Banks can now tap IBM Watson to fight financial crime

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Who will be the first to implement the new suite of Watson services? From the newly formed Watson Financial Services division, IBM has released the first suite of services covering regulatory requirements, financial crime insights, and financial risk modelling. These cognitive tools have been made possible following IBM's 2016 acquisition of global consulting operation, Promontory Financial Group. Promontory was originally working to provide support to banks dealing with the growing and tightening regulation and risk management within the financial services. It was the knowledge and expertise accessed in this acquisition that brought life to the new financial services-focussed Watson services, with regulation and risk accounting for two thirds of the suite, and a financial crime tool completing the set.


Five contributions of artificial intelligence the financial sector

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The traditional banking business is undergoing an exciting period of disruption. Big data, blockchain, an eruption of new competitors of all shapes and sizes … With so much that is new, we run the risk of overlooking how artificial intelligence is already beginning to change the core of the financial business. Its impact is clearly manifest in five areas: Credit scoring (or creditworthiness or rating), market research, personal assistants, asset management, and fraud detection. Startups like Kensho, recently acquired for $550 million, and Dataminr use artificial intelligence algorithms to improve the management of financial assets. Dataminr is specifically focused on identifying patterns and indexes via social networks, whereas Kensho stands out for its ability to establish correlations between news – from Brexit to natural catastrophes – and the markets.



Using Neural Networks for Credit Card Fraud Detection

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This walks you through how to build a Neural Network model for Kaggle's IEEE CIS Credit Card Fraud Competition. It discusses data preprocessing, learning lookup embeddings for categorical columns, visualizing full transaction embeddings, and ultimately ensembling this model with tree-based models to get an improvement in Leaderboard position.


Artificial intelligence powers digital medicine

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While this reality has become more tangible in recent years through consumer technology, such as Amazon's Alexa or Apple's Siri, the applications of AI software are already widespread, ranging from credit card fraud detection at VISA to payload scheduling operations at NASA to insider trading surveillance on the NASDAQ. Broadly defined as the imitation of human cognition by a machine, recent interest in AI has been driven by advances in machine learning, in which computer algorithms learn from data without human direction.1 Most sophisticated processes that involve some form of prediction generated from a large data set use this type of AI, including image recognition, web-search, speech-to-text language processing, and e-commerce product recommendations.2 AI is increasingly incorporated into devices that consumers keep with them at all times, such as smartphones, and powers consumer technologies on the horizon, such as self-driving cars. And there is anticipation that these advances will continue to accelerate: a recent survey of leading AI researchers predicted that, within the next 10 years, AI will outperform humans in transcribing speech, translating languages, and driving a truck.3