is easier to create new behaviors than change existing ones, so manage opt-ins carefully. 4) Context such as a customer's past, present intentions, and future opportunities defines the appropriate message. 5) Value is subjective so focus on relevance, adding value, and building trust over time. The key is avoiding coming across as "creepy" by ensuring messages are relevant, add value, and feel appropriate and trustworthy.
Even with the economic success of the financial industry, there will be challenges credit unions will face. With the use of business analytics and intelligence, your credit union will have a framework for tackling these challenges that will help with developing insights on business performance using statistical methods. So, what results should credit unions expect with the use of analytics? According to a recent survey, business analytics has been proven to reduce costs, increase profitability, improve risk and optimize internal processes for businesses. In this presentation, you will learn how to use business analytics and how the insight it provides will benefit your credit union today and in the future. For more info: www.nafcu.org/sas
As the technical skills and costs associated with the deployment of phishing attacks decrease, we are witnessing an unprecedented level of scams that push the need for better methods to proactively detect phishing threats. In this work, we explored the use of URLs as input for machine learning models applied for phishing site prediction. In this way, we compared a feature-engineering approach followed by a random forest classifier against a novel method based on recurrent neural networks. We determined that the recurrent neural network approach provides an accuracy rate of 98.7% even without the need of manual feature creation, beating by 5% the random forest method. This means it is a scalable and fast-acting proactive detection system that does not require full content analysis.
Analytics: Compitiendo en la era de la información
En años recientes el mundo ha entrado en la era de la información, la evolución de la tecnología y el desarrollo de las redes sociales ha permitido a las compañías obtener más información sobre el comportamiento de sus clientes. Adicionalmente, los sistemas se han vuelto mas eficientes y económicos, dando la oportunidad a las empresas de almacenar gran cantidad de datos. Sin embargo, toda esta información solo almacenada no genera valor agregado para las empresas; entonces la pregunta es como obtener beneficios y tomar decisiones mas informadas usando los datos recolectados? La respuesta es analytics.
Analytics es el uso de métodos y herramientas para entender la información y tomar decisiones más precisas. Permite a las compañías hacer predicciones de comportamientos, identificar potenciales clientes, crear segmentaciones inteligentes, dirigir de una manera eficiente campañas publicitarias, identificar riesgos y anticipar cambios en el mercado. Así, por medio de analytics las compañías logran diferenciarse más de sus competidores y entender mejor las necesidades de sus clientes.
Alejandro Correa Bahnsen
Ingeniero Industrial con Maestría en Ingeniería Industrial de la Universidad de los Andes. Candidato a Doctorado en inteligencia artificial de la Universidad de Luxemburgo. Actualmente se encuentra trabajando en Cetrel, el operador de tarjeta de crédito mas grande de Europa, desarrollando un sistema inteligente para la prevención de fraude.
Experiencia como profesor de analytics y econometría en las universidades de Luxemburgo y de los Andes, respectivamente. Conferencista de analytics en SAS Analytics (Orlando, Las Vegas, Londres), SAS Global Forum (Orlando, San Francisco), IEEE International Conference on Data Mining (Vancouver, Bruselas), IEEE International Conference on Machine Learning and Applications (Miami) y European Conference on Data Analysis (Luxemburgo). Fundador de la comunidad Data Science Luxembourg y organizador del workshop IEEE Data Mining Case Studies (Dallas).
Fraud Analytics: Detección y prevención de fraudes en la era del BigData
Durante el 2012 el nivel de fraude en tarjeta de crédito llego a 11.3 billones de dólares, un aumento de casi un 15% comparado con el 2011, esto demuestra el problema que el fraude representa no solo a las instituciones financieras sino también para la sociedad. Tradicionalmente la prevención del fraude consistía en proteger físicamente la infraestructura, sin embargo con cada vez más medios y canales de pago, la información financiera se ha vuelto cada vez más susceptible a ser hurtada. La siguiente opción para prevenir y controlar el fraude consiste en determinar si una transacción está siendo realizada por el cliente de acuerdo con sus patrones históricos de comportamiento. Este es el enfoque de Fraud Analytics.
