|By Roger Barga, Avinash Joshi, Pravin Venugopal||
|June 27, 2014 10:15 AM EDT||
This article explores how to detect fraud among online banking customers in real-time by running an ensemble of statistical and machine learning algorithms on a dataset of customer transactions and demographic data. The algorithms, namely Logistic Regression, Self-Organizing Maps and Support Vector Machines, are operationalized using a multi-agent framework for real-time data analysis. This article also explores the cloud environment for real-time analytics by deploying the agent framework in a cloud environment that meets computational demands by letting users' provision virtual machines within managed data centers, freeing them from the worry of acquiring and setting up new hardware and networks.
Real-time decision making is becoming increasingly valuable with the advancement of data collection and analytics techniques. Due to the increase in the speed of processing, the classical data warehousing model is moving toward a real-time model. A platform that enables the rapid development and deployment of applications, reducing the lag between data acquisition and actionable insight has become of paramount importance in the corporate world. Such a system can be used for the classic case of deriving information from data collected in the past and also to have a real-time engine that reacts to events as they occur. Some examples of such applications include:
- A product company can get real-time feedback for their new releases using data from social media
- Algorithmic trading by reacting in real times to fluctuations in stock prices
- Real-time recommendations for food and entertainment based on a customer's location
- Traffic signal operations based on real-time information of volume of traffic
- E-commerce websites can detect a customer transaction being authentic or fraudulent in real-time
A cloud-based ecosystem enables users to build an application that detects, in real-time, fraudulent customers based on their demographic information and financial history. Multiple algorithms are utilized to detect fraud and the output is aggregated to improve prediction accuracy.
The dataset used to demonstrate this application comprises of various customer demographic variables and financial information such as age, residential address, office address, income type, income frequency, bankruptcy filing status, etc. The dependent variable (the variable to be predicted) is called "bad", which is a binary variable taking the value 0 (for not fraud) or 1 (for fraud).
Using Cloud for Effective Usage of Resources
A system that allows the development of applications capable of churning out results in real-time has multiple services running in tandem and is highly resource intensive. By deploying the system in the cloud, maintenance and load balancing of the system can be handled efficiently. It will also give the user more time to focus on application development. For the purpose of fraud detection, the active components, for example, include:
- Web services
This approach combines the strengths and synergies of both cloud computing and machine learning technologies, providing a small company or even a startup that is unlikely to have specialized staff and necessary infrastructure for what is a computationally intensive approach, the ability to build a system that make decisions based on historical transactions.
As multiple algorithms are to be run on the same data, a real-time agent paradigm is chosen to run these algorithms. An agent is an autonomous entity that may expect inputs and send outputs after performing a set of instructions. In a real-time system, these agents are wired together with directed connections to form an agency. An agent typically has two behaviors, cyclic and triggered. Cyclic agents, as the name suggests, run continuously in a loop and do not need any input. These are usually the first agents in an agency and are used for streaming data to the agency by connecting to an external real-time data source. A triggered agent runs every time it receives a message from a cyclic agent or another triggered agent. Once it consumes one message, it waits for the next message to arrive.
Figure 1: A simple agency with two agents
In Figure 1, Agent 1 is a cyclic agent while Agent 2 is a triggered agent. Agent 1 finishes its computation and sends a message to Agent 2, which uses the message as an input for further computation.
Feature Selection and Data Treatment
The dataset used for demonstrating fraud detection agency has 250 variables (features) pertaining to the demographic and financial history of the customers. To reduce the number of features, a Random Forest run was conducted on the dataset to obtain variable importance. Next, the top 30 variables were selected based on the variable importance. This reduced dataset was used for running a list of classification algorithms.
Algorithms for Fraud Detection
The fraud detection problem is a binary classification problem for which we have chosen three different algorithms to classify the input data into fraud (1) and not fraud (0). Each algorithm is configured as a triggered agent for our real-time system.
This is a probabilistic classification model where the dependent variable (the variable to be predicted) is a binary variable or a categorical variable. In case of binary dependent variables favorable outcomes are represented as 1 and non-favorable outcomes are represented as 0. Logistic regression models the probability of the dependent variable taking the value 0 or 1.
For the fraud detection problem, the dependent variable "bad" is modelled to give probabilities to each customer of being fraud or not. The equation takes multiple variables as input and returns a value between 0 & 1 which is the probability of "bad" being 0. If this value is greater than 0.7, then that customer is classified as not fraud.
