|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
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
Nov. 25, 2015 09:00 AM EST Reads: 261
As organizations realize the scope of the Internet of Things, gaining key insights from Big Data, through the use of advanced analytics, becomes crucial. However, IoT also creates the need for petabyte scale storage of data from millions of devices. A new type of Storage is required which seamlessly integrates robust data analytics with massive scale. These storage systems will act as “smart systems” provide in-place analytics that speed discovery and enable businesses to quickly derive meaningful and actionable insights. In his session at @ThingsExpo, Paul Turner, Chief Marketing Officer at...
Nov. 25, 2015 08:15 AM EST Reads: 341
DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, demonstrated examples of com...
Nov. 25, 2015 07:45 AM EST Reads: 339
In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
Nov. 25, 2015 07:30 AM EST Reads: 246
The Internet of Everything is re-shaping technology trends–moving away from “request/response” architecture to an “always-on” Streaming Web where data is in constant motion and secure, reliable communication is an absolute necessity. As more and more THINGS go online, the challenges that developers will need to address will only increase exponentially. In his session at @ThingsExpo, Todd Greene, Founder & CEO of PubNub, exploreed the current state of IoT connectivity and review key trends and technology requirements that will drive the Internet of Things from hype to reality.
Nov. 25, 2015 05:45 AM EST Reads: 375
Two weeks ago (November 3-5), I attended the Cloud Expo Silicon Valley as a speaker, where I presented on the security and privacy due diligence requirements for cloud solutions. Cloud security is a topical issue for every CIO, CISO, and technology buyer. Decision-makers are always looking for insights on how to mitigate the security risks of implementing and using cloud solutions. Based on the presentation topics covered at the conference, as well as the general discussions heard between sessions, I wanted to share some of my observations on emerging trends. As cyber security serves as a fou...
Nov. 25, 2015 05:45 AM EST Reads: 287
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Nov. 25, 2015 02:30 AM EST Reads: 679
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
Nov. 25, 2015 02:00 AM EST Reads: 287
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Nov. 25, 2015 12:30 AM EST Reads: 410
With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now all corporate assets – people, objects, and spaces – can share information about themselves and thei...
Nov. 25, 2015 12:00 AM EST Reads: 161
PubNub has announced the release of BLOCKS, a set of customizable microservices that give developers a simple way to add code and deploy features for realtime apps.PubNub BLOCKS executes business logic directly on the data streaming through PubNub’s network without splitting it off to an intermediary server controlled by the customer. This revolutionary approach streamlines app development, reduces endpoint-to-endpoint latency, and allows apps to better leverage the enormous scalability of PubNub’s Data Stream Network.
Nov. 24, 2015 10:00 PM EST Reads: 259
I recently attended and was a speaker at the 4th International Internet of @ThingsExpo at the Santa Clara Convention Center. I also had the opportunity to attend this event last year and I wrote a blog from that show talking about how the “Enterprise Impact of IoT” was a key theme of last year’s show. I was curious to see if the same theme would still resonate 365 days later and what, if any, changes I would see in the content presented.
Nov. 24, 2015 08:00 PM EST Reads: 342
Apps and devices shouldn't stop working when there's limited or no network connectivity. Learn how to bring data stored in a cloud database to the edge of the network (and back again) whenever an Internet connection is available. In his session at 17th Cloud Expo, Ben Perlmutter, a Sales Engineer with IBM Cloudant, demonstrated techniques for replicating cloud databases with devices in order to build offline-first mobile or Internet of Things (IoT) apps that can provide a better, faster user experience, both offline and online. The focus of this talk was on IBM Cloudant, Apache CouchDB, and ...
Nov. 24, 2015 07:30 PM EST Reads: 346
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" in this scenario: microservice A (releases daily) depends on a couple of additions to backend B (re...
Nov. 24, 2015 06:00 PM EST Reads: 370
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
Nov. 24, 2015 03:30 PM EST Reads: 462
There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content. Join @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 7-9, 2016 in New York City, for three days of intense 'Internet of Things' discussion and focus, including Big Data's indespensable role in IoT, Smart Grids and Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) IoT's use in Vertical Markets.
Nov. 24, 2015 03:30 PM EST Reads: 510
Container technology is shaping the future of DevOps and it’s also changing the way organizations think about application development. With the rise of mobile applications in the enterprise, businesses are abandoning year-long development cycles and embracing technologies that enable rapid development and continuous deployment of apps. In his session at DevOps Summit, Kurt Collins, Developer Evangelist at Built.io, examined how Docker has evolved into a highly effective tool for application delivery by allowing increasingly popular Mobile Backend-as-a-Service (mBaaS) platforms to quickly crea...
Nov. 24, 2015 03:00 PM EST Reads: 290
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
Nov. 24, 2015 02:00 PM EST Reads: 419
The cloud. Like a comic book superhero, there seems to be no problem it can’t fix or cost it can’t slash. Yet making the transition is not always easy and production environments are still largely on premise. Taking some practical and sensible steps to reduce risk can also help provide a basis for a successful cloud transition. A plethora of surveys from the likes of IDG and Gartner show that more than 70 percent of enterprises have deployed at least one or more cloud application or workload. Yet a closer inspection at the data reveals less than half of these cloud projects involve production...
Nov. 24, 2015 01:45 PM EST Reads: 412
Internet of @ThingsExpo, taking place June 7-9, 2016 at Javits Center, New York City and Nov 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with the 18th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world and ThingsExpo New York Call for Papers is now open.
Nov. 24, 2015 01:30 PM EST Reads: 488