En esta presentación se mostrara cómo es posible por medio de Fraud Analytics, determinar la probabilidad que una transacción sea o no realizada por el cliente, utilizando la información de compra de los clientes, sus interacciones con la entidad financiera, y por medio de análisis de redes sociales. Adicionalmente, se discutirán y compararan los resultados de las comúnmente utilizadas reglas de decisión y modelos avanzados de inteligencia artificial.
-------------------------------------
Alejandro Correa Bahnsen
-------------------------------------
Ingeniero Industrial con Maestría en Ingeniería Industrial de la Universidad de los Andes. Candidato a Doctorado en inteligencia artificial de la Universidad de Luxemburgo. Actualmente se encuentra trabajando en SIX, uno de los operadores de tarjeta de crédito más grande de Europa, desarrollando un sistema inteligente para la prevención de fraude.
Experiencia como profesor de analytics y econometría en las universidades de Luxemburgo y de los Andes, respectivamente. Conferencista de analytics en SAS Analytics (Orlando, Las Vegas, Londres, Frankfurt), SAS Global Forum (Orlando, San Francisco), IEEE International Conference on Data Mining (Vancouver, Bruselas, Dallas), IEEE International Conference on Machine Learning and Applications (Miami, Detroit) y European Conference on Data Analysis (Luxemburgo). Fundador de la comunidad Data Science Luxembourg y organizador del workshop IEEE Data Mining Case Studies (Dallas).
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
This document summarizes a presentation on maximizing profit from customer churn prediction models using cost-sensitive machine learning techniques. It discusses how traditional evaluation measures like accuracy do not account for different costs of prediction errors. It then covers cost-sensitive approaches like cost-proportionate sampling, Bayes minimum risk, and cost-sensitive decision trees. The results show these cost-sensitive methods improve savings over traditional models and sampling approaches when the business costs are incorporated into the predictive modeling.
Presentation on Modern Data Science
Data scientists are in high demand. There is simply not enough talent to fill the jobs. Why? Because the sexiest job of 21th century requires a mixture of broad, multidisciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business. Finding a data scientist is hard. Finding people who understand who a data scientist is, is equally hard.
Check the video in spanish here :https://www.youtube.com/watch?v=R3jeBHLLiiM
Slides from my PhD defense
Example-Dependent Cost-Sensitive Classification
Applications in Financial Risk Modeling and Marketing Analytics
https://github.com/albahnsen/phd-thesis
Presentation at SAS Analytics conference 2014
Predictive analytics has been applied to solve a wide range of real-world problems. Nevertheless, current state-of-the-art predictive analytics models are not well aligned with business needs since they don't include the real financial costs and benefits during the training and evaluation phases. Churn modeling does not yield the best results when it's measured by investment per subscriber on a loyalty campaign and the financial impact of failing to detect a churner versus wrongly predicting a non-churner. This presentation will show how using a cost-sensitive modeling approach leads to better results in terms of profitability and predictive power – and is applicable to many other business challenges.
Slides of my Pycon 2017 short talk "Demystifying machine learning using lime"
Jupyter Notebook with code in https://github.com/albahnsen/Talk_Demystifying_Machine_Learning
Slides of the paper http://arxiv.org/abs/1505.04637
source code is available at https://github.com/albahnsen/CostSensitiveClassification/blob/master/costcla/models/cost_tree.py#L15
Abstract:
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
THE CUSTOMER IS YOUR MOST IMPORTANT ASSET. HAVING A FANTASTIC PRODUCT OR EXCELLENT PROCESSES WILL NOT RESULT IN MORE REVENUE WHEN THE CUSTOMERS DON’T SEE THE NEED FOR IT. THAT’S WHY YOU NEED TO UNDERSTAND YOUR CUSTOMER AND ALIGN YOUR BUSINESS TOWARDS THEM. COMPANIES THAT SUCCEED IN THIS ARE THE ONES THAT TRULY STAND OUT, BUT EVEN FOR THEM, THIS DIDN’T COME OVERNIGHT.
LET’S EXPLAIN THE DIFFERENT STEPS IN BECOMING MORE CUSTOMER CENTRIC.
The document discusses SAS High-Performance Analytics, a product that leverages in-memory architecture through a dedicated software and hardware appliance to drive high-performance analytics. It highlights how the product addresses the entire analytical lifecycle from data exploration to model deployment to achieve insights at breakthrough speed. Key differentiators of the product include being the only in-memory offering that can develop and deploy high-end analytics models.
