Statistical analysis

Harnessing AI to achieve Libor transition

Chris Dias, principal at KPMG, explains how the vast increase in accuracy that artificial intelligence (AI) offers when dealing with large volumes of complex agreements is crucial to exploring the market opportunities and mitigating the risks of the…

Resist the rise of the risk management machines

Overreliance on modern risk management systems, and metrics such as value-at-risk, can blind firms to tectonic structural market shifts. To help alleviate this problem, the use of human judgement and intervention is required, argues Vincent Kaminski

Modest means

Credit loss models typically calibrate default separate from loss given default. Here, Jon Frye calibrates simultaneously, using credit loss data. This produces a surprising test result: the credit loss models do not significantly outperform a…

Sponsor's article > Statistical process control

Too often, finance professionals manifest a smug sense of superiority towards their peers in manufacturing. In this third column in a series, David Rowe argues that when it comes to operational risk management, the manufacturing sector has much to teach…

Accord preparations: the rest is yet to come

While the debates have raged for months about many aspects of the proposed Basel II Accord, on some points there has been relative silence, in particular with regard to the seeming overreliance on statistical techniques.

Marking the cards at ANZ

Mark Lawrence of ANZ Group describes how the bank chose and developed a “scorecard” approach to measuring operational risks, and how – 12 months after the start of the project –it is already achieving a more efficient allocation of capital.

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: