@CloudExpo Authors: Pat Romanski, William Schmarzo, Liz McMillan, Yeshim Deniz, Elizabeth White

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Linux Containers, Containers Expo Blog, Cloud Security

@CloudExpo: 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

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
  • 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.

@CloudExpo Stories
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Evan Kirstel is an internationally recognized thought leader and social media influencer in IoT (#1 in 2017), Cloud, Data Security (2016), Health Tech (#9 in 2017), Digital Health (#6 in 2016), B2B Marketing (#5 in 2015), AI, Smart Home, Digital (2017), IIoT (#1 in 2017) and Telecom/Wireless/5G. His connections are a "Who's Who" in these technologies, He is in the top 10 most mentioned/re-tweeted by CMOs and CIOs (2016) and have been recently named 5th most influential B2B marketeer in the US. H...
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of bus...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
As you move to the cloud, your network should be efficient, secure, and easy to manage. An enterprise adopting a hybrid or public cloud needs systems and tools that provide: Agility: ability to deliver applications and services faster, even in complex hybrid environments Easier manageability: enable reliable connectivity with complete oversight as the data center network evolves Greater efficiency: eliminate wasted effort while reducing errors and optimize asset utilization Security: implemen...
DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
DXWorldEXPO | CloudEXPO are the world's most influential, independent events where Cloud Computing was coined and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals. Sponsors of DXWorldEXPO | CloudEXPO benefit from unmatched branding, profile building and lead generation opportunities.
@DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. From showcase success stories from early adopters and web-scale businesses, DevOps is expanding to organizations of all sizes, including the world's largest enterprises - and delivering real results.
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
DXWorldEXPO LLC announced today that Dez Blanchfield joined the faculty of CloudEXPO's "10-Year Anniversary Event" which will take place on November 11-13, 2018 in New York City. Dez is a strategic leader in business and digital transformation with 25 years of experience in the IT and telecommunications industries developing strategies and implementing business initiatives. He has a breadth of expertise spanning technologies such as cloud computing, big data and analytics, cognitive computing, m...
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
DXWorldEXPO LLC announced today that Kevin Jackson joined the faculty of CloudEXPO's "10-Year Anniversary Event" which will take place on November 11-13, 2018 in New York City. Kevin L. Jackson is a globally recognized cloud computing expert and Founder/Author of the award winning "Cloud Musings" blog. Mr. Jackson has also been recognized as a "Top 100 Cybersecurity Influencer and Brand" by Onalytica (2015), a Huffington Post "Top 100 Cloud Computing Experts on Twitter" (2013) and a "Top 50 C...
There is a huge demand for responsive, real-time mobile and web experiences, but current architectural patterns do not easily accommodate applications that respond to events in real time. Common solutions using message queues or HTTP long-polling quickly lead to resiliency, scalability and development velocity challenges. In his session at 21st Cloud Expo, Ryland Degnan, a Senior Software Engineer on the Netflix Edge Platform team, will discuss how by leveraging a reactive stream-based protocol,...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
Daniel Jones is CTO of EngineerBetter, helping enterprises deliver value faster. Previously he was an IT consultant, indie video games developer, head of web development in the finance sector, and an award-winning martial artist. Continuous Delivery makes it possible to exploit findings of cognitive psychology and neuroscience to increase the productivity and happiness of our teams.
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.