May 04, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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>> Improving the Value of Customer Experience Analytics by bobehayes

>> For the Bold, Bullied & Beautiful by v1shal

>> Big Data Provides Big Insights for U.S. Hospitals by bobehayes

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 Streaming analytics market forecast for 2016 made available by top research firm – WhaTech Under  Streaming Analytics

 Research details developments in the financial analytics global market analysis and forecast to 2021 – WhaTech Under  Financial Analytics

 6 Questions Every CEO Should Ask About Their Data Security – Information Management Under  Data Security

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Data Mining


Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more


The Misbehavior of Markets: A Fractal View of Financial Turbulence


Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… more


Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.


Q:What is the Central Limit Theorem? Explain it. Why is it important?
A: The CLT states that the arithmetic mean of a sufficiently large number of iterates of independent random variables will be approximately normally distributed regardless of the underlying distribution. i.e: the sampling distribution of the sample mean is normally distributed.
– Used in hypothesis testing
– Used for confidence intervals
– Random variables must be iid: independent and identically distributed
– Finite variance



Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

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The world is one big data problem. – Andrew McAfee


#GlobalBusiness at the speed of The #BigAnalytics

 #GlobalBusiness at the speed of The #BigAnalytics


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A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

How Big Data Analytics Can Help Track Money Laundering

Criminal and terrorist organizations are increasingly relying on international trade to hide the flow of illicit funds across borders. Big data analytics may be the key to tracking these financial flows.

or the past decade, governments around the world have established international anti-money laundering (AML) and counter-terrorist financing efforts in an effort to shut down the cross-border flow of funds to criminal and terrorist organizations. Their success has encouraged criminals to move their cash smuggling away from the financial system to the byzantine world of global trade. According to PwC US, big data analytics are becoming essential to tracking these activities.

It’s easy to understand why criminal and terrorist organizations would turn to the global merchandise export trade to hide the movement of their funds. It’s a classic needle in a haystack — an $18.3 trillion business formed of a “web of complexity that involves finance, shipping and insurance interests operating across multiple legal systems, multiple customs procedures, and multiple languages, using a set of traditional practices and procedures that in some instances have changed little for centuries,” PwC says.

Watching the Money Flow

There’s no real way to quantify how much money criminals are invisibly exchanging using this system. PwC notes that the Global Financial Integrity (GFI) research and advocacy organization estimates that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering (TBML), from more than $200 billion in 2002 to more than $600 billion in 2011. GFI believes more than $101 billion was illicitly smuggled into China in 2012 via over-invoicing, which is only one of the common TBML techniques.

“At its core, trade finance is an old-fashioned business,” the report says. “As other industries have adopted more technology- and data-driven infrastructures, trade finance has remained extremely document-intensive and paper-based, moored on a framework of instruments, systems, and practices that have proven their effectiveness and earned global trust over the generations.”

But they are also opaque, PwC says, making it extremely difficult for AML efforts to see what’s going on.

“For example, trade finance’s legacy procedures affect the relationship management aspect of AML, which includes know-your-customer (KYC) procedures and examination of customer documentation prior to transaction approval,” the report says. “In this paper-intensive environment, AML remains a largely manual procedure and thus prone to human error. It remains reliant upon established “red flag” checklists provided by regulators, in which transactions are manually reviewed by analysts, escalated should any concerns be raised, and then subjected to further manual review if wrongdoing is suspected.”

The Need to Share Data

This state of affairs is exacerbated by a number of factors, especially the lack of data sharing between customs, tax and legal authorities and a tendency to rely on AML procedures designed to target cash smuggling and financial system misuse. Instead, PwC says, authorities need to develop targeted TBML responses that focus on data sharing and text and data analytics.

So what exactly does TBML look like? Common TBML techniques include the following:

Under-invoicing. The exporter invoices trade goods at a price below the fair market price. This allows the exporter to effectively transfer value to the importer, as the payment for the trade goods will be lower than the value the importer receives when reselling the goods on the open market.
Over-invoicing. This technique is much the same as the first, except in reverse. The exporter invoices trade goods at a price above the fair market value, allowing the importer to transfer value to the exporter.
Multiple invoicing. With this technique, a money launderer or terrorist financier issues multiple invoices for the same international trade transaction, justifying multiple payments for the same shipment. “Payments can originate from different financial institutions, adding to the complexity of detection, and legitimate explanations can be offered if the scheme is uncovered (e.g., amendment of payment terms, payment of late fees, etc.),” the report explains.
Over- and under-shipment. In some cases, the parties simply overstate or understate the quantities of goods shipped relative to the payments sent or received. PwC calls out an extreme example of this, known as “phantom shipping,” in which no goods are exchanged at all, but shipping and customs documents are processed as normal.
False description of trade goods. With this technique, money launderers misrepresent the quality or type of trade goods. For instance, they might replace an expensive item listed on the invoice and customs documents with an inexpensive item.
Informal money transfer systems (IMTS). These networks have, in many cases, been co-opted by criminals and terrorists. PwC points to Colombia’s Black Market Peso Exchange (BMPE) as a prime example. Established by Colombian businesses trying to get around Colombia’s restrictive currency exchange policies, the BMPE allows users to sell dollars to a broker, who then trades them for Pesos to a legitimate Colombian business that needs hard U.S. currency to purchase goods for shipment to South America. It’s not just Colombian drug traffickers repatriating their profits either; PwC notes that similar systems exist around the world, including the hawalahundi system on the Indian sub-continent and others in Venezuela, Argentina, Brazil and Paraguay.

