Large Scale Modelling and Analytics Challenges at a Payments Company

Jul 2014

21 Mon

22 Tue

23 Wed 09:30 AM – 05:00 PM IST

24 Thu 09:45 AM – 05:00 PM IST

25 Fri 08:30 AM – 07:15 PM IST

26 Sat 08:30 AM – 07:15 PM IST

27 Sun

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NIMHANS Convention Centre, Bangalore

In 2014, infrastructure components such as Hadoop, Berkeley Data Stack and other commercial tools have stabilized and are thriving. The challenges have moved higher up the stack from data collection and storage to data analysis and its presentation to users. The focus for this year’s conference on analytics – the infrastructure that powers analytics and how analytics is done.

Talks will cover various forms of analytics including real-time and opportunity analytics, and technologies and models used for analyzing data.

Proposals will be reviewed using 5 criteria:
Domain diversity – proposals will be selected from different domains – medical, insurance, banking, online transactions, retail. If there is more than one proposal from a domain, the one which meets the editorial criteria will be chosen.
Novelty – what has been done beyond the obvious.
Insights – what insights does the proposal share with the audience that they did not know earlier.
Practical versus theoretical – we are looking for applied knowledge. If the proposal covers material that can be looked up online, it will not be considered.
Conceptual versus tools-centric – tell us why, not how. Tell the audience what was the philosophy underlying your use of an application, not how an application was used.
Presentation skills – proposer’s presentation skills will be reviewed carefully and assistance provided to ensure that the material is communicated in the most precise and effective manner to the audience.

Tickets: http://fifthel.doattend.com

Website: https://fifthelephant.in/2014

For queries about proposals / submissions, write to info@hasgeek.com

Theme

Data Collection and Transport – for e.g, Opendatatoolkit, Scribe, Kafka, RabbitMQ, etc.
Data Storage, Caching and Management – Distributed storage (such as Gluster, HDFS) or hardware-specific (such as SSD or memory) or databases (Postgresql, MySQL, Infobright) or caching/storage (Memcache, Cassandra, Redis, etc).
Data Processing, Querying and Analysis – Oozie, Azkaban, scikit-learn, Mahout, Impala, Hive, Tez, etc.
Real-time analytics
Opportunity analytics
Big data and security
Big data and internet of things
Data Usage and BI (Business Intelligence) in different sectors.

Please note: the technology stacks mentioned above indicate latest technologies that will be of interest to the community. Talks should not be on the technologies per se, but how these have been used and implemented in various sectors, enterprises and contexts.

Hosted by

The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

All submissions

This submission has been added to the schedule

Large Scale Modelling and Analytics Challenges at a Payments Company

Submitted Jul 4, 2014

Section: Full talk Technical level: Intermediate

This talk first presents a broad overview of the Big Data challenges in a payments company. Then it discusses in details an application around modelling spend behavior of credit card holders. Through the application the talk demonstrates how various machine learning and data mining techniques are utilized to glean insights from petabyte scale data, and how one build practical models to solve real world problems.

Outline

At American Express, we serve tens of millions of credit card members who transact at several million merchants globally. Apart from this transaction level data, there is petabyte scale data generated from card members’ other interactions with American Express:- through visiting and interacting with our website, phone and chat interactions with customer care representatives, the surveys we conduct to gauge the pulse of our customers, etc. All these result in large scale data coming from multiple modalities. In this talk, we will focus on some key challenges which arise from dealing with this data. The talk will first give a broad overview of the various challenges in the context of machine learning in the payments industry and then focus in some details on a particular problem of modelling purchase intent of credit card holders.

Requirements

Though this talk is pitched at a broad overview level, it may be helpful to have some basic familiarity with popular machine learning and data mining techniques.