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Hadoop in Financial Services for Risk Profiling, SLA Analytics, Security and Fraud Detection
- 1. Hadoop in Financial Services Adam Gugliciello, Solutions Engineer February, 2012
- 2. A Different Approach… What is the problem? History of the data... What is the pain in the market place. Real life example.. Walmart or GAP Cost pain point there is 5 main paintpoinst + more competition + to make more mney you need to analyze more money + what is the pain. THIS slide replace with old vs new slow expansive expertice one platform fast economic self service Data Warehouse Expensive ETL Slow Business Intelligence Expertise Hadoop Economical Raw Load Fast Drag and Drop, Spreadsheets Self Service
- 10. All talk, little action indication of interest, action (quote/buy/sell/fulfill), “ symbol ” , “ quantity ” , “ price ” Footer Header Financial Information eXchange (FIX)
- 11. Analyze Order Families Order Family Analysis sell cancel Time Instrument Vendor buy
- 12. A Second in the life…
- 13. Cut through the noise
Editor's Notes
- There are 3 main pain points + strong economic pressure ++ to make more money companies need to analyze more data ++ IT backlog, self service + large amount of Data + new kind of data + this applies also to verticals like financial services, telco, internet + ebay collects with each page view 300 data points + ebay from 5 to 20 to 40 PB this year. + The Gap use case
- Problem Description (1 of 3) -- Financial Institutions often hold large positions that they want to trade in ways that don ’ t affect public markets -- Dark pools are private venues that enable trading for this practice -- Dark Pool trading grants advantages but is still costly -- It ’ s a big data problem: large volumes, high velocity -- The lack of visibility is the true nature of the problem: visibility into who the players are and what they ’ re really offering
- -- Banks “ advertise ” intentions with FIX messages -- they can be structured and nested -- messages are “ offers ” not contracts
- -- group FIX messages into order families -- mine the order family data for insight -- look at the patterns and identify questionable behavior -- time to review the terms of use with a particular party? -- time to review the relationship with certain vendors?
- -- negotiation is often mechanical and automated -- doesn ’ t always result in a deal -- key is to find the patterns where volume is wasted -- heavy negotiating resulting in cancellations is very costly