Analytics Summit 2013
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Analytics Summit 2013

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Financial Services firms are using big data to build better intelligence about their employees and customers, this presentation describes "how" and "why" data scientists play an important role in that ...

Financial Services firms are using big data to build better intelligence about their employees and customers, this presentation describes "how" and "why" data scientists play an important role in that process.

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  • "Knowledge is a process of piling up facts; wisdom lies in their simplification." --Martin Fischer
  • A Key Risk Indicator, also known as a KRI, is a measure used in management to indicatehow: risky an activity is to detect an adverse impact or prevent the possibility of future adverse impact
  • So how serious of a problem has this become for the commercial sector? Let’s take a look at some recent cases involving top tier financial institutions.In September 2011, the Swiss bank UBS announced that it had lost over 2 billion dollars, as a result of unauthorized trading.In this case the trader was actually talking about the unauthorized trading in e-mail, specifically he was using the terms “umbrella” and “slush funds” in communication with colleagues.There was also a chat transcript in which a trade support analyst told Adoboli to cancel and rebook a trade to change the settlement date. The trade support analyst must have known it was a fake trade since If it were a genuine transaction, you couldn’t rebook and just move the settlement date. In April and May 2012 large trading losses occurred at JPMorgan's Chief Investment Office, based on transactions booked through its London branch. This is the famous case, nicknamed the London Whale, where a rogue trader had accumulated unauthorized outsized positions in the market.Again there were e-mails with damaging information, JPMorgan traders reportedly called regulators from the Office of the Comptroller of the Currency "stupid," and other traders fretted in an email that "we are going to crash.”At one point Iksilemailed a senior trader advising against increasing the bet, as the size of the trades were becoming “scary”. Advised take the “full pain” now. HSBC Holdings Plc agreed to pay a record $1.92 billion in fines to U.S. authorities for allowing itself to be used to launder a river of drug money flowing out of Mexico and other banking lapses.HSBC conceded that its anti-money laundering measures were inadequate and that it has taken big steps in beefing up its controls. In return for being spared prosecution, HSBC said it would continue to strengthen its compliance policies and procedures. a Senate investigation concluded that HSBC’s lax controls exposed it to money laundering and terrorist financing.  In December 2, 2001, Enron filed for Chapter 11 Bankruptcy, with $63.4 billion in assets it was the largest corporate bankruptcy in U.S. history. Enron’s downfall was a result of its complex financial statements and complex business model. "the primary motivations for Enron's accounting and financial transactions seem to have been to keep reported income and reported cash flow up, asset values inflated, and liabilities off the books.” A lot of these complex financial dealings were discussed to some extent in e-mail(BILL, YOU NEED TO SPEAK TO HOW THIS RELATES TO THIS AUDIENCE – THE GOVT. I SUGGEST YOU SPEAK TO THIS ON THIS SLIDE OR ADD ANOTHER ONE.)
  • Copyright Deloitte 2012.
  • Copyright Deloitte 2012.
  • Copyright Deloitte 2012.
  • Copyright Deloitte 2012.
  • Machines can help data scientists. Predict meaning of words and phrases based on context.

