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Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases
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Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use Cases

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Financial institutions today are under intense pressure to provide more value add to the customers, reduce IT costs and also grow year to year. This challenge has been further complicated by huge …

Financial institutions today are under intense pressure to provide more value add to the customers, reduce IT costs and also grow year to year. This challenge has been further complicated by huge amounts of data being generated as well as mandatory federal compliances in place.
Similarly, Manufacturing industry today also is facing the challenge to process huge amount of data in real time and predict failures as early as possible to reduce cost and increase production efficiency.
The session will cover some high level Big Data use cases applicable to financial and manufacturing domain and how big data technologies are being used successfully to solve these challenges using some examples in credit card/banking industry in financial domain and semi-conductor production in manufacturing domain.

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  • Fraud Detection:You may not be surprised that Banks and Credit cards are monitoring your spending habits on real-time basis. One of the large credit card issuing bank has implemented fraud detection system that would disable your card if they see suspicious activity based on your past history with spending patters and trends. In addition to the transaction records for authorization and approvals, banks and credit card companies are collecting lot more information from location, your life style, spending patterns. Credit card companies manage huge volume of data from individual Social Security number and income, account balances and employment details, and credit history and transaction history. All this put together helps credit card companies to fight fraud in real-time. Big Data architecture provides that scalability to analyze the incoming transaction against individual history and approve/decline the transaction and alert the account owner.Fraud detection and analysis -­‐ Hadoop provides a scalable method to more easily detect many types of fraud or loss prevention, and perform effective risk management. Hadoop is also being used to develop models that predict future fraud events.Credit Risk – Valuations for credit risk are very computationally intensive. The number of risk factors that need to be modeled are in the 1,000s, with each risk factor taking 1,000s of stochastic paths. This problem is traditionally solved using very large grids of compute nodes. Big Data technologies offer a significantly cheaper alternative, with a huge number of loosely coupled Hadoop nodes that these computations can be offloaded onto.Risk Mitigation -­‐ Hadoop is used to analyze potential market trends and understand future possibilities to mitigate the risk of financial positions, total portfolio assets, and capital returns.Customer insights -­‐ Large financial service providers have adopted Hadoop to improve customer profile analysis to help determine eligibility for equity capital, insurance, mortgage or credit.Customer Segmentation applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. In Banking & Financial industry, customer segmentation is a key tool in risk scoring analysis and for sales, promotion and marketing campaigns. In addition to existing information that banks and FIs collect from day to day transactions from customers, they are also buying external data like home values, merchant records from hotels, aviation, retailers, etc. The 360 degree view of customer is still a work in progress and Big Data is enabling filling in the gaps by providing the processing power needed to mine for intelligence from underlying data.The major objectives of segmentation are:Customized product offeringCustomized and priority serviceImprove relationship with profitable customers and cut resources spent on loss making customersBetter offering to new customers based on the intelligence gained from the existing customer segment they belong New product development and bundling as per the customer segment profileCustomer Sentiment AnalysisThe bank can now respond to negative (or positive) brand perception by focusing its communication strategies on particular Internet sites, countering – or backing up – the most outspoken authors on Twitter, boards and blogs. When a company releases a new product