Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

A causal falling rule list (CFRL) is a sequence of if-then rules that specifies heterogeneous treatment effects, where (i) the order of rules determines the treatment effect subgroup a subject belongs to, and (ii) the treatment effect decreases monotonically down the list. A given CFRL parameterizes a hierarchical bayesian regression model in which the treatment effects are incorporated as parameters, and assumed constant within model-specific subgroups. We formulate the search for the CFRL best supported by the data as a Bayesian model selection problem, where we perform a search over the space of CFRL models, and approximate the evidence for a given CFRL model using standard variational techniques. We apply CFRL to a census wage dataset to identify subgroups of differing wage inequalities between men and women.

Automation and computer intelligence to support complex human decisions becomes essential to manage large and distributed systems in the Cloud and IoT era. Understanding the root cause of an observed symptom in a complex system has been a major problem for decades. As industry dives into the IoT world and the amount of data generated per year grows at an amazing speed, an important question is how to find appropriate mechanisms to determine root causes that can handle huge amounts of data or may provide valuable feedback in real-time. While many survey papers aim at summarizing the landscape of techniques for modelling system behavior and infering the root cause of a problem based in the resulting models, none of those focuses on analyzing how the different techniques in the literature fit growing requirements in terms of performance and scalability. In this survey, we provide a review of root-cause analysis, focusing on these particular aspects. We also provide guidance to choose the best root-cause analysis strategy depending on the requirements of a particular system and application.

We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines.

Imagine a world where robots ruled, where they could continuously improve their intelligence without human intervention or programming. It sounds like the future--but it's also our present reality. Artificial Intelligence (AI) isn't a new idea, though it's certainly developed a more forward-facing interface in recent years. In 2010, only $14.9 million was invested in the technology. But in 2014, a Bloomberg report showed that Venture Capitals had invested $309.2 million into AI startups for a 300% increase.

If you are not aware of the concepts of decision tree classifier, Please spend some time on the below articles, As you need to know how the Decision tree classifier works before you learning the working nature of the random forest algorithm. Given the training dataset with targets and features, the decision tree algorithm will come up with some set of rules. In decision tree algorithm calculating these nodes and forming the rules will happen using the information gain and gini index calculations. In random forest algorithm, Instead of using information gain or gini index for calculating the root node, the process of finding the root node and splitting the feature nodes will happen randomly.

The greatest challenge when talking about artificial intelligence/machine learning is actually in understanding what data sets we are looking at, and what model/combination of models to apply. Amazon's Machine Learning offering is one example of an automated process which analyses the data and automatically selects the best model to use in the scenario. Other big players who have similar offerings are IBM Watson, Google and Microsoft. Provenir's clients are continually looking at new and innovative ways to improve their risk decisioning. Traditional banks offering consumer, SME and commercial loans and credit, auto lenders, payment providers and fintech companies are using Provenir technology to help them make faster and better decisions about potential fraud. Integrating artificial intelligence/machine learning capabilities into the risk decisioning process can increase the organization's ability to accurately assess the level of risk in order to detect and prevent fraud. Provenir provides model integration adaptors for machine learning models, including Amazon Machine Learning (AML) that can automatically listen for and label business-defined events, calculate attributes and update machine learning models. By combining Provenir technology with machine learning, organizations can increase both the efficiency and predictive accuracy of their risk decisioning.

The Darren Aronofsky film Pi features a mathematician with the uncanny ability to perform complex arithmetic in his head. Notwithstanding his talent, he uses a computer to make stock predictions. In one scene, after recognizing a predictive pattern in a 216-digit output, the character becomes so overwhelmed that he passes out. This scenario is science fiction, but computers have long had greater processing power than humans. When used to build artificial intelligence (AI), as its datasets grow, so does a computer's advantage.

Only recently Seneca's chief investment officer Peter Elston hit the headlines when he poised the question; is a computer going to steal my job as fund manager? Elston pointed to three famous examples in modern history when a computer beat the world's best player in three games; chess in 1996, the Chinese board game'Go' in October 2015 and poker in January this year. His natural question to follow was how long will it be before computers are beating the world's best fund managers? While algorithm trading and quantitative strategies have been around for a numbers of years, the announcement of a completely new AI managed fund, will have done little to appease Elston's fears. Fund management group Sanlam Global Investment Solutions (SGIS) this week announced that its Sanlam Managed Risk Ucits fund is now managed solely by AI, which it claims makes it the first fund in the world that is solely driven by AI.

Whilst most businesses don't earn revenue by processing data, they do spend a large amount of their hard earned revenue in manually processing data, validating it and ultimately performing manual tasks that don't scale. But at what point does this manual involvement become a burden of cost upon your business? And really how much manual involvement should be required? Take payment fraud for example. According to the 2015 Merchant Risk Council (MRC) Global Fraud Survey, merchants typically manually review 10-15% of online orders.