Series: BCBS 239 - Principle 2

The 14 Principles of BCBS 239

Principle 2:
Data architecture and IT infrastructure – A bank should design, build and maintain data architecture and IT infrastructure which fully supports its risk data aggregation capabilities and risk reporting practices not only in normal times but also during times of stress or crisis, while still meeting the other principles.

In the previous post in this series on BCBS 239, Brooke Upton discussed Principle 1 and outlined the need for accurate trustworthy data and some steps you can take to meet the data governance requirements. In this post, I’ll discuss the need for data consistency and the place an integrated infrastructure plays.  

The Basel Committee on Banking Supervision’s motivation for Principle 2 is to stress the importance of technology in meeting these requirements.  The view of the committee is that risk data aggregation and reporting (RDAR) systems are critical features of a financial institution.  So much so that they play a role in business continuity planning and ought to be subject to business impact analysis.  It is obvious and is reinforced, with hindsight of recent crises, the harmful effect upon firms that lacked these critical capabilities.

In particular, the principle calls for data consistency.  Institutional data dictionaries (metadata repositories) are important for ensuring consistent definitions, as well as documenting resources and the relationship between those resources.  For example, one should be able to search the term “liquidity” and see its definition as well as all other related terms (i.e. LCR, NSFR, etc.), tables, business rules, dashboards, processes, models and associated individuals (such as the liquidity risk committee members and data stewards).  Consistency in identifiers and naming conventions for legal entities, counterparties, customers and accounts are also expected.

Highly integrated systems are required to gain this level of consistency and clarity.  In the above data dictionary example, data must be fed from a company directory, data models, business rules and risk model repositories as well as ETL jobs.  This is not a trivial endeavor, and the master data management (MDM) and ETL systems have to be up to the task.  The enormity of the task becomes clearer when considering that legacy systems, point solutions, document management system and internally developed web systems may all be running competing database management systems or technologies with different semantics for resources access.  Even when those resources house identical data as is normally the case when data is fed to downstream niche systems.

Communication and controls become ever more important in light of these increased consistency and integration demands.  Owners of these systems (both business and IT) must be able to verify and certify all data entering and leaving their systems.  Failed audits are of concern for those institutions that cannot show the who, what, when and why of any data making its way into regulatory reports.

There is also the issue of aggregating data.  It is very common to have point solutions from niche vendors performing a specific area of risk.  The issue then becomes integrating risk measure across an enterprise that may have used differing assumptions, horizons or market data sources.  Having an aggregation engine that can combine this very complex data to show exposures at the aggregate level and drilldown to the most granular level is crucial.

SAS offers an intuitive platform that addresses each of the issues.  SAS’ risk engine can aggregate data from disparate platforms. Risk data dictionaries document all terms, hierarchies and related resources with ease of exploration and search. And MDM provides the integration of it all.  SAS’s platform integrates with the most prevalent competing platforms.

In the next post, I will discuss the requirements in Principle 3 - Data architecture and IT infrastructure. In the meantime, learn more about how SAS can help you address BCBS 239 compliance.

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Ask the Statistician: Putting statistical results into action

So far in this Ask the Statistician series, we heard statisticians at the Analytics 2013 conference  discuss the benefits of statistical analysis, the types of statistical techniques they use to solve their business problems and how they share their statistical results with non-technical audiences that need to use this information.  So, the next step is to learn more about how they are putting these results in action within their organizations.  After all, if the models aren't used, they are of no benefit. Watch the video below to learn how our statisticians put their statistical analyses into action.

This video features the question:  What are some ways you put your statistical analyses results into action?

We want to hear from you. How are you putting your statistical results into action within your organization?

And check back soon for more upcoming posts and videos featuring our statisticians.

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Series: BCBS 239 - Principle 1

The 14 Principles of BCBS 239

Principle 1:
Governance – A bank’s risk data aggregation capabilities and risk reporting practices should be subject to strong governance arrangements consistent with other principles and guidance established by the Basel Committee.

My colleagues and I have written a series of posts on the principles of BCBS 239. In this post, I’ll focus on the requirements of Principle 1 and describe how high-performance analytics can support you.

BCBS Principle 1 requires that a set of governance processes are created and that management oversees these processes at the executive level. However, before these can be effectively enforced, the data needed to aggregate and report on regulatory compliance must be accurate and trustworthy. The goal of this principle is to hold executives beyond the CRO and across all lines of business accountable for a sustainable framework that will not only meet regulatory obligations but strengthen risk strategy and oversight.

The Basel Committee’s Working Group on SIB Supervision developed a questionnaire to identify the progress (or lack thereof) that global systemically important banks (G-SIBs) are making to meet the January 2016 BCBS 239 compliance deadline.

Read More »

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What’s on your analytics to-do list?

Are you wondering what to do next with your analytics program? The latest issue of sascom magazine provides a handy guide. Check it out to get help checking off must-do items like these:

  1.  Establish an analytics center of excellence. Find out how SunTrust centralized all of the bank’s analytics teams – and improved service delivery with grid computing.
  2. Get buy in for analytics projects. Check out Lenovo’s Innovation Theater.
  3. Deploy analytics in the cloud. Get tips for choosing the right deployment model.
  4. Tackle big (and small) data with Hadoop. Get help with data staging, processing, archiving and more.
  5. Hire an analytics dream team. Learn what differentiates a data scientist from a data steward.

