Tag Archives: risk analysis

Formalizing and optimizing your risk architecture within the BCBS context

1 Jul

This article was interesting to me for two reasons: 1) It formalized a data view of risk management within the financial community around the context of BCBS (and for BCBS 239 related to Risk Data Aggregation and Reporting); and, 2) it provided an interesting perspective on governance / data quality and associated metrics.

This paragraph sums it up:

“What is actually happening in practice is that each major institution’s regional banks are lobbying/negotiating with their local/regional regulators to agree on an initial form of compliance – typically as some form of MS Word or PowerPoint presentation to prove that that they have an understanding of their risk data architecture. One organization might write up 100 page tome to show its understanding – and another might write up 10. The “ask” is vague and the interpretation is subjective. Just what is adequate?”

The perspective on governance that  the article proposed is a way to “systematically compare different architectures” with a set of metrics that was understandable, obtainable, and actionable.

Have a read – let me know your thoughts. The article also provides a nice summary of the BCBS requirements.

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The different aspects of BI

5 Dec

http://www.martinsights.com/?p=774

I like the recognition that approaches need to be integrated in order to create useful insights. Valuable insights come from balancing the needs and capabilities of  business strategy, business analysis, business intelligence and advanced analytics.

Back to basics

11 Feb

As an Analytics professional, I’m exhausted. I’m exhausted by the constant search for silver bullets (by others). And I’m even more exhausted of the notion upheld by every non-Analytics person around me that what I provide might as well come in the form of pixie dust. Product companies love to create this mystique around Analytics; making it look as difficult, magical, and opaque as possible. That helps them sell their software. However, they have all been so successful at this approach that it has made my job eternally harder. By convincing potential buyers that what they have to offer is magic, silver bullets in a black box, the software companies raise buyer expectations to unreasonably high levels. Purchasers of that software are also my clients; and they are often astounded that I can’t unbox these magical silver bullets and begin firing away like a gun slinger in an old western. As cool as that sounds, it just isn’t realistic. So, what is realistic? Well, to best answer that question, let’s start at the beginning…

As I see it, Analytics is fairly simple. It is the rigorous application of statistics, mathematics, common sense, and technology to arrive at valuable outcomes for clients. Hard work is rewarded with more hard work, and there are no short cuts! Period. And it all starts with data. Without data, you are merely performing an academic exercise, which can only be rigorously validated by…. Collecting data! Thus, Analytics in all its forms truly revolves around data. So, before one even bothers to read a pamphlet on the latest whizzy thing in the market, there are some basic questions to be answered.

1) What questions do you want to answer?
2) What data do you have to answer those questions?
3) Where are the data gaps, and what are your options for filling them?

Answering these questions can sometimes take longer than one might expect as there is real work to be done at each step–often Analytics professionals help organizations work through these steps. However, it is only after these questions have been effectively answered that an organization has the information necessary to make sound decisions regarding Analytics (e.g., Choosing the right software, etc.). And that is when the hard work begins.

Analytics professionals begin the task of understanding data through exploratory analysis and experimentation. Part of this work includes cleaning and preparing the data for analysis. Based on the results of this process, predictive models can be developed, improved, and automated. Whether the goal is to predict the next word in a sentence, risk of default on a loan, the next location of a crime, or the likelihood of fraudulent activity; the same general process still applies. No bullets, wands or pixie dust… Just blood, sweat, and … coffee. Lots of coffee.

Link

Success of TSA’s Risk-Based Security Focus Hard To Gauge

14 Dec

Success of TSA’s Risk-Based Security Focus Hard To Gauge

I like the idea of randomizing the approach. TSA says it is to keep the staff awake. however, it also serves to look at things from a new angle, and potentially identify new issues.

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