that’s causing problems, analyzing comments in social media sites or product review sites can enable it to quickly remediate.Next Best Offer – Banks can use predictive analytics on a combination of data to create a series of targeted offers for customers, and make these offers available in real time at the next point of customer interactionMicro targeting -­‐ Banks have numerous disparate data systems (e.g., loans, mortgages, investments) that need to be aggregated in order to provide an up-­‐to-­‐date view on customer profitability, consistent CRM, and customized product recommendations and offerings. Payments Analytics – Banks can offer value-added services to their merchant customers by analysing payments cleared by them and alerting them of opportunities if they see certain patterns emergeTrade Analysis -­‐ Financial service firms use Hadoop to analyze the daily streams of transaction data in conjunction with unstructured news or social media feeds, or to back-­‐test their trading algorithmsPricing Management – Banks, Capital Markets institutions and insurance firms can use information from both types of data source to price products for individual customers, taking into account risk, capital, cost allocations, transfer pricing and a multitude of additional dimensionsLong-­‐Term Storage & Analytics – Hadoop provides a drastic cost reduction for the long-­‐term storage and analytics of transaction data. New Opportunities – Many large firms are using Hadoop to identify cross-­‐ sell and upsell opportunities by cross referencing sentiment analysis with internal customer profile data.Reputational Risk – All institutions worry about their reputation and getting feedback on newly adopted policies and newly launched products. Most of the ’noise’ around a brand comes from new data sources, and Big Data technologies can be very effective at quickly gathering  this information for analysis.Sales and Marketing CampaignsOn the customer experience side, every time you get closer to delighting your customer by showing that you understand what their real needs are, without blindly sending them emails and credit card offers, it makes the customer view their institution as caring about them and understanding what their needs are.Fraud, AML, Trader and Broker Compliance – All categories of fraud suffer from over-zealous software that generates a high number of false positives. These result in a significant operational problem as they need to be analysed manually. Tuning the software to reduce the number of these alerts results in the opposite problem, with real fraud going unreported. Using both sources of seemingly unrelated data offers the potential to catch fraudulent activities earlier than current methods allow. In the case of internal fraud monitoring, trader and broker compliance software can monitor trading activity coupled with additional data points from sources such as social media, SMS and emails, and create a graph analysis that traditional tools are unable to provide, in order to detect any patterns.Web-­‐scale Analysis -­‐ Hadoop is used to analyze what is being said on the web at large and on social networks in particular. Sentiment can apply to individual companies or products or reflect general customer satisfaction. This has potential to improve marketing to existing customers through better targeting. CrowdsourcingSome of the larger institutions have realized they can use analytics to learn about new lines of business and products, to ask customers what they think, and to get ideas. In a move to expand its utility beyond simply finding better answers to known statistical problems, data-science startup Kaggle is now letting its stable of expert data scientists compete to tell companies how they can improve their businesses using machine learning.Call Center AnalysisFor decades, companies have been analyzing call center data for staffing, agent performance, network management. But with big data age, many new interesting software are being implemented today in attempt to take unstructured voice recordings and analyze them for content and sentiment. Banks are applying text and sentiment analysis to this unstructured data, and looking for patterns and trends. Many banks are integrating this call center data with their transactional data warehouse to reduce customer churn, and drive up-sell, cross-sell, customer monitoring alerts and fraud detection.These are just few of the use cases that I have highlighted here to give a fair idea about how Big Data is being leveraged in this industry. If you have use-case that you are working with, please add it to the comment section.
  • Transcript

