Banking & Finance


AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


Shift to Artificial Intelligence Could Lead To World Conflicts

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Artificial intelligence is slowly changing the world, and not everyone is happy about it. As for China and the United States, Lee believes these countries will be the primary beneficiaries in the AI shift. With China's economy growing without an end in sight, and the United States economy in an uncertain situation, there's always the chance for the Asian giant to surpass its western rival in the next 10 or more years. Several fast food companies in the United States and other countries around the world are planning to replace their primary workforce with robots.


Hedge Funds Look to Machine Learning, Crowdsourcing for Competitive Advantage

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A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund. Many of the world's largest hedge funds already rely on powerful computing infrastructure and quantitative methods--whether that's high-frequency trading, incorporating machine learning, or applying data science--to make trades. Some have begun to incorporate machine learning into their systems, hand over key management decisions to troves of data scientists, and even crowdsource investment strategies. Domeyard can't incorporate machine learning, Qi says, because machine learning programs are generally optimized for throughput, rather than latency.


First Global Credit - Do A.I and Cryptocurrency work well together?

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We are speaking about the world-altering technology of Artificial Intelligence as the first superpower coupled with the financial system disruptive technology of cryptocurrency -- a decentralised payment system that circumvents government manipulation of currency and is forcing us to redefine the concept of money. Artificial Intelligence (AI) means software that after its initial programming continues to improve its performance based on experience of the environment it has been set to'learn.' So we return to the original question: "can a market as young and volatile as cryptocurrency be successfully partnered with Artificial Intelligence to produce a profitable outcome? This means, there is room while cryptocurrency markets are still in their infancy for AI developers to create systems that learn to identify profit opportunities in these young, highly volatile markets.


How Deep Learning Machines Program Themselves – Saad Hussain – Medium

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Finally, once we understand how machines learn and what kind of skills they develop, we will learn how deep learning machines program themselves. Essentially, the kid has learnt'the relationship' between'the objective' (higher accuracy of shooting the ball through the hoop) and'the relevant parameters' (trajectory, use of force, distance etc.) Suppose, a machine has to learn to predict the risk of credit card default ("The Objective") given a large set of historical data on credit card defaults including demographic information, past payment details, credit limits etc. The machine will learn the relationship between the input parameters and the resulting credit default and develop a skill to predict future default by developing a complex mathematical model.


Using Azure Data Lake and R for Fraud Detection

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The advantage of processing data using Azure Data Lake Analytics (ADLA) comes from the unique characteristics of U-SQL, a big data query language that combines SQL-like declarative benefits with the expressiveness and extensibility of C#. This document uses the example of online purchase transactions to demonstrate a basic 3-step process in fraud detection: feature engineering, training, and scoring. To evaluate the model, we can split the dataset into training and testing sets, train a model using the training set, and then evaluate the model's performance using metrics such as accuracy or AUC on the test set. With Azure Data Lake Analytics, AI engineers and data scientists can easily enable their machine learning solutions on petabyte-scale infrastructure instantly, without having to worry about cluster provision, management, etc., and the code can automatically be parallelized for the scale they need.


Artificial intelligence could add '£232BN to UK GDP by 2030'

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While we expect that the nature of jobs will change and that some will be susceptible to automation, our research shows that the boost to UK GDP that AI-driven products and services will bring will also generate significant offsetting job gains, as well as boosting average real wage levels." Automating the more mundane and repetitive aspects of people's jobs will also increase the UK's productivity and boost real wages." Euan Cameron, UK Artificial Intelligence leader at PwC, said "The potential size of the AI prize is huge and our research shows it has the potential to transform the productivity and GDP potential of the UK's economy. The potential GDP boost is equivalent to a total of around $10 trillion in these two regions, which account for around two-thirds of the estimated total global economic impact of around $15 trillion.


4 ways Artificial Intelligence is impacting the real estate industry - RealtyBizNews: Real Estate News

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The end goal of real estate agents never changes – they want to buy and sell homes for their clients, in the fastest time possible, for the best possible price. For example, there are listing platforms that can automatically match buyers to new listings within minutes of their being posted online. AI powered sales platforms are able to think and learn just like real-life agents, and that means they can also work out how to get the most money from each sale. Projected to massively improve agent productivity, platforms with artificial intelligence make an agent's life easier and their workflow more efficient --just push the button and watch the magic.


Banking Industry Fails to Meet Personalization Expectations

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In the financial services industry, one of the results of this digital transformation is a marketplace crowded with new fintech start-ups and, to a certain degree, major technology firms such as Google, Amazon, Facebook and Apple that are moving market share from legacy banking organizations. A research study from GfK shows there are ever increasing gaps between what consumers hope to receive from their bank or credit union – in terms of both level of service and financial advice – and what they actually receive. At a time when all financial services organizations are investing significant time and money in improving the digital customer experience, the research shows gaps in deliverables of between 13 to almost 40 percentage points. For instance, the research found that 38% of consumers were willing to pay more for a person who provides personal customer support, while only 1 in 5 were willing to give up personal service to save money (1/3 of customers earning over $200K would give up human interaction to save money).


China Auto Insurance Claims Adjusters Get AI Boost from Ant - Alizila

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The AI system is also aimed at making assessment of external vehicle damage more-standardized and objective, reducing the potential for human claims adjusters on the scene to be influenced by the parties involved in the accident. We are opening up our AI capabilities to our partners, so that they can reduce cost and better serve small and micro-businesses," said Yin Ming, president of Ant Financial's Insurance Business Unit. Ant is initially offering the AI damage-assessment system only to insurers, but plans to make the product available to car owners within a year. Ant said its technology is pushing out the borders of the financial services sector's technological infrastructure and making the sector more-accessible.