Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
T&E Fraud, Misuse & Waste are things every travel manager fears. In the U.S. 250,000 employees cost business $3B from 48 million transactions, and 75.6% of employees committing expense report fraud are engaged in another form of occupational fraud. Learn about the troubling T’s of managers and find out how to detect the bad apples to make a positive impact on your T&E spend program.
Microsoft Introduction to Automated Machine LearningSetu Chokshi
A gentle introduction to Microsoft's AutoML SDK package. This presentation introduces the concept of why automated machine learning has an important place in any data scientists tool box. Auto ML SDK allows you to to build and run machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks or your favourite Python IDE.
The demos included in the presentation are making use of the Azure Notebooks.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
T&E Fraud, Misuse & Waste are things every travel manager fears. In the U.S. 250,000 employees cost business $3B from 48 million transactions, and 75.6% of employees committing expense report fraud are engaged in another form of occupational fraud. Learn about the troubling T’s of managers and find out how to detect the bad apples to make a positive impact on your T&E spend program.
Microsoft Introduction to Automated Machine LearningSetu Chokshi
A gentle introduction to Microsoft's AutoML SDK package. This presentation introduces the concept of why automated machine learning has an important place in any data scientists tool box. Auto ML SDK allows you to to build and run machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks or your favourite Python IDE.
The demos included in the presentation are making use of the Azure Notebooks.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
Machine learning on big data for personalized Internet advertisingTrieu Nguyen
Michael Recce discusses how advertising works and what algorithms Quantcast uses to analyze large amounts of data in order to find out what people are interested in.
http://www.infoq.com/presentations/Machine-Learning-on-Big-Data-for-Personalized-Internet-Advertising
Digital Banking / Digital Only Banks is the concept which is recent hot topic in Banking and Fintech. And in advancement and spread of internet and mobile technologies, it's the fantastic concept to remove to need to go to bank physically. However the challenges is roll out of Digital Banks and reaching out to right customer base and keep on growing. Without growth, Digital Banks are dead.
I'm attaching a short presentation on this topic depicting how power of analytics can be leveraged along with marketing tools and techniques to run campaign for digital only banks for lead generation and continuously improving on the campaign.
Telecommunication frauds involves the exploitation and misuse of airtime by fraudsters who have wrong intention of not paying any bills. Different types of telecommunication frauds are discussed in the slide
Operations Management Suite, the Penguins and the othersChristian Heitkamp
With the addition of the OMS Linux agent, OMS took a great leap forward by providing more functionalities than ever before. In this session, we will take a closer look at the Linux Agent and providers like the unified log data collector + others. If you have heard of Zabbix, Nagios, Icinga, you want to attend this session. We will do a live hands-on demo and integrate other Operations Management systems with OMS, elevating OMS to a real Operations Bridge with full analytics possibilities across IT management domains. To close off the session, we will spend some time on OMS and IOT too.
Christian Heitkamp (Germany)
Level 300
The battle to be your virtualization vendor is in full swing, and it
has important ramifications for the vendors involved, and for your
data center. The goal of this whitepaper is to analyze the
technical aspects of the two major choices: VMware vSphere 4
and Microsoft Hyper-V R2 (as part of Windows Server 2008 R2).
The two contenders are described in technical detail, and then
those details are compared head-to-head. Typical pricing in two
scenarios is included. Analysis of these tools, how they will
impact your datacenter virtualization, and what the future likely
holds is included. »
A practical guidance of the enterprise machine learning Jesus Rodriguez
This session provides an analysis of the machine learning market in the enterprise. The analysis includes vendors, platforms and best practices that should be considered by companies implementing data science solutions at an enterprise scale
10x Content: What it is and Why it MattersAdam Monago
December 2015 Webinar with @TrackMaven on 10x #Content #Marketing. Features an explanation of the 10x phenomenon, criteria for evaluating your content and helpful tips for building 10x content yourself!
Hank Roark of H2O gives an overview on data science, machine learning, and H2O.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Apache Spark™ Applications the Easy Way - Pierre Borckmanssparktc
At the sold-out Spark & Machine Learning Meetup in Brussels on October 27, 2016, Pierre Borckmans of Real Impact Analytics delivered a lightning talk called "Writing Spark applications, the easy way".
