Welcome!

Microservices Journal Authors: Liz McMillan, Carmen Gonzalez, Elizabeth White, Pat Romanski, Kathy Thomas

Related Topics: Cloud Expo, Java, Microservices Journal, Linux, Virtualization, Security

Cloud Expo: Article

Real-Time Fraud Detection in the Cloud

Using machine learning agent ensembles

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:

  • ActiveMQ
  • Web services
  • PostgreSQL

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.

Agent Paradigm
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.

Logistic Regression
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

Conclusion
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.

References

  • 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

More Stories By Roger Barga

Roger Barga, PhD, is Group Program Manager for the CloudML team at Microsoft Corporation where his team is building machine learning as a service on the cloud. He is also a lecturer in the Data Science program at the University of Washington. Roger joined Microsoft in 1997 as a Researcher in the Database Group of Microsoft Research (MSR), where he was involved in a number of systems research projects and product incubation efforts, before joining the Cloud and Enterprise Division of Microsoft in 2011.

More Stories By Avinash Joshi

Avinash Joshi is a Senior Research Analyst in the Innovation and Development group of Mu Sigma Business Solutions. He is currently part of a team that works on generating insights from real-time data streams in financial markets. Avinash joined this team in 2011 and has interests ranging from marketing mix modeling to algorithmic trading.

More Stories By Pravin Venugopal

Pravin Venugopal is a Senior Research Analyst in the Innovation and Development group of Mu Sigma Business Solutions. He is currently part of a team that is developing a low latency platform for algorithmic trading. Pravin received his Masters degree in Computer Science and has been a part of Mu Sigma since 2012. His interests include analyzing real-time financial data streams and algorithmic trading.