Self-Organizing Maps (SOM)
This is an artificial neural network that uses unsupervised learning to represent the data in lower (typically two dimensions) dimensions. This representation of the input data in lower dimensions is called a map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. "Training" builds the map using input examples, while "mapping" automatically classifies a new input vector.
For the fraud detection problem, the input space which is a fifty dimensional space is mapped to a two dimensional lattice of nodes. The training is done using data from the recent past and the new data is mapped using the trained model, which puts it either in the "fraud" cluster or "not - fraud" cluster.
Figure 2: x is an in-put vector in higher dimension, discretized in 2D using wij as the weight matrix
Image Source: http://www.lohninger.com/helpcsuite/kohonen_network_-_background_information.htm
Support Vector Machines (SVM)
This is a supervised learning technique used generally for classifying data. It needs a training dataset where the data is already classified into the required categories. It creates a hyperplane or set of hyperplanes that can be used for classification. The hyperplane is chosen such that it separates the different classes and the margin between the samples in the training set is widest.
For the fraud detection problem, SVM classifies the data points into two classes. The hyperplane is chosen by training the model over the past data. Using the variable "bad", the clusters are labeled as "0" (fraud) and "1" (not fraud). The new data points are classified using the hyperplane obtained while training.
Figure 3: Of the three hyperplanes which segment the data, H2 is the hyperplane which classifies the data accurately
Image Source: http://en.wikipedia.org/wiki/File:Svm_separating_hyperplanes.png
Fraud Detection Agency
A four-tier agency is created to build a workflow process for fraud detection.
Streamer Agent (Tier 1): This agent streams data in real-time to agents in Tier 2. It is the first agent in the agency and its behavior is cyclic. It connects to a real-time data source, pre-processes the data and sends it to the agents in the next layer.
Algorithm Agents (Tier 2): This tier has multiple agents running an ensemble of algorithms with one agent per algorithm. Each agent receives the message from the streamer agent and uses a pre-trained (trained on historical data) model for scoring.
Collator Agent (Tier 3): This agent receives scores from agents in Tier 2 and generates a single score by aggregating the scores. It then converts the score into an appropriate JSON format and sends it to an UI agent for consumption.
User Interface Agent (Tier 4): This agent pushes the messages it receives to a socket server. Any external socket client can be used to consume these messages.
Figure 4: The Fraud detection agency with agents in each layer. The final agent is mapped to a port to which a socket client can connect
Results and Model Validation
The models were trained on 70% of the data and the remaining 30% of the data was streamed to the above agency simulating a real-time data source.
Under-sample: The ratio of number of 0s to the number of 1s in the original dataset for the variable "bad" is 20:1. This would lead to biasing the models towards 0. To overcome this, we sample the training dataset by under-sampling the number of 0s to maintain the ration at 10:1.
The final output of the agency is the classification of the input as fraudulent or not. Since the value for the variable "bad" is already known for this data, it helps us gauge the accuracy of the aggregated model.
Figure 5: Accuracy for detecting fraud ("bad"=1) for different sampling ratio between no.of 0s and no. of 1s in the training dataset
Fraud detection can be improved by running an ensemble of algorithms in parallel and aggregating the predictions in real-time. This entire end-to-end application was designed and deployed in three working days. This shows the power of a system that enables easy deployment of real-time analytics applications. The work flow becomes inherently parallel as these agents run as separate processes communicating with each other. Deploying this in the cloud makes it horizontally scalable owing to effective load balancing and hardware maintenance. It also provides higher data security and makes the system fault tolerant by making processes mobile. This combination of a real-time application development system and a cloud-based computing enables even non-technical teams to rapidly deploy applications.
- Gravic Inc, "The Evolution of Real-Time Business Intelligence", "http://www.gravic.com/shadowbase/pdf/white-papers/Shadowbase-for-Real-Time-Business-Intelligence.pdf"
- Bernhard Schlkopf, Alexander J. Smola ( 2002), "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)", MIT Press
- Christopher Burges (1998), "A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery, Kluwer Publishers
- Kohonen, T. (Sep 1990), "The self-organizing map", Proceedings of IEEE
- Samuel Kaski (1997), "Data Exploration Using Self-Organizing Maps", ACTA POLYTECHNICA SCANDINAVICA: MATHEMATICS, COMPUTING AND MANAGEMENT IN ENGINEERING SERIES NO. 82,
- Rokach, L. (2010). "Ensemble based classifiers". Artificial Intelligence Review
- Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, "Variable Selection using Random Forests", http://robin.genuer.fr/genuer-poggi-tuleau.varselect-rf.preprint.pdf
In the next forty months – just over three years – businesses will undergo extraordinary changes. The exponential growth of digitization and machine learning will see a step function change in how businesses create value, satisfy customers, and outperform their competition. In the next forty months companies will take the actions that will see them get to the next level of the game called Capitalism. Or they won’t – game over. The winners of today and tomorrow think differently, follow different...