As Pierre explained, even though Apache Spark™ offers intuitive and high-level APIs, writing production-ready Spark data pipelines involves non-trivial challenges for data scientists without expert background in software development and devops matters. In this short talk, he showed how his team tackled these issues at Real Impact Analytics, by developing an intuitive framework for writing dataflows, offering convenient data exploration and testing facilities, while hiding devops-related complexity.
SmartData Webinar: Applying Neocortical Research to Streaming AnalyticsDATAVERSITY
We are witnessing an explosion of sensors and machine generated data. Every server, every building, and every device generates a continuous stream of information that is ever changing and potentially valuable. The existing big data paradigm requires storing data for batch analysis, and extensive modeling by a human expert, prior to deployment. This is incredibly inefficient and cannot scale.
In this webinar, Ahmad will describe a new paradigm for streaming data algorithms, based on recent neuroscience findings and on the computational properties of the neocortex. These systems are highly automated, adapt to changing statistics, and naturally deal with temporal data streams. Many of the core ideas have been implemented in the open source project NuPIC, and validated in commercial anomaly detection and predictive maintenance applications. Given the massive increase in the number of data sources, a general-purpose automated approach is the only scalable way to effectively analyze and act on continuously streaming information.
Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Its a process of identifying fraudulent transaction.
This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases
AI & ML in Cyber Security - Why Algorithms Are DangerousRaffael Marty
Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
Machine Learning open studio solution for data scientists & developersActiveeon
Machine Learning Open Studio (ML-OS) is an interactive graphical interface that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. It provides a rich set of generic machine learning tasks that can be connected together to build either basic or complex machine learning workflows for various use cases such as: fraud detection, text analysis, online offer recommendations, prediction of equipment failures, facial expression analysis, etc. These tasks are open source and can be easily customized according to your needs. ML-OS can schedule and orchestrate executions while optimising the use of computational resources. Usage of resources (e.g. CPU, GPU, local, remote nodes) can be easily monitored.
Drilling systems automation is the real-time reliance on digital technology in creating a wellbore. It encompasses downhole tools and systems, surface drilling equipment, remote monitoring and the use of models and simulations while drilling. While its scope is large, its potential benefits are impressive, among them: fewer workers exposed to rig-floor hazards, the ability to realize repeatable performance drilling, and lower drilling risk. While drilling systems automation includes new drilling technology, it is most importantly a collaborative infrastructure for performance drilling. In 2008, a small group of engineers and scientists attending an SPE conference noted that automation was becoming a key topic in drilling and they formed a technical section to investigate it further. By 2015, the group reached a membership of sixteen hundred as the technology rapidly gaining acceptance. Why so much interest? The benefits and promises of an automated approach to drilling address the safety and fundamental economics of drilling. What will it take? Among the answers are an open collaborative digital environment at the wellsite, an openness of mind to digital technologies, and modified or new business practices. What are the barriers? The primary barrier is a lack of understanding and a fear of automation. When will it happen? It is happening now. Digital technologies are transforming the infrastructure of the drilling industry. Drilling systems automation uses this infrastructure to deliver safety and performance, and address cost.
Rise of the machines -- Owasp israel -- June 2014 meetupShlomo Yona
Rise of the machines -- Owasp israel -- June 2014 meetup
Shlomo Yona presents why it is a good idea to use Machine Learning in Security and explains some Machine Learning jargon and demonstraits with two fingerprinting examples: a wifi device (PHY) and a browser (L7)
Machine learning, or predictive analytics have started entering into our daily life. Businesses and enterprises could use predictive analytics to improve efficiency, improve user experience, as well as to create new business opportunities. This talk will present WSO2 Machine Learner, our experiences of predicting Super Bowl winners, and few real life use cases. Furthermore, talk will discuss open challenges and problems people are working on.
Von der Zustandsüberwachung zur vorausschauenden WartungPeter Schleinitz
Talk at Sensorik-Stammtisch of thew Mittelstand 4.0-Kompetenzzentrum Ilmenau, http://www.kompetenzzentrum-ilmenau.digital/news/item/157-predictive-analytics-thema-beim-sensorik-stammtisch #ibmaot
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
IT Operation Analytic for security- MiSSconf(sp1)stelligence
IT Operation Analytic: Using Anomaly Detection , Unsupervised Machine Learning, to distinct normal and abnormal behavior and enhance efficiency of SIEM detection and alert capability.
