There are huge numbers of variants of deep architectures as it’s a fast developing field and so it helps to mention other leading algorithms. The list is intended to be comprehensive but not exhaustive since so many algorithms are being developed  ,.
While Deep Learning has shown itself to be very powerful in applications, the underlying theory and mathematics behind it remains obscure and vague. Deep Learning works, but theoretically we do not understand much why it works. Some leading machine learning theorists like Vladimir Vapnik criticise Deep Learning for its ad-hoc approach that gives a strong flavour of brute force rather than technical sophistication. Deep Learning is not theory intensive; it is empirical based more (hence…Continue
This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before.…Continue
Added by Syed Danish Ali on June 26, 2016 at 5:00am — No Comments
“The only function of economic forecasting is to make astrology look respectable” John Kenneth Galbraith
Predictive modeling and traditional ratemaking is an exercise of forecasting the future, whether directly or indirectly (indirectly as generalizing historical lessons to the future). But is such forecasting so hopeless as being same as seeing into a crystal ball?
The advantage of data scientist and actuaries is their close in contact with…Continue
Added by Syed Danish Ali on June 20, 2016 at 2:30am — No Comments
Shakespeare and Fuzzy Logic
There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy. - Hamlet (1.5.167-8), Hamlet to Horatio
Shakespeare teaches us in this Hamlet quote that reality is much more complex than our mental projections and understanding. Reality is fuzzier than we would care to think. Although introducing subjectivities to modeling seems to harm the…Continue
We should attempt to creatively synthesize the many pluralistic approaches as well as focus more on synergistic interpretation of findings of these pluralistic researches.
We can recognize that though we cannot precisely predict black swans but forecasting emerging liabilities and their ratemaking can be a professional-character building experience in itself where we train to be better evolvers rather than better predictors alone.
Added by Syed Danish Ali on June 20, 2016 at 1:44am — No Comments
This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems.
‘In pricing, are we swapping specific risk for systematic risk?’
Added by Syed Danish Ali on June 20, 2016 at 1:30am — No Comments
Up till recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops. In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents promising prospect for overcoming problems of big data. Deep learning is a method of machine learning that undertakes calculations in a layered fashion starting from high…Continue
Added by Syed Danish Ali on June 20, 2016 at 1:15am — No Comments
In this case study for Complexity Science, we aim to show its value addition through agent based modeling of an important insurance problem, i. e, underwriting cycle. Underwriting in Property-Casualty/General Insurance is where insurance underwriting follows and mimics the Economy and its cycles of boom and busts. These soft and hard market swings are a key source of challenge and volatility to insurers. The main value addition of…Continue
It is quite difficult at first to precisely define ‘Complexity Science’. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock…Continue
Topology is the mathematical study of the properties that are preserved through deformations, twistings, and stretchings of objects. Tearing, however, is not allowed. Topology can be used to abstract the inherent connectivity of objects while ignoring their detailed form. Put simply, Topology is a mathematical discipline that studies shape and assumes that shape has meaning. This post discusses topology’s applications in finance and insurance. While…Continue