Oct. 22, 2016 04:30 AM EDT Reads: 793
One of biggest questions about Big Data is “How do we harness all that information for business use quickly and effectively?” Geographic Information Systems (GIS) or spatial technology is about more than making maps, but adding critical context and meaning to data of all types, coming from all different channels – even sensors. In his session at @ThingsExpo, William (Bill) Meehan, director of utility solutions for Esri, will take a closer look at the current state of spatial technology and ar...
Oct. 22, 2016 03:30 AM EDT Reads: 1,661
The Open Connectivity Foundation (OCF), sponsor of the IoTivity open source project, and AllSeen Alliance, which provides the AllJoyn® open source IoT framework, today announced that the two organizations’ boards have approved a merger under the OCF name and bylaws. This merger will advance interoperability between connected devices from both groups, enabling the full operating potential of IoT and representing a significant step towards a connected ecosystem.
Oct. 22, 2016 02:45 AM EDT Reads: 1,148
SYS-CON Media announced today that @WebRTCSummit Blog, the largest WebRTC resource in the world, has been launched. @WebRTCSummit Blog offers top articles, news stories, and blog posts from the world's well-known experts and guarantees better exposure for its authors than any other publication. @WebRTCSummit Blog can be bookmarked ▸ Here @WebRTCSummit conference site can be bookmarked ▸ Here
Oct. 22, 2016 01:30 AM EDT Reads: 9,608
SYS-CON Events announced today that Streamlyzer will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Streamlyzer is a powerful analytics for video streaming service that enables video streaming providers to monitor and analyze QoE (Quality-of-Experience) from end-user devices in real time.
Oct. 22, 2016 01:15 AM EDT Reads: 889
You have great SaaS business app ideas. You want to turn your idea quickly into a functional and engaging proof of concept. You need to be able to modify it to meet customers' needs, and you need to deliver a complete and secure SaaS application. How could you achieve all the above and yet avoid unforeseen IT requirements that add unnecessary cost and complexity? You also want your app to be responsive in any device at any time. In his session at 19th Cloud Expo, Mark Allen, General Manager of...
Oct. 22, 2016 01:15 AM EDT Reads: 806
@ThingsExpo has been named the Top 5 Most Influential Internet of Things Brand by Onalytica in the ‘The Internet of Things Landscape 2015: Top 100 Individuals and Brands.' Onalytica analyzed Twitter conversations around the #IoT debate to uncover the most influential brands and individuals driving the conversation. Onalytica captured data from 56,224 users. The PageRank based methodology they use to extract influencers on a particular topic (tweets mentioning #InternetofThings or #IoT in this ...
Oct. 22, 2016 01:00 AM EDT Reads: 8,149
SYS-CON Events announced today that Super Micro Computer, Inc., a global leader in Embedded and IoT solutions, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 7-9, 2017, at the Javits Center in New York City, NY. Supermicro (NASDAQ: SMCI), the leading innovator in high-performance, high-efficiency server technology, is a premier provider of advanced server Building Block Solutions® for Data Center, Cloud Computing, Enterprise IT, Hadoop/Big Data, HPC and ...
Oct. 22, 2016 12:30 AM EDT Reads: 3,490
Cloud based infrastructure deployment is becoming more and more appealing to customers, from Fortune 500 companies to SMEs due to its pay-as-you-go model. Enterprise storage vendors are able to reach out to these customers by integrating in cloud based deployments; this needs adaptability and interoperability of the products confirming to cloud standards such as OpenStack, CloudStack, or Azure. As compared to off the shelf commodity storage, enterprise storages by its reliability, high-availabil...
Oct. 22, 2016 12:15 AM EDT Reads: 966
Explosive growth in connected devices. Enormous amounts of data for collection and analysis. Critical use of data for split-second decision making and actionable information. All three are factors in making the Internet of Things a reality. Yet, any one factor would have an IT organization pondering its infrastructure strategy. How should your organization enhance its IT framework to enable an Internet of Things implementation? In his session at @ThingsExpo, James Kirkland, Red Hat's Chief Arch...
