Citigroup to pay $15 million for analyst supervision lapses

A unit of Citigroup Inc must pay a $15 million fine for not adequately supervising communications among its equity research analysts, clients and the firm’s sales and trading staff, Wall Street’s industry funded regulator said on Monday.


(Reuters) – A unit of Citigroup Inc must pay a $15 million fine for not adequately supervising communications among its equity research analysts, clients and the firm’s sales and trading staff, Wall Street’s industry funded regulator said on Monday.

The supervision lapses at Citigroup Global Markets Inc, which occurred between January 2005 and February 2014, included an instance in which the firm allowed one of its analysts to participate indirectly in two of its initiatives to promote clients’ initial public offerings to investors, the Financial Industry Regulatory Authority said.

Citigroup, which settled the allegations with FINRA, neither admitted nor denied the charges, but consented to the entry of FINRA’s findings.

“We are pleased to have resolved and put this matter behind us,” a Citigroup spokeswoman said in a statement. “Citi takes its regulatory compliance obligations seriously, and we believe we have strong procedures and controls in place to address…

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I spent a weekend on a cruise ship staffed by robot bartenders

The two robots are perfectly efficient, fast bartenders. They always select the perfect amounts of Bulleit borboun, ice, and lime, and shake it just so before dumping it carefully into my plastic…


Quantum of the Seas is full of things like the Bionic Bar. Things that Royal Caribbean did not because it had to, or because it saved or made lots of money (though the company certainly hopes to do plenty of both), but because it could. Because those things just seemed better, and because Quantum of the Seas is meant to be what the company calls "a before-and-after" ship. This is supposed to be the first of a new kind of ship, the one that resets the bar for everything that comes after it. With it, Royal Caribbean seeks a different kind of cruiser, a person who would never before have considered spending their week-long vacation on a boat.

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Big Data Problem: Could Fake Reviews Kill Amazon?

Amazon authors are vulnerable to the following fraud, that would eventually result in significant business loss for Amazon.
A start-up company selling good reviews for $500 per book with a $100 monthly fee. It would work as follows.
A new book receives several negative reviews (1-star) using the methodology developed in the previous section
The author is then reached by email: typically, most authors have a public email address easy to harvest with automated tools, or easy to purchase from mailing list re-sellers
The start-up offers to post good reviews only (and it does not discuss the bad reviews previously planted before reaching out to the author to “fix” the problem)


How scalable is this? A college student could easily make $500 a day, targeting only a few books each day. That’s $100k per year, and collect the money via Paypal. Because the money is relatively easy to make, a large number of (educated and under-employed) people could be interested in setting up such a scheme, eventually targeting thousands of authors each day when combined together. Or someone might find a way to automate this activity, maybe using a Botnet, and make millions of dollars each year. Many authors would eventually refuse to have their books listed on Amazon, and choose to self-publish with platforms such as Lulu. Publishers would also opt out of Amazon. Revenue on Amazon (from book sales) would drop. Or Amazon could simply eliminate all reviews and not accept new ones.

Interestingly, it appears that Yelp might be making money with a similar scheme: out of fake reviews and blackmailing small businesses listed on its website. And I’ve seen companies selling fake Twitter followers or Facebook profiles, though they quickly disappear. Even LinkedIn was recently victim of a massive scheme involving fake profiles automatically generated. 

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‘Vegas’ explores mining of our personal data : Ct

Gary Loveman, CEO of Caesars Entertainment, seems like an unlikely mastermind for a Vegas casino. After earning a Ph.D. in economics from the Massachusetts Institute of Technology, Loveman got a job teaching at Harvard Business School.

His research into consumer behavior led to the theory that the lifetime value of a single customer is affected by their satisfaction — the more satisfied a customer is, the more valuable they are to a company. When Loveman started to apply his research to casinos, he discovered that customers didn’t show much loyalty to any single casino over time. He suggested that the best way to retain customers was to use data the company was already collecting to develop a more robust loyalty program.


In addition to data collected by casinos, Las Vegas is a trove of public data — more couples marry there than anywhere else in the United States. And the subsequent divorce records provide even more personal details that then become part of the public record, Tanner notes.


While U.S. law does restrict trade of some personal information like medical and financial data and how some types of data can be used for decisions like hiring or granting loans, the rules are otherwise rather thin.