    • 1. 1 Finance and Manufacturing Big Data Use Cases and Solutions Sanjay Sharma Principal Architect August 2013
    • 2. Impetus Big Data Services 2 Big Data Platform Implementation Operations and Visualization Business Analytics and Data Science Solution Architecture, POC and Production planning Technology strategy, Use Case development & Validation BUSINESS PROCESS MANAGEMENT Assessment Solution Modeling Solution Analysis People, Proc ess, Technol ogy Impact Analyze & Optimize Objectives & Strategy Model
    • 3. Big Data : Value Drivers © 2013 Impetus Technologies
    • 4. Financial World: Key Challenges © 2013 Impetus Technologies Business Challenges • Fraud • Regulatory Compliances • Customer Insights • Risk Handling Technical Challenges • Reduce IT costs • Do more with LESS • Unstructured data
    • 5. Financial World: Some Use Cases © 2013 Impetus Technologies Fraud Detection and Analysis Risk Management Customer Insights/Management Micro targeting Trade/Payments Analytics Long‐Term Storage & Analytics New Opportunities Reputational Risk Marketing Campaigns
    • 6. Financial World: Technical Solution Challenges © 2013 Impetus Technologies • Regulatory Compliances • Machine Learning based supervised and unsupervised analytics Large Data Storage and Advanced Analytics • Mash up structured and unstructured data • Enrich TX with NLP/Text analytics Unstructured Data • Customer level Profiling/ Recommendations • Transaction/Trade Ticker level analytics Individual Level Analytical Processing
    • 7. Big Data Solution: Building Blocks © 2013 Impetus Technologies
    • 8. Big Data Use Case Solution: Example © 2013 Impetus Technologies • Major Credit Card Company • Traditionally Oracle/ DB2 + SAS + Informatica • Huge data – Existing solutions/ infrastructure inadequate to meet new business requirements and data growth cost effectively – Successfully revamping to Big Data Platform
    • 9. Big Data Use Case Solution: Example © 2013 Impetus Technologies Step 1: Hadoop POC with Sentiment Analysis Step 2: Hadoop Distribution Selection Step 3: Production-ized Recommendation Engine Step 4: Production-ized Negative Fraud Detection ($100+mlln) Step 5: … Hive Hadoop Platform HBas e Oozie Mahout + NLP Transactions Unstructured Sources (Email, Surveys, CRM notes, Social Feeds etc.) latform Data a a er CEP ata alytics g on Data Visualization, Exploration and Discovery Tools Tools Reporting Tools Analytical Tools Business User Business Analyst Advanced Analytics User Sqoop Flume MapRed Unified Analytics Platform (UAP) Unstructured Data Internal Data External Data Twitter Facebook Voice to Text Call Center Notes CEP WEP Structured Data In-Database Analytics Predictive Data Mining Segmentation Data Visualization, Exploration and Discovery Tools S A N D B O X Domain Specific Tools Reporting Tools Analytical Tools Applications Business User Business Analyst Advanced Analytics User BI/ Visualization Analytics- SAS/R Real Time Delivery RDBMS/API RDBMS/MPP Informatica ETL/ELT L Advantages = PB scale = ETL and Analytical Offloading = Prescriptive Analytics than Predictive
    • 10. Manufacturing Domain: Challenges © 2013 Impetus Technologies Business Challenges • Early preventive maintenance/repair • Real time/ near real time actionable response • Improve productivity/Margin Recovery • Reduce wastes, improve efficiency • Improve Yield • Supply Chain • Optimize supply chain Technical Challenges • High Ingestion Rates • Sensor/ tool data with sub second ingestion requirements • Millions of read/writes per second • Complex log formats • Semi-structured data • Huge amount of data • TB/PB of data storage for deeper analytics
    • 11. Manufacturing Domain: Architectural Building Blocks © 2013 Impetus Technologies NoSQL + Search Machine Data
    • 12. © 2013 Impetus Technologies Machine Data Ingestion Engine (Real time + Batch components) Real Time Processing Engine (CEP/Analytics/ Rule Engine) Real Time Data Storage Engine (Store + Indexing/Searc h) Business Process Engine (Business Process+ Rule management) Kafka/ Storm Storm + Esper Cassandra + Solr JBoss Drools/jBPM Manufacturing Domain: Data Ingestion/ Streaming – Customer Example
    • 13. © 2013 Impetus Technologies NoSQL + Search Machine Data Manufacturing Domain: Reference Architecture-Cloud– Customer Example
    • 14. • Software Solutions and Services Company • Leader in Innovation led Technology services • 17 years of customer success, 1500 people across US/ India • Big Data, Enterprise Mobility, Test and Performance Engineering, Carrier Grade Large Systems • Vendor neutral, open source contributor with Big data accelerator products © 2013 Impetus Technologies Impetus Technologies
    • 15. Q&A ssharma@impetus.com bigdata@impetus.com http://bigdata.impetus.com © 2013 Impetus Technologies

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