Similar to Fraud Analytics with Machine Learning and Big Data Engineering for Telecom (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. A Quick Intro – Telecom Frauds
Fraud Analytics With Machine Learning &
Engineering
2
• Have you got missed call from unknown numbers from
overseas?
• Have you heard of PBX hacking and corporate facing huge
bills?
3. Problem Definition
• Telecom industries loose 46.3 billion USD
globally due to various frauds
• 10% operators have bad debt due to fraud
• Detection is cat and mouse game – pattern
changes to get undetected by available
data mining techniques
• Timely alert by processing huge volume of
call records is a challenge
• Alerts with high false positives have more
operational expenses
Fraud Analytics With Machine Learning &
Engineering
3
4. Importance to Telecom Industry & Society
• Efficient and self adaptive detection
mechanism can reduce significant loss
(about 2.1% of the revenue) due to fraud
and operational cost
• Less “Bad Money” to the system
Fraud Analytics With Machine Learning & Engineering 4
5. Data Source
• More than 1 TB of Call Detail Record
(CDR) from a reputed wholesale carrier
as history data
• Tested on few weeks of live CDR of the
carrier
Fraud Analytics With Machine Learning & Engineering 5
6. Analytics Technique
• Basic components of FAME are:
– Self adaptive Machine learning
methodology
– Actionable dash board for operations and
investigations team to act upon the alerts
and feedback sent to machine learning
model for adjusting weights.
– High performance big data platform for
data processing and machine learning
Fraud Analytics With Machine Learning & Engineering 6
7. How it detects and adapts …
7Fraud Analytics With Machine Learning & Engineering
Fraud Detection Model
Pipeline
Novelty Detection
Pipeline / Stacking
Actionable Dashboards
Pattern validation and
tuning work bench
CDR Feed
1
2 4
Remaining
Data
Frauds detected
3
5
6
7 New Patterns
More frauds
8
New model addition / Tuning of existing9
10
Operators
feedback
Analyst
Operator
8. Novelty Detection Pipeline
8Fraud Analytics With Machine Learning & Engineering
• Novelty detection of origin and destination
numbers separately
• Various Contextual Anomaly Detection used and
outputs are combined
• Below are some examples of algorithms used
• Box-plot based outlier
• Clustering to find out cluster with distinct
centroid
• Use of Mahalonbis Distance –
Mdist > ɸ. IQR
10. Fraud Detection Pipeline
10
• Use history data and flag records based on
“Novelty Detection Pipeline”
• Verify those records and mark them
• Build separate models (logistic regression,
random forest models and threshold based)
for different patterns
• Combine outputs of the models
Fraud Analytics With Machine Learning & Engineering
11. ACTIONABLE DASHBOARD
System Behind Magic …
11Fraud Analytics With Machine Learning & Engineering
ENSEMBLE OF SELF ADAPTIVE ALGOS
BIG DATA PLATFORM
POWERED BY HADOOP & SPARK
INTEGRATION
FACETS
FEEDBACK
CDR FEED
FROM TELECOM SYSTEM
13. Accuracy Results
13
0 0.2 0.4 0.6 0.8 1
True positive
False positive
Accuracy
B-Number A-Number
Fraud Analytics With Machine Learning & Engineering
• Individual accuracy for
origin and destination
numbers detection
• Combined mechanism
has <5% false positive
14. What Next …
14
• Test for different types telecom frauds
• Extend this industrialized approach to other
areas (such as network intrusion detection)
• Productize as cloud based service as well as on
premise implementation
Fraud Analytics With Machine Learning & Engineering
15. Contact Us @
15Fraud Analytics With Machine Learning & Engineering
Amartya Kumar Das
amartya_das_2014@cba.isb.edu
https://in.linkedin.com/pub/amartya-
das/b/72b/637
Subhadip Paul
Subhadip_paul_2014@cba.isb.edu
https://in.linkedin.com/in/subhadippaul
Pranab Kumar Dash
Pranab_dash_2014@cba.isb.edu
www.linkedin.com/profile/view?id=19155
039
Sudarson Roy Pratihar
sudarson_pratihar_2014@cba.isb.edu
www.linkedin.com/in/sudarson
Follow us #FAMETELCO