With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness. Credit scoring models are decision models that help lenders decide whether or not to accept a loan application based on the model's expectation of the applicant being capable or not of repaying the financial obligations [1]. Such models are beneficial since they reduce the time needed for the loan approval process, allow loan officers to concentrate on only a percentage of the applications, lead to cost savings, reduce human subjectivity and decrease default risk [2]. There has been a lot of research on this problem, with various Machine Learning (ML) and Artificial Intelligence (AI) techniques proposed. Such techniques might be exceptional in predictive power but are also known as black-box methods since they provide no explanations behind their decisions, making humans unable to interpret them [3]. Therefore, it is highly unlikely that any financial expert is ready to trust the predictions of a model without any sort of justification [4]. With regards to credit scoring, lenders will need to understand the model's predictions to ensure that decisions are made for the correct reasons.

Artificial Intelligence (AI) is a concept which has pervaded every area of business, offering new opportunities which were hitherto impossible. This is especially the case within financial trading in areas such as CFDs where speed and reduction of errors are absolutely vital. Of course, you can't merely slot AI in and expect it to do all the work. You'll still need to have a fundamental working knowledge of CFDs yourself. You can find detailed guides on CFD trading which will provide an excellent foundation for learning.

Fast-forward to the 1980s, when digital electronics started having a deep impact on society--the dawning Digital Revolution. Building on that era is what's called the Fourth Industrial Revolution. Like its predecessors, it is centered on technological advancements--this time it's artificial intelligence (AI), autonomous machines, and the internet of things--but now the focus is on how technology will affect society and humanity's ability to communicate and remain connected. "In the first Industrial Revolution, we replaced brawn with steam. In the second, we replaced steam with electricity, and in the third, we introduced computers," says Guido Jouret, chief digital officer for Swiss industrial corporation ABB.

There are many approaches to determining whether a particular transaction is fraudulent. From rule-based systems to machine learning models - each method tends to work best under certain conditions. Successful anti-fraud systems should reap the benefits of all the approaches and utilize them where they fit the problem best. The notion of networks and connection analysis in the world of anti-fraud systems is paramount since it helps uncover hidden characteristics of transactions that are not retrievable any other way. In this blog post, we will try to shed some light on the way networks are created and then used to detect fraudulent transactions.

At online retail platforms, it is crucial to actively detect risks of fraudulent transactions to improve our customer experience, minimize loss, and prevent unauthorized chargebacks. Traditional rule-based methods and simple feature-based models are either inefficient or brittle and uninterpretable. The graph structure that exists among the heterogeneous typed entities of the transaction logs is informative and difficult to fake. To utilize the heterogeneous graph relationships and enrich the explainability, we present xFraud, an explainable Fraud transaction prediction system. xFraud is composed of a predictor which learns expressive representations for malicious transaction detection from the heterogeneous transaction graph via a self-attentive heterogeneous graph neural network, and an explainer that generates meaningful and human understandable explanations from graphs to facilitate further process in business unit. In our experiments with xFraud on two real transaction networks with up to ten millions transactions, we are able to achieve an area under a curve (AUC) score that outperforms baseline models and graph embedding methods. In addition, we show how the explainer could benefit the understanding towards model predictions and enhance model trustworthiness for real-world fraud transaction cases.

Emerging technologies are'all the rage' in procurement today – especially for rules-based tasks that have to be quickly and reliably executed at scale. Transaction categorization is no exception from the trend. There are a number of ways to leverage technology for categorizing procurement activity, but they are not all created equal. Some employ top-down business rules'under the hood' while others leverage AI, and while the end result might appear the same at first glance, this could not be further from the truth. When taxonomy information is assigned to a transaction, the goal is to represent an accurate and actionable truth about the business activity.

Generally, business rules are modeled by domain experts and operations teams in decision automation scenarios. But what if you see a behaviour in your operational system from your customers, clients, etc. but you don't know the business rules? Business rule mining is the ability of a system allowing you to extract rules from data. In this process, you connect the data FlexRule Designer and it will analyze the data and extract the rules from it. FlexRule Designer will take it one step further and prepare and model a Decision Table and Fact Concept related to those rules automatically and will add them to the project.

One of the main weaknesses of current chatbots or dialogue systems is that they do not learn online during conversations after they are deployed. This is a major loss of opportunity. Clearly, each human user has a great deal of knowledge about the world that may be useful to others. If a chatbot can learn from their users during chatting, it will greatly expand its knowledge base and serve its users better. This paper proposes to build such a learning capability in a rule-based chatbot so that it can continuously acquire new knowledge in its chatting with users. This work is useful because many real-life deployed chatbots are rule-based.

Chinese state media with optimistic tone after Biden win. In a detailed policy document, President Trump's State Department is warning the incoming administration that it must address a Chinese government intent on displacing the United States as the world's foremost power. "The Trump administration achieved a fundamental break with the conventional wisdom. It concluded that the CCP's [Chinese Communist Party's] resolute conduct and self-professed goals require the United States and other countries to revise assumptions and develop a new strategic doctrine to address the primacy and magnitude of the China challenge," according to the document. The State Department's policy planning office, often called the department's "think tank," wrote the report as a long-term document to "sketch a framework for the fashioning of sturdy policies that stand above bureaucratic squabbles and interagency turf battles and transcend short-term election cycles."

How do AI models compare to rules-based decisioning? Brighterion shows how its AI models outperform and gives client metrics.