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Artificial Intelligence at American Express - Two Current Use Cases

#artificialintelligence

Ryan Owen holds an MBA from the University of South Carolina, and has rich experience in financial services, having worked with Liberty Mutual, Sun Life, and other financial firms. Ryan writes and edits AI industry trends and use-cases for Emerj's editorial and client content. American Express began as a freight forwarding company in the mid-19th century. Expanding over time to include financial products and travel services, American Express today reports some 114 million cards in force and $1.2 trillion in billed business worldwide. American Express trades on the NYSE with a market cap that exceeds $136 billion, as of November 2021.


Significance of FTC guidance on artificial intelligence in health care

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November 24, 2021 - The Federal Trade Commission has issued limited guidance in the area of artificial intelligence and machine learning (AI), but through its enforcement actions and press releases has made clear its view that AI may pose issues that run afoul of the FTC Act's prohibition against unfair and deceptive trade practices. In recent years it has pursued enforcement actions involving automated decision-making and results generated by computer algorithms and formulas, which are some common uses of AI in the financial sector but may also be relevant in other contexts such as health care. In FTC v. CompuCredit Corp., FTC Case No. 108-CV-1976 (2008), the FTC alleged that subprime credit marketer CompuCredit violated the FTC Act by deceptively failing to disclose that it used a behavioral scoring model to reduce consumers' credit limits. If cardholders used their credit cards for cash advances or to make payments at certain venues, such as bars, nightclubs and massage parlors, their credit limit might be reduced. The company, the FTC alleged, did not inform consumers that these purchases could reduce their credit limit, neither at the time they signed up nor at the time they reduced the credit limit.


The Impact of AI on the Finance Industry

#artificialintelligence

A race towards digitization is bringing a revolution in the Financial and FinTech sectors. At the core of this digitization lies the availability of a vast array of data (such as Big Data), advancements in affordable computing technologies, and the advent of intelligent technologies such as Machine Learning and Artificial Intelligence. AI has been around for nearly 70 years, its practicality and intelligence have increasing over time. Today, AI has become an integral part of the industrial landscape as well as the lives of common people. Examples of this can be seen in the voice assistants in smartphones, the use of AI robots in supply chain logistics, self-driving cars, movie recommendations on Netflix, and more.


Why Artificial Intelligence Will Solve All Our Money Problems

#artificialintelligence

Artificial intelligence is already pretty big in the finance industry, but it's going to get better within a few years. It will get to the point where it makes life easier for the average person on a daily basis. Things like paying off credit cards and applying for loans are still tough, but technology is coming on leaps and bounds. Let's take a look at some of the most popular ways AI will play a large part in our lives. When loan providers are unable to make smart lending decisions fewer people will be approved.


The Future of Banking To Be Driven by Artificial Intelligence; New Report States

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With dedicated regulation now emerging for fintechs and digital banks in some jurisdictions, it's a case of adapt or die for incumbent players. But banks have one asset on their side – data. With around a billion credit card transactions every day, banks have access to one of the largest volumes of customer data of any industry. Using AI, banks can harness this information to unlock unparalleled insights and growth. It's estimated that AI technologies could deliver up to $1 trillion of additional value each year for the global banking industry, combining a deep understanding of customer needs with the composable cloud architecture to roll out hyper-personalised services at scale.


Artificial intelligence to power the banks of the Future - Google

#artificialintelligence

With dedicated regulation now emerging for fintech and digital banks in some jurisdictions, it's a case of adapt or die for incumbent players. But banks have one asset on their side - data. With around a billion credit card transactions every day, banks have access to one of the most significant volumes of customer data of any industry. Using AI, banks can harness this information to unlock unparalleled insights and growth. McKinsey estimates that AI technologies could deliver up to $1 trillion of additional value each year for the global banking industry, combining a deep understanding of customer needs with the composable cloud architecture to roll out hyper-personalised services at scale.


Artificial intelligence in Finance

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Artificial intelligence has specified the world of banking and therefore the financial industry as a whole how to fulfill the stress of consumers who want smarter, more convenient, harmless ways to access, spend, save and invest their money. AI in finance is changing the way we relate to money. AI provides assistance to the financial industry to rationalize and optimize processes starting from credit decisions to measurable trading and financial risk management. A current study established 77% of consumers favored paying with a debit or MasterCard related to only 12% who favored cash. But easier payment options are not the only reason the supply of credit is vital to consumers.


Fair Sequential Selection Using Supervised Learning Models

arXiv.org Artificial Intelligence

We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the vacant positions are filled. In this paper, we discuss whether the fairness notions (e.g., equal opportunity, statistical parity, etc.) that are commonly used in classification problems are suitable for the sequential selection problems. In particular, we show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups. This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions. We introduce a new fairness notion, ``Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion. We also consider a setting where the applicants have privacy concerns, and the decision maker only has access to the noisy version of sensitive attributes. In this setting, we can show that the perfect ES fairness can still be attained under certain conditions.


Neural Additive Models: Interpretable Machine Learning with Neural Nets

arXiv.org Machine Learning

Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output. Our experiments on regression and classification datasets show that NAMs are more accurate than widely used intelligible models such as logistic regression and shallow decision trees. They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees. To demonstrate this, we show how NAMs can be used for multitask learning on synthetic data and on the COMPAS recidivism data due to their composability, and demonstrate that the differentiability of NAMs allows them to train more complex interpretable models for COVID-19.


How to request an invitation for the Black Card (formerly known as the Centurion Card from American Express)

ZDNet

Carrying the "Black Card," formally known as the Centurion Card from American Express, gives you special status. It's rare and broadcasts that you're part of a small group of individuals who have exclusive access to the invitation-only elite card. Its inaccessibility makes it sought after by credit card hackers and American Express loyalists who won't settle for the Platinum card. Now, Amex has loosened its grip on the Black Card ever so slightly. There's an air of mystery surrounding the card's perks, but what sets the card apart from others is the personal concierge service it offers cardholders.