In my first post, I discussed how analytics should play a major role in modern policing to help forces plan for the future and meet the requirements of the Force Management Statement. In my second post, I went on to discuss the importance of police data and data preparation to
Nous voulons mener des actions et mesurer leur impact - de manière scientifique. Pour évaluer la vitesse moyenne (d'une voiture) à mi-chemin d'une distance donnée, nous devrions marquer le point médian et utiliser un chronomètre. Pour mesurer la conversion générée par un catalogue imprimé et la comparer à celle d'un
In my previous blog post, I introduced the “four pillars of trust” for automated decisions. The key takeaway was that explainability and transparency refer to the entire analytical process. But what about the “black box” of machine learning algorithms? Here, too, the analytical platform must guarantee transparency. The good news
The reach of sports analytics is growing by the day. Increasingly, fans expect access to additional information when viewing their favourite sport. The Premier League has started to provide information about distance run, completed passes and penalty placement history. The Australian Football League shows—on live TV—the heart rate of a
Insurance companies are working hard to renew their business models. A huge part of that is a modernised approach to actuarial practice, but why and how? I asked my colleague, actuary Diego Rivas. The insurance business looks like a smooth, calm river from the outside. Is that image misleading? DR:
A relação do SAS com a AGEAS Seguros, outrora AXA Portugal, começou há cerca de 20 anos, com o objetivo da então líder mundial em proteção financeira poder aceder a bases de dados e criar uma Data Warehouse. Estava assim dado o primeiro passo para uma relação de confiança e
Could smart cities and social media be the perfect match? Politics is a divisive issue and not simply from a philosophical standpoint. Different sections of society have very different engagement levels. We could postulate many reasons for this, but the real question is, how can government re-energise citizen engagement in
Most of us occasionally stand back and marvel at the meteoric rise of businesses succeeding in the algorithm economy. Not only are they steeped in data decisioning, but it’s also in their DNA. True, they are fortunate to be born of big data and in the advanced analytics era. But
One of the main reasons for the last financial crisis was that some of the analytical models used to estimate risk were incorrectly built and used. Resulting regulations on the US market including the Dodd-Frank Act and the Comprehensive Capital Analysis and Review (CCAR) therefore require the use of an
Model risk management is an important issue for companies in a number of sectors. All models – to predict demand, for example, or to support business decision making pose some risk. But both model complexity and regulatory requirements are increasing around the world, and model risk management is, therefore, becoming considerably
