Despite investing enormous amounts in people resources to prevent money laundering and terrorist financing, and comply with regulations, banks have paid approximately $320 billion in fines over the last ten years alone. Cue artificial intelligence (AI) and machine learning, the latest technologies promising financial institutions a way to outflank criminals in the world of digital finance. While people versus machines arguments grab headlines, the most successful banks will use a combination of humans and AI to prevent fraud. I listened to some interesting fintech scenarios during the launch of the SAP Next-Gen Innovation Community for Financial Services at the SAP Leonardo Center in New York City, and one of the most impressive was from Surendra Reddy, Founder and CEO of Quantiply. The California-based startup is infusing AI into its software solutions to help banks address financial crime, risk and compliance.

Telstra is set to inject artificial intelligence and machine learning capabilities into both customer and agent facing systems. The telco is in the process of appointing internal product owners to oversee "applied" artificial intelligence and machine learning projects respectively. It is intended that the new "delivery-focused" central resources will sit with project teams out in the business "throughout the entire project lifecycle from idea through development and delivery into production". They will then have responsibility for expanding the AI or machine learning capability of that system, creating and "grooming" a new feature "backlog" to prioritise features that can deliver the most value. The telco did not go into specifics for how and where it intends to extract value using AI and machine learning algorithms first. However, it stated an interested in pursuing projects that involved some form of natural language processing, rule-based systems, and "automated inference and decisioning" technology, which could help it – for example – predictively react to customer preferences.

Despite investing enormous amounts in people resources to prevent money laundering and terrorist financing and comply with regulations, banks have paid approximately $320 billion in fines over the last ten years alone. Cue artificial intelligence (AI) and machine learning, the latest technologies promising financial institutions a way to outflank criminals in the world of digital finance. I listened to some interesting fintech scenarios during the launch of the SAP Next-Gen Innovation Community for Financial Services at the SAP Leonardo Center in New York City, and one of the most impressive was from Surendra Reddy, Founder and CEO of Quantiply. The California-based startup is infusing AI into its software to help banks address financial crime, risk and compliance. "Working with SAP, can we bring machine learning and AI to augment investigators so they can proactively stop activities before anything happens. Unlike humans, machines can see patterns in minutes not hours, and they never sleep," said Reddy.

One thing that makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network--activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems. Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job.

This whole topic – where (and how) should ML and rule-based verification meet – has been on my mind for a while, but I still don't have good answers. I do think it deserves significant attention from researchers and practitioners. The next three chapters will discuss why I expect ML to keep growing in dynamic verification, why there will always be an unavoidable, irreducible non-ML part, and some ideas about connecting the two. Finally, the last chapter will talk about rules in ML-based systems, explainable AI and all that. If you are not into verification, just go directly there. Please take a quick look at my Dynamic verification in one picture post.

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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.