Deep Learning for Financial Applications : A Survey

arXiv.org Machine Learning

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.


Machine Learning being used by over half of top insurers globally, new research shows

#artificialintelligence

Tel Aviv, Israel, Thursday 1 June 2017 – Earnix, a leading provider of analytics solutions for the financial services industry, today announced the results of a global survey of insurance executives, which shows wide adoption of Machine Learning across the globe, and the expectation that ML will bring "significant" change to the industry over the next 3-5 years. Over half (54%) of the almost 200 insurance executives surveyed said that their organization was using Machine Learning for predictive analytical modelling. Of those deploying the technology, 70% said they were using it for risk modelling; followed by demand models (45%) and fraud detection (36%). Although nascent, most companies using Machine Learning have realized measurable benefits. Over half of the respondents (57%) said that Machine Learning has made their analytical models far more accurate, which has led to better risk assessments, and ultimately better decisions.


The robot that became racist: AI that learnt from the web finds white-sounding names 'pleasant' and black-sounding names are 'unpleasant'

Daily Mail - Science & tech

Humans look to the power of machine learning to make better and more effective decisions. However, it seems that some algorithms are learning more than just how to recognize patterns - they are being taught how to be as biased as the humans they learn from. Researchers found that a widely used AI characterizes black-sounding names as'unpleasant', which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web. Researchers found that a widely used AI characterizes black-sounding names as'unpleasant', which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web Machine learning has been adopted to make a range of decisions, from approving loans to determining what kind of health insurance, reports Jordan Pearson with Motherboard. A recent example was reported by Pro Publica in May, when an algorithm used by officials in Florida automatically rated a more seasoned white criminal as being a lower risk of committing a future crime, than a black offender with only misdemeanors on her record.


Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital

arXiv.org Machine Learning

Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care database of Beth Israel Deaconess Medical Center, Boston, USA was used to retrospectively study trends and outcomes of cancer patients admitted to the ICU between 2002 and 2011. Multiple logistic regression analysis was performed to adjust for confounders of 28-day and 1-year mortality. Out of 41,468 unique ICU admissions, 1,100 hemato-oncologic, 3,953 oncologic and 49 patients with both a hematological and solid malignancy were analyzed. Hematological patients had higher critical illness scores than non-cancer patients, while oncologic patients had similar APACHE-III and SOFA-scores compared to non-cancer patients. In the univariate analysis, cancer was strongly associated with mortality (OR= 2.74, 95%CI: 2.56, 2.94). Over the 10-year study period, 28-day mortality of cancer patients decreased by 30%. This trend persisted after adjustment for covariates, with cancer patients having significantly higher mortality (OR=2.63, 95%CI: 2.38, 2.88). Between 2002 and 2011, both the adjusted odds of 28-day mortality and the adjusted odds of 1-year mortality for cancer patients decreased by 6% (95%CI: 4%, 9%). Having cancer was the strongest single predictor of 1-year mortality in the multivariate model (OR=4.47, 95%CI: 4.11, 4.84).


A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN

arXiv.org Machine Learning

The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the extent to which predictive accuracy differs for Kriging and machine learning techniques using big data. Thus, this study compares the predictive accuracy of apartment rent price in Japan between the nearest neighbor Gaussian processes (NNGP) model, which enables application of Kriging to big data, and the deep neural network (DNN), a representative machine learning technique, with a particular focus on the data sample size (n = 10^4, 10^5, 10^6) and differences in predictive performance. Our analysis showed that, with an increase in sample size, the out-of-sample predictive accuracy of DNN approached that of NNGP and they were nearly equal on the order of n = 10^6. Furthermore, it is suggested that, for both higher and lower end properties whose rent price deviates from the median, DNN may have a higher predictive accuracy than that of NNGP.