Analytics Summit 2013 Analytics Summit 2013 Presentation Transcript

  • A Data Cocktail Dirty, with a Compliance Twist
  • How to effectively leverage analytics in your organization? Stock your bar with data & tools. Get awesome bartenders. Demand great cocktails. Mix it up based on context.
  • 90% of the world’s data was generated over the last 2 years…
  • That changes everything: how we collect, store, manage, analyze and visualize data.
  • Data > Information > Knowledge > Wisdom Past | Present | Future
  • Big Data is so 2012  Cloud Social the path of the enterprise Mobile Big Data Algorithms next disruption? Dion Hinchcliffe. Dachis Group.
  • Algorithms • Machine learning – Predictions – Clustering • Statistical models working at scale – – – – Counting Comparing Ranking Filtering
  • Geeky is the new ‘sexy’ * *
  • Geeky? Yup … • • • • • • • • • • Java, R, Python SQL, RDBMS, DW, OLAP NoSQL, Hbase, Cassandra Hadoop, HDFS, MapReduce & Yarn Pig, Hive, Impala, Shark ETL, Webscrapers, Sqoop, Flume Knime, Weka, RapidMiner SPSS, SAS, OBIEE D3.js, Gephi, Tableau, Flare, Shiny Microsoft Excel 
  • Data scientists enable the creation of data products.
  • A data product is … • Curated and crafted from raw data • Meshed together from disparate sources, some with structured and some with unstructured data • A result of exploration and iterations • Answers known unknowns, or unknown unknowns • Triggers immediate business value • A probabilistic window of future events or behavior
  • Financial services is the world’s most heavily regulated industry.
  • Risk is uncertainty about a future outcome.
  • Key Risk Indicator (KRI) is a management measure used to detect an adverse impact or prevent the possibility of future adverse impact. Expressed as a data product.
  • Compliance risk is the current and prospective risk to earnings or capital arising from violations of, or nonconformance with, laws, rules, regulations, prescribed practices, internal policies, and procedures, or ethical standards. This risk exposes the institution to diminished reputation, fines, civil money penalties, payment of damages, and the voiding of contracts.
  • In early September 2011, the Swiss bank UBS announced that it had lost over 2 billion dollars, as a result of unauthorized trading performed by Kweku Adoboli, a director of the bank's Global Synthetic Equities Trading team in London. In April and May 2012 large trading losses occurred at JPMorgan's Chief Investment Office, based on transactions booked through its London branch. Trader Bruno Iksil, nicknamed the London Whale, accumulated unauthorized outsized CDS positions in the market. The original estimated trading loss of $2 billion was announced, with the final actual loss expected to be substantially larger. HSBC Holdings Plc agreed to pay a record $1.92 billion in fines to U.S. authorities for allowing itself to be used to launder a river of drug money flowing out of Mexico and other banking lapses. is the world’s most heavily regulated industry In January 2008, the bank Société Générale lost approximately €4.9 billion closing out positions over three days of trading beginning January 21, 2008, The bank states these positions were fraudulent transactions created by Jérôme Kerviel, a trader with the company. After analyzing post-loss & causal factors, they all had a good chance of being prevented or detected if Key Risk Indicators (KRIs) had provided information that could be aggregated, analyzed, and escalated. 16 DIGITAL REASONING | CONFIDENTIAL
  • Example: UBS Rogue Trading 2012
  • Example: UBS Rogue Trading 2012
  • Many KRIs defined to monitor trading risk
  • With access to the right data product – We can build an “Holistic view” of a Trader’s risk profile
  • Human risks are hard to predict: Even the best designed risk controls are subject to the failings of people’s experience, attitude, mindset and values.
  • Traders are people. People communicate with people. People communicate using human language.
  • Human language is a rich data source that enables data scientists to study people’s past behavior or predict future behavior.
  • Human language is dirty data. Different languages. Full of ambiguity. Large amounts of it, and very noisy. Difficult to count things.
  • "You shall know a word by the company it keeps." - J. R. Firth, English linguist
  • Making human language tractable: Resolving entities, facts & relationships In time and space *09/26/201 3 *Social Interaction *09/26/201 3 26
  • Transforming data into knowledge 26 Sept VZ (*Verizon? ) Tom Watson Social Interaction UBS +44-20-7567-8000 *Social Hans Interaction Gruber 1 Finsbury Avenue London, UK EC2M 2PP hans.gruber@uk.ubs .com Kurt Dyson kdyson@richardson
  • Combining data from multiple sources – Social media 28 28
  • Combining data from multiple sources – Financial system 29
  • Combining data from multiple sources – Trade Surveillance On September 29, UBS trader Hans Gruber executes a short on 100K shares of AAPL shares for Kurt Dyson, a high profile buy side client of the firm. On Oct. 7, Apple announces disappointing iPhone6 sales resulting in a 10% share price drop and a windfall profit for Kurt Dyson based on the Gruber’s short order.
  • Data Cocktail – Holistic View of Hans Gruber 26 Sept VZ Verizon (*Verizo n?) Tom Watson Social Interaction UBS +44-20-7567-8000 Hans Gruber 1 Finsbury Avenue London, UK EC2M 2PP hans.gruber@uk.ubs .com Kurt Dyson kdyson@richardson 31
  • Analyzing Enron’s public email data. Bill DiPietro & Jascha Swisher Digital Reasoning.
  • Example: KRI for Human Language “Legal Entity” on restricted trading list occurring in electronic communications + Legal Entity occurring in the context of “deal related” language + Communication “outside” company firewall
  • PHONE (615) 370-1860 EMAIL marten@digitalreasoning.com WEB digitalreasoning.com * *