Oct. 22, 2016 12:00 AM EDT Reads: 5,935
The IoT industry is now at a crossroads, between the fast-paced innovation of technologies and the pending mass adoption by global enterprises. The complexity of combining rapidly evolving technologies and the need to establish practices for market acceleration pose a strong challenge to global enterprises as well as IoT vendors. In his session at @ThingsExpo, Clark Smith, senior product manager for Numerex, will discuss how Numerex, as an experienced, established IoT provider, has embraced a ...
Oct. 22, 2016 12:00 AM EDT Reads: 940
We are reaching the end of the beginning with WebRTC, and real systems using this technology have begun to appear. One challenge that faces every WebRTC deployment (in some form or another) is identity management. For example, if you have an existing service – possibly built on a variety of different PaaS/SaaS offerings – and you want to add real-time communications you are faced with a challenge relating to user management, authentication, authorization, and validation. Service providers will w...
Oct. 21, 2016 11:45 PM EDT Reads: 3,219
When people aren’t talking about VMs and containers, they’re talking about serverless architecture. Serverless is about no maintenance. It means you are not worried about low-level infrastructural and operational details. An event-driven serverless platform is a great use case for IoT. In his session at @ThingsExpo, Animesh Singh, an STSM and Lead for IBM Cloud Platform and Infrastructure, will detail how to build a distributed serverless, polyglot, microservices framework using open source tec...
Oct. 21, 2016 11:45 PM EDT Reads: 4,418
November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Penta Security is a leading vendor for data security solutions, including its encryption solution, D’Amo. By using FPE technology, D’Amo allows for the implementation of encryption technology to sensitive data fields without modification to schema in the database environment. With businesses having their data become increasingly more complicated in their mission-critical applications (such as ERP, CRM, HRM), continued ...
Oct. 21, 2016 11:45 PM EDT Reads: 873
SYS-CON Events announced today that Cloudbric, a leading website security provider, will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Cloudbric is an elite full service website protection solution specifically designed for IT novices, entrepreneurs, and small and medium businesses. First launched in 2015, Cloudbric is based on the enterprise level Web Application Firewall by Penta Security Sys...
Oct. 21, 2016 11:45 PM EDT Reads: 989
A completely new computing platform is on the horizon. They’re called Microservers by some, ARM Servers by others, and sometimes even ARM-based Servers. No matter what you call them, Microservers will have a huge impact on the data center and on server computing in general. Although few people are familiar with Microservers today, their impact will be felt very soon. This is a new category of computing platform that is available today and is predicted to have triple-digit growth rates for some ...
Oct. 21, 2016 10:00 PM EDT Reads: 33,900
You think you know what’s in your data. But do you? Most organizations are now aware of the business intelligence represented by their data. Data science stands to take this to a level you never thought of – literally. The techniques of data science, when used with the capabilities of Big Data technologies, can make connections you had not yet imagined, helping you discover new insights and ask new questions of your data. In his session at @ThingsExpo, Sarbjit Sarkaria, data science team lead ...
Oct. 21, 2016 10:00 PM EDT Reads: 4,221
SYS-CON Events announced today that Roundee / LinearHub will exhibit at the WebRTC Summit at @ThingsExpo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. LinearHub provides Roundee Service, a smart platform for enterprise video conferencing with enhanced features such as automatic recording and transcription service. Slack users can integrate Roundee to their team via Slack’s App Directory, and '/roundee' command lets your video conference ...
Oct. 21, 2016 09:15 PM EDT Reads: 1,985
SYS-CON Events announced today that Enzu will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Enzu’s mission is to be the leading provider of enterprise cloud solutions worldwide. Enzu enables online businesses to use its IT infrastructure to their competitive advantage. By offering a suite of proven hosting and management services, Enzu wants companies to focus on the core of their online busine...
Oct. 21, 2016 09:15 PM EDT Reads: 1,204
SYS-CON Events announced today that SoftNet Solutions will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. SoftNet Solutions specializes in Enterprise Solutions for Hadoop and Big Data. It offers customers the most open, robust, and value-conscious portfolio of solutions, services, and tools for the shortest route to success with Big Data. The unique differentiator is the ability to architect and...
Oct. 21, 2016 08:15 PM EDT Reads: 373