Welcome!

Microservices Expo Authors: Elizabeth White, Pat Romanski, Jason Bloomberg, Kong Yang, Mark Leake

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

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
Managing mission-critical SAP systems and landscapes has never been easy. Add public cloud with its myriad of powerful cloud native services and this may not change any time soon. Public cloud offers exciting new possibilities for enterprise workloads. But to make use of these possibilities and capabilities, IT teams need to re-think everything they have done before. Otherwise, they will just end up using public cloud as a hosting platform for their workloads, aka known as “lift and shift.”
You know you need the cloud, but you’re hesitant to simply dump everything at Amazon since you know that not all workloads are suitable for cloud. You know that you want the kind of ease of use and scalability that you get with public cloud, but your applications are architected in a way that makes the public cloud a non-starter. You’re looking at private cloud solutions based on hyperconverged infrastructure, but you’re concerned with the limits inherent in those technologies.
"Tintri focuses on the Ops side of the DevOps, which basically is pushing more and more of the accessibility of the infrastructure to the developers and trying to get behind the scenes," explained Dhiraj Sehgal of Tintri in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Both SaaS vendors and SaaS buyers are going “all-in” to hyperscale IaaS platforms such as AWS, which is disrupting the SaaS value proposition. Why should the enterprise SaaS consumer pay for the SaaS service if their data is resident in adjacent AWS S3 buckets? If both SaaS sellers and buyers are using the same cloud tools, automation and pay-per-transaction model offered by IaaS platforms, then why not host the “shrink-wrapped” software in the customers’ cloud? Further, serverless computing, cl...
In the decade following his article, cloud computing further cemented Carr’s perspective. Compute, storage, and network resources have become simple utilities, available at the proverbial turn of the faucet. The value they provide is immense, but the cloud playing field is amazingly level. Carr’s quote above presaged the cloud to a T. Today, however, we’re in the digital era. Mark Andreesen’s ‘software is eating the world’ prognostication is coming to pass, as enterprises realize they must be...
Hybrid IT is today’s reality, and while its implementation may seem daunting at times, more and more organizations are migrating to the cloud. In fact, according to SolarWinds 2017 IT Trends Index: Portrait of a Hybrid IT Organization 95 percent of organizations have migrated crucial applications to the cloud in the past year. As such, it’s in every IT professional’s best interest to know what to expect.
A common misconception about the cloud is that one size fits all. Companies expecting to run all of their operations using one cloud solution or service must realize that doing so is akin to forcing the totality of their business functionality into a straightjacket. Unlocking the full potential of the cloud means embracing the multi-cloud future where businesses use their own cloud, and/or clouds from different vendors, to support separate functions or product groups. There is no single cloud so...
In 2014, Amazon announced a new form of compute called Lambda. We didn't know it at the time, but this represented a fundamental shift in what we expect from cloud computing. Now, all of the major cloud computing vendors want to take part in this disruptive technology. In his session at 20th Cloud Expo, Doug Vanderweide, an instructor at Linux Academy, discussed why major players like AWS, Microsoft Azure, IBM Bluemix, and Google Cloud Platform are all trying to sidestep VMs and containers wit...
Companies have always been concerned that traditional enterprise software is slow and complex to install, often disrupting critical and time-sensitive operations during roll-out. With the growing need to integrate new digital technologies into the enterprise to transform business processes, this concern has become even more pressing. A 2016 Panorama Consulting Solutions study revealed that enterprise resource planning (ERP) projects took an average of 21 months to install, with 57 percent of th...
The taxi industry never saw Uber coming. Startups are a threat to incumbents like never before, and a major enabler for startups is that they are instantly “cloud ready.” If innovation moves at the pace of IT, then your company is in trouble. Why? Because your data center will not keep up with frenetic pace AWS, Microsoft and Google are rolling out new capabilities. In his session at 20th Cloud Expo, Don Browning, VP of Cloud Architecture at Turner, posited that disruption is inevitable for comp...
New competitors, disruptive technologies, and growing expectations are pushing every business to both adopt and deliver new digital services. This ‘Digital Transformation’ demands rapid delivery and continuous iteration of new competitive services via multiple channels, which in turn demands new service delivery techniques – including DevOps. In this power panel at @DevOpsSummit 20th Cloud Expo, moderated by DevOps Conference Co-Chair Andi Mann, panelists examined how DevOps helps to meet the de...
"When we talk about cloud without compromise what we're talking about is that when people think about 'I need the flexibility of the cloud' - it's the ability to create applications and run them in a cloud environment that's far more flexible,” explained Matthew Finnie, CTO of Interoute, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"We are a monitoring company. We work with Salesforce, BBC, and quite a few other big logos. We basically provide monitoring for them, structure for their cloud services and we fit into the DevOps world" explained David Gildeh, Co-founder and CEO of Outlyer, in this SYS-CON.tv interview at DevOps Summit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Colocation is a central pillar of modern enterprise infrastructure planning because it provides greater control, insight, and performance than managed platforms. In spite of the inexorable rise of the cloud, most businesses with extensive IT hardware requirements choose to host their infrastructure in colocation data centers. According to a recent IDC survey, more than half of the businesses questioned use colocation services, and the number is even higher among established businesses and busine...
For most organizations, the move to hybrid cloud is now a question of when, not if. Fully 82% of enterprises plan to have a hybrid cloud strategy this year, according to Infoholic Research. The worldwide hybrid cloud computing market is expected to grow about 34% annually over the next five years, reaching $241.13 billion by 2022. Companies are embracing hybrid cloud because of the many advantages it offers compared to relying on a single provider for all of their cloud needs. Hybrid offers bala...
@DevOpsSummit at Cloud Expo taking place Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center, Santa Clara, CA, is co-located with the 21st International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is ...
The reality of data ubiquity is here—data is buried in operational statistics, machine logs, stacks of overflowing tickets and customer details, among other things. How can any user get valuable information amid this rapid influx of data? Imagine a situation where your firm’s revenue takes a hit owing to an unexpected failure in some business process. It would be a nightmare for IT admins to sift through the interminable piles of data to deduce exactly why and where the problem occurred. To sav...
For organizations that have amassed large sums of software complexity, taking a microservices approach is the first step toward DevOps and continuous improvement / development. Integrating system-level analysis with microservices makes it easier to change and add functionality to applications at any time without the increase of risk. Before you start big transformation projects or a cloud migration, make sure these changes won’t take down your entire organization.
What's the role of an IT self-service portal when you get to continuous delivery and Infrastructure as Code? This general session showed how to create the continuous delivery culture and eight accelerators for leading the change. Don Demcsak is a DevOps and Cloud Native Modernization Principal for Dell EMC based out of New Jersey. He is a former, long time, Microsoft Most Valuable Professional, specializing in building and architecting Application Delivery Pipelines for hybrid legacy, and cloud ...
21st International Cloud Expo, taking place October 31 - November 2, 2017, 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. Me...