And anyone could learn an analytics trick or two from Orlando Magic CEO Alex Martins. Read about the magic behind the Magic to learn how Martins has turned a small-market team into one of the NBA’s biggest earners.

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Predictive analytics described in one word

At SAS Global Forum 2014 customers were asked to describe SAS in one word and they came up with quite a few including awesome, powerful, data, fun, easy, and of course statistics, analytics, and PROC.   Then I gave it some thought on what one word would I choose to describe SAS.  This word cloud popped into my mind.

Then it hit me, I would choose strategic!   Why strategic? Because operational or plain BI reports (those that don't bake in analytic insight) only look at what has happened in the past and so they help keep your business running this year, but the types of reports that predict what happens in the future (like those based on analytics) provide a strategic advantage to help beat your competition so you continue to be successful in your business three to five years from now.  Read More »

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Convert the analytics agnostics: How to prove the value of customer intelligence early

What could you do with Derren Brown-like powers of persuasion? Convince your boss to give you that overdue raise? Turn the office cynic into your number-one fan? Convert analytics agnostics into data evangelists?

Unfortunately – or fortunately, depending on your point of view – Derren Brown is one of a kind. For the rest of us mere mortals, getting others to buy into a project or idea will usually involve giving hard proof of its value.

But, as attendees at our recent Customer Intelligence roundtable asked: how can you demonstrate value and get executive buy-in before investment is made? How can you prove something works, before it exists?

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Top 5 reasons why IT and business leaders should attend conferences together

Judging by the headlines like “Big Data Sparks Corporate Turf Fights” and “5 Things CFOs Hate About IT,” you might think that every IT organization is at odds with the company’s business leaders.

But let me ask you, does this look like a group of people at odds with one another?

The Coach team (left to right): Danielle Schmelkin, Vice President, Business Intelligence and Customer Engagement; Parinaz Vahabzadeh, Vice President, Global Customer Intelligence and Advanced Analytics; Chunqing Lu, Manager, Strategy and Consumer Insights; Matt Giunipero, Director, Data Management; Sid Shah, Manager, Customer Intelligence and Advanced Analytics

These are business and IT executives from retailer Coach attending the recent SAS Global Forum Executive conference.

Why are they there together? And how dare they get along well enough to take a selfie?

As their account executive, I got to spend quite a bit of time with them in Washington DC during the conference, and the smiles weren’t just for the camera. It struck me that Coach made a great decision sending leaders and representatives to the conference from both IT and the lines of business. Here are five of the benefits I witnessed myself: Read More »

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Series: Understanding analytics' role in BCBS 239

The 14 Principles of BCBS 239

Interestingly, the Basel Committee’s Principles for Effective Risk Data Aggregation and Risk Reporting (otherwise known as BCBS 239) begins with a quote from T.S. Elliot’s The Rock:

Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?

In this age of big data and risk data management, Elliot’s words from 1934 should not ring so true. But for the signatories of the Basel Accords, they peal painfully.  Hopefully, what doesn’t get lost in this post-financial crisis regulation is that those words are significant—not to IT risk—but to risk IT.

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Big data lessons from Google Flu Trends

The Google Flu Trends application has received negative press since 2013 over its inability to accurately detect flu outbreaks. The latest critique, “The Parable of Google Flu: Traps in Big Data Analysis,” from Science magazine compares Google Flu Trends data to CDC data and dissects where the Google analysis went wrong.

As you might remember, Google Flu Trends was designed to pinpoint flu outbreaks by analyzing search data for flu related keywords. The problem? At least 80 percent of people who conduct flu related searches don’t actually have the flu.

Why does this story fascinate us? Partly because we can relate to it: Most of us have searched Google for medical information, and many of us, at one point or another, have thought we had the flu when we did not. But also because we like complex problems that are hard to solve.

The real lessons, though, are in the analysis. And this story reminds us of some important truths:

  1. Crowd sourced data is dirty data. It needs to be cleaned and managed before using it for any type of official analysis.
  2. Social data is just one data point. Whether you’re working with Twitter, Facebook or Google data, it’s going to be more powerful when combined with other data sources – like CDC data, for instance – and not as a standalone source.
  3. Keep monitoring and evaluating. You can’t just build a model and walk away. You have to monitor results and re-model your data over and over again before you might find an accurate representation of reality.

Be sure to read the Science magazine article for additional (and more scientific) lessons.

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What Colin Powell and Jim Goodnight have in common

Colin Powell

General Colin Powell speaks at the SAS Global Forum Executive Conference

Retired four-star General Colin Powell discussed the traits he shared with SAS CEO Jim Goodnight during his morning keynote at the SAS Global Forum Executive Conference yesterday. Among them: A clear passion for education, an appreciation for information and a sincere leadership style.

“I’d like to talk about something that’s important in my life, that is also very important to Jim Goodnight, and that’s education,” Powell said in reference to SAS’ continued philanthropy and the SAS® Analytics U announcement.

Powell, who attended public school from kindergarten through college, discussed how he was unable to enroll in any prestigious military schools, like West Point or Virginia Military Institute, because they had yet to be desegregated.

Yet it was in college when he became interested in ROTC, a path that jumpstarted what would become an illustrious military career.

“What I tell kids as I visit schools all across America is, it’s not where you started in life, it’s what you do in life and where you end up in life,” he said.

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