An exclusive presentation by Mr. Mazhar Leghari, Business Development Solution Manager, SAS Middle East FZ LLC; on ‘Building for Success: The Foundation for Achievable MDM’. The presentation was made at SAS Forum India 2013.
The document discusses next generation strategies for improving top-line growth. It recommends defining unique value for target customers, executing and measuring lead generation and nurturing programs, and ensuring supply chain and workforce sustainability. The final section encourages defining value, measuring marketing activities, and planning for strong growth in the coming year.
Tony Spelkens is a customer intelligence consultant at SAS Institute. He discusses steps companies can take to become more customer-centric, from basic customer data to predictive models that power personalized campaigns. Customer intelligence maturity progresses from segmentation and targeted newsletters to event-based marketing and offers optimized for individual customers. Case studies show how Zappos focuses on culture but also handles high customer contact volumes, and how Tesco in the UK increased coupon redemption five-fold using targeted vouchers for products customers bought or may buy. Taking a customer-centric approach requires using data intelligence to understand individuals rather than just culture.
Given the recent financial crisis and the extended impact on global credit market and liquidity, it is imperative that financial institutions strengthen their market risk management capabilities to effectively meet compelling business objectives and challenges which include portfolio pricing and portfolio exposure management
Communication principles for complex loyaltyDerek Martin
This document discusses principles for effective communication strategies to drive loyalty in complex loyalty programs. It outlines 5 common sense principles: 1) Keep communication simple with a clear format, function, and brand narrative. 2) Different channels have different costs, complexity, usage and characteristics that must be considered. 3) It is easier to create new behaviors than change existing ones, so manage opt-ins carefully. 4) Context such as a customer's past, present intentions, and future opportunities defines the appropriate message. 5) Value is subjective so focus on relevance, adding value, and building trust over time. The key is avoiding coming across as "creepy" by ensuring messages are relevant, add value, and feel appropriate and trustworthy.
Even with the economic success of the financial industry, there will be challenges credit unions will face. With the use of business analytics and intelligence, your credit union will have a framework for tackling these challenges that will help with developing insights on business performance using statistical methods. So, what results should credit unions expect with the use of analytics? According to a recent survey, business analytics has been proven to reduce costs, increase profitability, improve risk and optimize internal processes for businesses. In this presentation, you will learn how to use business analytics and how the insight it provides will benefit your credit union today and in the future. For more info: www.nafcu.org/sas
Zd sap - predictive analytics - 3-26-13 r1Richard Lee
Richard Lee is an executive consultant who advises companies on predictive analytics and becoming predictive enterprises. Predictive analytics uses modeling, machine learning and data mining to analyze past and present data to predict future events. It has evolved from descriptive analytics in the past to now aiming to embed predictive analysis into real-time applications. Becoming a predictive enterprise requires using all data sources and predictive models across the organization to gain insights.
Here are the main tasks and places users engage with on this site:
Tasks:
- View stories
- Log in
- Create account
Places:
- Landing page
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- Comments section
The key metrics would be:
- Bounce rate from landing page
- Time on site
- Engagement (votes, comments)
- Conversion to registered users
Let me know if you need any clarification or have additional questions!
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Robert Moberg, Prediktiv Analysexpert, IBM Sverige
The document discusses how utilities companies can use analytics to improve various areas of their operations, such as load forecasting, asset management, and customer management. It provides examples of utilities reducing costs and improving forecast accuracy through analytics. SAS analytics solutions help utilities optimize risk, asset availability, and customer service while reducing losses and increasing savings.
Prezentarea sustinuta de catre Ionut Boldizsar, CEO New Tech Consulting, in cadrul conferintei regionale Marketing 24/7 Sibiu 2-3 Iunie 2011 Hotel Ibis
www.marketing247.ro
The document discusses how companies can anticipate and manage change by innovating, optimizing, and striking a balance between the two. It emphasizes the need to leverage business analytics across various business functions to gain quantifiable benefits. It provides examples of traditional business intelligence techniques and how analytics is moving beyond those to areas like predictive modeling and optimization. The document concludes by suggesting six key questions for companies to consider to focus their business analytics efforts.