Comments (1)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@MicroservicesExpo Stories
BlueBox bridge the chasm between development and infrastructure. Hosting providers are taking standardization and automation too far. For many app developers it does nothing but spawn mayhem and more work. They have to figure out how their creations live on a pre-fab infrastructure solution full of constraints. Operations-as-a-Service is what BlueBox does. BlueBox utilizes development tools such as OpenStack, EMC Razor, Opscode’s Chef and BlueBox's proprietary tools give the power to do the unor...
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With "smart" appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user's habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps,...
NuoDB just introduced the Swifts 2.1 Release. In this demo at 15th Cloud Expo, Seth Proctor, CTO of NuoDB, Inc., discussed why scaling databases in the cloud is challenging, why building your application on top of the infrastructure that is designed with this in mind makes a difference, and what you can do with NuoDB that simplifies your programming model, your operations model.
The 17th International Cloud Expo has announced that its Call for Papers is open. 17th International Cloud Expo, to be held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA, brings together Cloud Computing, APM, APIs, Microservices, Security, Big Data, Internet of Things, DevOps and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding bu...
“We are a managed services company. We have taken the key aspects of the cloud and the purposed data center and merged the two together and launched the Purposed Cloud about 18–24 months ago," explained Chetan Patwardhan, CEO of Stratogent, in this SYS-CON.tv interview at 15th Cloud Expo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
"Blue Box has been around for 10-11 years, and last year we launched Blue Box Cloud. We like the term 'Private Cloud as a Service' because we think that embodies what we are launching as a product - it's a managed hosted private cloud," explained Giles Frith, Vice President of Customer Operations at Blue Box, in this SYS-CON.tv interview at DevOps Summit, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Cloud Expo New York is happening from June 9 - 11. This event brings together the worlds of Cloud Computing, DevOps, IoT, WebRTC, Big Data and SDDC. We hope to see you there-members of the Blue Box team will exhibit in booth 218 next to the DevOps area. Plus, our Chief Product Officer, Hernan Alvarez, will present his talk "The Cloud Has a Down-and-Dirty Lining" as part of the Operations track in the DevOps Summit portion of the event on June 9 at 11 am. Learn more about his session her...
It's a "given" in software development - release schedules always slip. Requirements shift, developers underestimate timelines, and quality assurance (QA) finds unexpected defects. Another law of software development is that final release dates are often inflexible. If a market or a holiday shopping season defines your release date you understand how important it is to meet a fixed timeline. Once you've promised a release to the business at the end of the quarter, you are under pressure to de...
Here are a few questions to help you assess the scope of your release management challenges. Based on the answers to these questions, you can calculate your Release Management risk factor. This will help you understand what steps you need to take today to mitigate release management risks that accompany software development at scale. These 100 people don't have to be in a single group and the systems maintained don't have to be limited to a single group. Also, note that the systems supported...
DevOps approaches within “Unicorns” vary significantly from the reality of DevOps in the enterprise. Most enterprises manage portfolios of heterogeneous applications that are increasingly interconnected, delivered by global teams, at various stages of technology maturity, and are often encumbered by additional compliance and governance obligations. In his session at DevOps Summit, Dalibor Siroky, Director and co-founder at Plutora, will discuss the emerging and evolving experiences of Agile, Co...
With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo in Silicon Valley. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be! Internet of @ThingsExpo, taking place Nov 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 17th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading in...
Lacking the traditional fanfare associated with any technology that can use the word "container" or mention "Docker" in its press release, Ubuntu Core and its new Snappy system management scheme was introduced late last year. Since then, it's been gaining steam with Microsoft and Amazon and Google announcing support for the stripped-down version of the operating system. Ubuntu Core is what's being called a "micro-OS"; a stripped down, lean container-supporting machine that's becoming more pop...
17th Cloud Expo, taking place Nov 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises a...
Cloud computing seems destined to be the way enterprises will use information technology. The drastic cost reductions and impressive operational improvements make the transition an unstoppable trend. The “What is cloud computing?” question now, however, seems to be morphing into “Where is cloud computing going?” While software-as-a-service (SaaS) providers see their market rocketing upward as the easiest and quickest path for cloud adoption, infrastructure-as-a-service providers are suffering...
SYS-CON Events announced today Isomorphic Software, the global leader in high-end, web-based business applications, will exhibit at SYS-CON's DevOps Summit 2015 New York, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Isomorphic Software is the global leader in high-end, web-based business applications. We develop, market, and support the SmartClient & Smart GWT HTML5/Ajax platform, combining the productivity and performance of traditional desktop software ...
SYS-CON Events announced today that B2Cloud, a provider of enterprise resource planning software, will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. B2cloud develops the software you need. They have the ideal tools to help you work with your clients. B2Cloud’s main solutions include AGIS – ERP, CLOHC, AGIS – Invoice, and IZUM
Containers and microservices have become topics of intense interest throughout the cloud developer and enterprise IT communities. Accordingly, attendees at the upcoming 16th Cloud Expo at the Javits Center in New York June 9-11 will find fresh new content in a new track called PaaS | Containers & Microservices Containers are not being considered for the first time by the cloud community, but a current era of re-consideration has pushed them to the top of the cloud agenda. With the launch ...
The world's leading Cloud event, Cloud Expo has launched Microservices Journal on the SYS-CON.com portal, featuring over 19,000 original articles, news stories, features, and blog entries. DevOps Journal is focused on this critical enterprise IT topic in the world of cloud computing. Microservices Journal 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. Follow new article posts on T...
SYS-CON Events announced today that MangoApps will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY., and the 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. MangoApps provides private all-in-one social intranets allowing workers to securely collaborate from anywhere in the world and from any device. Social, mobile, and eas...
There is no doubt that Big Data is here and getting bigger every day. Building a Big Data infrastructure today is no easy task. There are an enormous number of choices for database engines and technologies. To make things even more challenging, requirements are getting more sophisticated, and the standard paradigm of supporting historical analytics queries is often just one facet of what is needed. As Big Data growth continues, organizations are demanding real-time access to data, allowing immed...