What Can Big Data Do?

So how can big data analytics help organizations find these illicit transactions in an $18.3 trillion haystack? Well, for one, the sea of documents generated by this activity — the commercial invoices, bills of lading, insurance certificates, inspection certificates, certificates of origin and more — that make it so difficult to see what’s truly happening may also be the point of vulnerability.

“A global, one-stop solution to TBML is highly unlikely,” PwC says. “The most effective solution would involve the imposition of bank-like compliance requirements on all organizations that trade internationally. But while this would create transparency across transactions, it would also create a massive layer of red tape that would adversely impact the preponderance of traders and related parties who are engaged in legitimate activity. The largely unquantifiable nature of the TBML problem makes it difficult to justify such an intrusive, expensive and vastly complicated solution. Short of global regulation, we have global analytics.”

In other words, automating anti-TBML monitoring — extracting and analyzing in-house and external data, both structured and unstructured — is of critical importance.

PwC believes such a program must properly align across key business areas and incorporate automated processes using a variety of advanced techniques, including:

Text analytics. The capability to extract data from text files in an automated fashion can unlock a massive amount of data that can be used for transaction monitoring.
Web analytics and Web-crawling. These tools can systematically scan the web to review shipment and custom details and compare them against corresponding documentation.
Unit price analysis. This statistic-driven approach uses publicly available data and algorithms to detect if unit prices exceed or fall far below global and regional established thresholds.
Unit weight analysis. This technique involves searching for instances where money launderers are attempting to transfer value by overstating or understating the quantity of goods shipped relative to payments.
Network (relationship) analysis of trade partners and ports. Enterprise analytics software tools can identify hidden relationships in data between trade partners and ports, and between other participants in the trade lifecycle. They can also identify potential shell companies or outlier activity.

International trade and country profiling analysis. An analysis of publicly available data may establish profiles of the types of goods that specific countries import and export, flagging outliers that might indicate TBML activity.

Thor Olavsrud

Orginally posted via “How Big Data Analytics Can Help Track Money Laundering”

Source: How Big Data Analytics Can Help Track Money Laundering by anum

The Modern Day Software Engineer: Less Coding And More Creating

Last week, I asked the CEO of a startup company in Toronto, “How do you define a software engineer?”.

She replied, “Someone who makes sh*t work”;

This used to be all you needed. If your online web app starts to crash, hire a software engineer to fix the problem.

If your app needs a new feature, hire a software engineer to build it (AKA weave together lines of code to make sh*t work).

We need to stop referring to an engineer as an ‘engineer’. CEOs of startups need to stop saying ‘we need more engineers’.

The modern day ‘engineer’ cannot simply be an engineer. They need to be a renaissance person; a person who is well versed in multiple aspects of life.

Your job as a software engineer cannot be to simply ‘write code’. That’s like saying a Canadian lawyer’s job is to speak English.

English and code are means of doing the real job: Produce value that society wants.

So, to start pumping out code to produce a new feature simply because it’s on the ‘new features list’ is mindless. You can’t treat code as a means itself.

The modern day engineer (MDE) needs to understand the modern day world. The MDE cannot simply sit in a room alone and write code.

The MDE needs to understand the social and business consequences of creating and releasing a product.

The MDE cannot leave it up to the CEOs and marketers and business buffs to come up with the ‘why’ for a new product.

Everyone should be involved in the ‘why’, as long they are in the ‘now’.

New frameworks that emphasis less code and more productivity are being released every day, almost.

We are slowly moving towards a future where writing code will be so easy that it would be unimpressive to be someone who only writes code.

In the future Google Translate will probably add JavaScript and Python (and other programming languages) to their list of languages. Now all you have to do is type in English and get a JavaScript translation. In fact, who needs a programming language like JavaScript or Python when you can now use English to directly tell a computer what to do?

Consequently, code becomes a language that can be spoken by all. So, to write good code, you need to be more than an ‘engineer’. You need to be a renaissance person and a person who understands the wishes, wants, emotions and needs of the modern day world.

Today (October 22nd, 2015), I was at a TD Canada Trust networking event designed for ‘tech professionals’ in Waterloo ON, Canada. The purpose of this event was to demo new ‘tech’ (the word has so many meanings nowadays) products to young students and professionals. The banking industry is in the process of a full makeover, if you didn’t know. One of the TD guys, let’s call him Julio, was telling me a little summary of what TD was (and is) trying to do with its recruitment process.

Let me give you the gist of what he said:

“We have business professionals (business analysts, etc) whose job is to understand the 5 W’s of the product. Also, we have engineers/developers/programmers who just write code. What we are now looking for is someone who can engage with others as well as do the technical stuff.”

His words were wise, but I was not sure if he fully understood the implications of what he was talking about. This is the direction we have been heading for quite some time now, but it’s about time we kick things up a notch.

Expect more of this to come.
Expect hybrid roles.
Expect it become easier and easier to write code.
Expect to be valued for your social awareness paired with your ability to make sh*t work.

Perhaps software tech is at the beginning of a new Renaissance era.

*View the original post here*

Twitter: @nikhil_says


Originally Posted at: The Modern Day Software Engineer: Less Coding And More Creating by nbhaskar