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A couple of weeks ago, a colleague of mine came to tell me about a new finding that she knew I'd love: BCM helps reduce data breach costs.

All I could think was, 'FINALLY.'

This is something that I've been telling clients for years: BCM is more valuable to your business than you can possibly imagine.

The new research from Ponemon Institute found that data breaches now cost as much as $4m - to say nothing of reputational hits. And, you could face a double whammy with higher insurance premiums, too.

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How do you get the balance right between customer service and security? The battle between banks and fraudsters is ongoing, with banks losing millions each year through fraud.

In addition, the rise of digital and mobile banking over the past few years has led to banks investing heavily in their fraud and security measures. These authentication devices alongside integrated malware detection and fraud monitoring systems can be a barrier to a satisfying, simple customer experience. How do banks prot

One of the most concerning trends that continually persists in operational risk management, is the lack of interest from analysts to attempt to quantify this risk exposure coherently.

In this blog we look operational risk from the perspective of the normal and the extreme.

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In general only a handful of businesses correctly capture Operational Risk Loss Data and of those that do, only a small number of risk units in these firms are modelling their risk data in a coherent manner. After a bit of research on the internet and in various other channels, it has become relatively apparent that there isn't a comprehensive list of potential models which can be uses for understanding Operational Risk. I would have expected an analyst somewhere at some point in time to have do

Building a loss reporting database for operational risk. What you need & links to some outstanding risk systems.

In our last blog posting on Monte Carlo and Loss Data we described the importance of the Loss Data exercise. A few people have personally emailed me asking for more information on this aspect of risk management, so I have decided to write a blog post on it.

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By looking at a case study in Monte Carlo and Loss Data, we are able to see how important it is to model loss experience and to categorise operational risk loss events.

Recently I had a discussion on modelling risk with a fantastic and successful business person who said to me : "I have read about Monte Carlo, you even make mention to it on your blog but it doesn't make great sense to me. The maths in Monte Carlo is even worse because it seems to confuse the concept by taking it into an academic

In the world of operational risk, there are a lot of analysts who believe that they can dimension the impacts from uncertainty by counting the number of events they experience over a period of time and then multiply that count by the average loss amount for the total event horizon they observe.

This approach for quantifying the impacts from uncertainty is full of error and it should be avoided. In fact, let's be clear, it is so fundamentally wrong as a measure of exposure that it isn't even a goo

This white paper explores data loss from a contrarian point of view - exploring the top 7 shortcuts you can take to ensure that you lose your data. And since a fundamental responsibility of any information technology professional, as well as any C-level executive, is to ensure that the data upon which any company is created is protected - scrupulously following these shortcuts should also ensure that you lose not only your data but your job as well.

===> http://bit.ly/LosingData