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Understanding the Real Problem – Enterprise Complaints Analytics

A couple of weeks ago, my team was asked to come up with a solution for an Enterprise Complaints Platform with Advanced Analytics capability for a Fortune 50 Bank. The initial scope statements were high level requirements like for example, Identification of high risk complaints that were likely to be escalated to regulatory agencies, Complaint Root Cause Analysis, etc.
It quickly became apparent that while the solution did include Advanced Analytics components what was really needed was a repeatable process for Data Discovery and Descriptive Modeling that would provide the Associates with a semi automated way to complete the Root Cause Analysis or provide a way to identify the initial set of categories that could be used as inputs for Advanced Predictive and Prescriptive Analytics.


With these challenges in mind, it became apparent that any proposal we made for an Advanced Analytics solution would require considerable pre-processing capability on the input data including Big Data and a workflow capability to provide the “value add” repeatable automated processes that the front line associates were looking for. While this may seem obvious in hindsight, it was a concept that was not immediately apparent when we started the evaluation with the business looking for a One stop solution to meet their Advanced Analytics needs.

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Selection of 29 external resources and articles from thought leaders – November 17

An estimate that is slightly biased but robust, model independent, easy to compute, and easy to interpret, is better than one that is a non-biased, difficult to compute, mysterious, or not robust. That’s one of the differences between data science and statistics. Also, many times, reinventing the wheel can be more efficient than researching the literature for days, to perhaps find and old solution, barely documented, that won’t work any better than your (quickly designed) reinvented algorithm, on real data.


Starred articles were potential candidates for our picture of the week published in our weekly digest. Enjoy our new selection of articles and resources (R, data science, Python, machine learning etc.) Comments are from Vincent Granville.

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5 Trends That Will Change How You Use Social Media in 2015

Hacks released in October show a hidden payment feature deep inside Facebook’s popular Messenger app. If activated by the company, it will allow the app’s 200 million users to send money to each other using just debit card information, free of charge. Meanwhile, the network has also already rolled out a new Autofill feature (a kind of Facebook Connect for credit cards), which allows users who save their credit card info on Facebook to check out with 450,000 e-commerce merchants across the web.


This year started with a death sentence for Facebook. In January, a research company called Global Web Index published a study showing that Facebook had lost nearly one-third of its U.S. teen users in the last year. Headlines pronounced the network “dead and buried.”

Fast forward to the present and Facebook is reporting record growth. The company earned $2.96 billion in ad revenue in the third quarter of 2013, up 64 percent from just a year ago. More impressively, the network has added more than 100 million monthly active users in the last year.

All of which goes to show how difficult it can be to predict the future of social media. With that caveat in mind, here’s a look into the crystal ball at five ways social media will (likely) evolve in 2015.

Your social network wants to be your wallet

Hacks released in October show…

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What’s In Store for Big Data and Analytics?

To keep up with the increase of data from mobile and sensor sources, companies will also be required to quickly ingest huge amounts of different types of data. Technology will, for the most part, keep up with that requirement. However, “just because we can ingest that amount of data, should we do so?” Nugent asks. The answer will depend on the use case, he notes. “This is just the beginning and the potential demands we understand data lifecycles better than we did in the past,” he adds.


Improved analysis and data visualization tools are needed to comprehend relationships, patterns, etc. “Extract, transform and load technologies (ETL) have to evolve so they can perform their services across a broader set of inputs and outputs, all while providing a dynamic capability for extending to not yet known forms"

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Beyond Digital Analytics Metrics | Analytics & Optimization

ustomer Lifetime Value (LTV) is a metric that predicts the profit of an entire relationship with a client. Instead of focusing on the profit for the first sale, we try to estimate the profit for all sales we will make to that customer. Hence, we focus the company on long term relationships rather than short term profits, in other words, we incentivise innovation, better products and better customer service. Not bad for a single metric 🙂


It’s surprising how often this is overlooked, and we’re fascinated by statistically impressive models that are modeling (and hence optimizing) for the wrong business behavior.

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Data science: ‘Machines do analytics. Humans do analysis’

Sullivan said it’s difficult to show ROI on data science capabilities in the first year. Companies should think of that first data science year as a bootstrapped effort where you’re discovering the unknown unknowns, curating data and tagging it so it can later be linked to a business outcome. “It’s about small wins at first,” Sullivan said. Another key point from Zutavern: No company has perfect data ontology and categorization so you shouldn’t put off an analytics effort in hopes of perfection. Every company has data gaps and the information is likely to be messy.


"Human behavior is hard to predict," Sullivan explained. "For instance, in fraud it’s hard to analyze a human who is doing everything to defeat you and avoid detection."

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