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Deal Terms, Pricing, and Valuations of the latest financing rounds for TaskRabbit, Inc. Similar data on thousands of private companies is available in the Valuation & Deal Term Database at http://vcexperts.com.
Jim Davis, Senior Vice President and Chief Marketing Officer, SAS, presented at the Premier Business Leadership Series, 2010. http://www.sas.com/theserieshk.
Organisations today are dealing with diverse issues, a wider range of regulations and heightened global competition. There has never been a greater urgency for proactive, evidence-based decisions and agile strategies.
By delivering insights that are gleaned from data about customers, suppliers, operations, performance and more, Davis will explore how leading organisations are solving complex business problems, lifting performance and driving sustainable growth through business analytics.
This document provides an overview of analytics and its increasing use in business. It discusses:
- What analytics is and common business questions it can answer with different techniques.
- Examples of how various industries use analytics in areas like marketing, risk management, and operations.
- Data showing analytics adoption is growing as more companies recognize its benefits.
- Different analytics tools and techniques ranging from basic to advanced.
- Potential career paths in analytics fields like statistical modeling, software development, and domain expertise.
- How even small and medium enterprises can leverage analytics for solutions like management systems, data-driven decisions, and online marketing optimization.
Similar to 2011 advanced analytics through the credit cycle (20)
This document discusses using machine learning and deep learning techniques to detect malicious URLs and TLS certificates. It describes building recurrent neural network models to classify URLs and certificates based on their content. The models were able to achieve over 98% accuracy on URL classification and can be used to detect phishing URLs and malicious TLS certificates that aim to mimic legitimate ones. A demo of these techniques is also mentioned. The goal is to develop more robust detection of malicious AI that is trying to simulate legitimate behavior.
In this work we describe how threat actors may use AI algorithms to bypass AI phishing detection systems. We analyzed more than a million phishing URLs to understand the different strategies that threat actors use to create phishing URLs. Assuming the role of an attacker, we simulate how different threat actors may leverage Deep Neural Networks to enhance their effectiveness rate. Using Long Short-Term Memory Networks, we created DeepPhish, an algorithm that learns to create better phishing attacks. By training the DeepPhish algorithm for two different threat actors, they were able to increase their effectiveness from 0.69% to 20.9%, and 4.91% to 36.28%, respectively.
This document discusses how artificial intelligence could be used by hackers to improve cyberattacks like phishing. The author details an experiment where threat actors were able to improve their phishing attacks using AI, making them 3000% more successful. The document argues that AI enhances attackers' efficiency and that companies need multi-layered AI and machine learning detection systems as well as deep adversarial learning to monitor threats and fight against adversary AI. It maintains that companies must take action now as AI amplifies the power of attacks like phishing, malware, and weakening authentication controls.
Most people think a successful data product requires just three things: data, the
right algorithm, and good execution. But as anyone who’s tried to create one
knows, an effective product requires much more. In this talk, Dr. Correa Bahnsen
will share his successes—and failures—in building data products for information
security, and why an isolated data science team is a recipe for failure.
Worldwide, billions of euros are lost every year due to credit card fraud. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative new fraud patterns emerge. Hence, it remains challenging to find effective methods of mitigating fraud. Existing solutions include simple if-then rules and classical machine learning algorithms. Credit card fraud is by definition an example-dependent and cost-sensitive classification problem, in which the costs due to is classification vary between examples and not only within classes, i.e., misclassifying a fraudulent transaction may have a financial impact ranging from a few to thousands of euros. In this paper, we propose an extension to the cost-sensitive decision trees algorithm, by creating an ensemble of such trees, and combining them using a stacking approach with a cost-sensitive logistic regression. We compare our method with standard machine learning algorithms and state-of-the-art cost-sensitive classification methods using a real credit card fraud dataset provided by a large European card processing company. The results show that our method achieves savings of up to 73.3%, more than 2 percentage points more than a single cost-sensitive decision tree.
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BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
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Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
Brian Fitzsimmons on the Business Strategy and Content Flywheel of Barstool S...Neil Horowitz
On episode 272 of the Digital and Social Media Sports Podcast, Neil chatted with Brian Fitzsimmons, Director of Licensing and Business Development for Barstool Sports.
What follows is a collection of snippets from the podcast. To hear the full interview and more, check out the podcast on all podcast platforms and at www.dsmsports.net
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Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations