With the recent advances in social media, the use of NLP techniques in social media data analysis has become an emerging research direction. Business organizations can particularly benefit from such an analysis of social media discourse, providing an external perspective on consumer behavior. Some of the NLP applications such as intent detection, sentiment classification, text summarization can help FinTech organizations to utilize the social media language data to find useful external insights and can be further utilized for downstream NLP tasks. Particularly, a summary which highlights the intents and sentiments of the users can be very useful for these organizations to get an external perspective. This external perspective can help organizations to better manage their products, offers, promotional campaigns, etc. However, certain challenges, such as a lack of labeled domain-specific datasets impede further exploration of these tasks in the FinTech domain. To overcome these challenges, we design an unsupervised phrase-based summary generation from social media data, using 'Action-Object' pairs (intent phrases). We evaluated the proposed method with other key-phrase based summary generation methods in the direction of contextual information of various Reddit discussion threads, available in the different summaries. We introduce certain "Context Metrics" such as the number of Unique words, Action-Object pairs, and Noun chunks to evaluate the contextual information retrieved from the source text in these phrase-based summaries. We demonstrate that our methods significantly outperform the baseline on these metrics, thus providing a qualitative and quantitative measure of their efficacy. Proposed framework has been leveraged as a web utility portal hosted within Amex.

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.

On the first day of the Association of Data Scientist's (ADaSci) Deep Learning DevCon 2021 (DLDC), Radhakrishnan G, Head- Global Commercial and Merchant Risk Decision Science at American Express (Amex), spoke about how his company helps small businesses with real-time credit decisioning using machine learning and artificial intelligence. Radhakrishnan is an alumnus of Management Development Institute, Gurugram. Throughout his almost two-decade-long ongoing stint at American Express, Radhakrishnan has been associated with risk management. His current role as the Head of Global Commercial and Merchant Risk Data Science and Risk Models across customer life cycle for card and non-card portfolios involves leading a team of more than 80 data and decision scientists across the globe. Radhakrishnan began his talk by introducing the audience by providing insights into the financial services company American Express.

Companies rely on real-world data to train artificial-intelligence models that can identify anomalies, make predictions and generate insights. To detect credit-card fraud, for example, researchers train AI models to look for specific patterns of known suspicious behavior, gleaned from troves of data. But unique, or rare, types of fraud are difficult to detect when there isn't enough data to support the algorithm's training. To get around that, companies are learning to fake it, building so-called synthetic data sets designed to augment training data. At American Express Co., machine-learning and data scientists have been experimenting with synthetic data for nearly two years in hopes of improving the company's AI-based fraud-detection models, said Dmitry Efimov, head of the company's Machine Learning Center of Excellence. The credit-card company uses an advanced form of AI to generate fake fraud patterns aimed at bolstering the real training data.

The ability to deploy data as a competitive business asset is what has distinguished a set of well-established, data-rich companies who have reigned as market leaders over the course of the past several decades. However, business conditions evolve, and today, these companies face a new set of challenges that threaten their hard-won leadership positions. How do these well-established data leaders transform from excellence in traditional data and analytics -- of the kind that they have deployed in recent decades -- to leadership in a new era of Big Data, AI, and machine learning driven decision-making? What do companies that have excelled at disciplines like database marketing, CRM, one-to-one marketing, and advanced analytics need to do to continue to stay on top? Data and technology are driving business change.

The second day of Transform, the annual digital event dedicated to applied enterprise AI, is another big one – VBLab and Accenture have partnered up to take you into the heart and soul of the digital age. On July 13, 2021, we'll take a virtual deep dive into data, analytics, and intelligent automation, from navigating the digital journey now to building a strategy for the future. We've gathered leaders from retail (Nordstrom, Nike, Doordash, Orangetheory), tech, (Google, Adobe, Zillow), healthcare (Cigna, Commonspirit Health; Dignity health/ Catholic health), finance (American Express, Creditkarma, American Fidelity) and more. These CXOs and BUs are gathering data across multiple sources, performing ETL (extract, transform, load), and storing it in the cloud or in a hybrid model in a data lake/data warehouse. They're finding innovative ways to enrich the data with crowd-sourcing or synthetic sources, cleaning and normalizing the data.

American Express (Amex) is a globally integrated payments company, providing customers with access to products, insights and experiences that enrich lives and build business success. And inside the company, the Amex Credit Fraud Risk business unit's mission is all about minimising credit and fraud losses while promoting business growth and delivering superior customer service. Nothing about this will surprise you so far, we're presuming. What may: while the financial services industry uses digital for just about every process imaginable, there's one surprising remaining exception-the commercial card underwriting process, which to you and me is'Are you going to lend my small business any money?' In a lot of Europe, this process is still completely manual and takes an underwriter a good chunk of time to complete.

Zeni, an AI-powered finance concierge for startups, today announced it has raised $13.5 million in a series A round led by Saama Capital. The company says this will bolster the launch of its new product, Zeni, an intelligent bookkeeping, accounting, and CFO service available to startups across the U.S. Studies show the vast majority of day-to-day accounting tasks can be automated with software. That may be why over 50% of respondents in a survey conducted by the Association of Chartered Certified Accountants said they anticipate the development of automated and intelligent systems will have a significant impact on accounting businesses over the next 30 years. Zeni, which was founded by twin brothers Swapnil Shinde and Snehal Shinde in 2019, combines AI with a team of finance experts to perform bookkeeping while managing finance functions -- including taxes, bill pay and invoicing, financial projections, budgeting, payroll administration, and more -- on behalf of customers. The Shinde brothers started Zeni after selling their last startup, Mezi, to American Express in 2018 for $125 million and the Indian music streaming service they cofounded, Dhingana, to Rdio in 2014.

Andrei Papancea, is the CEO at NLX a comprehensive SaaS platform for building and managing AI-powered conversational applications at scale. Previously, he built the Natural Language Understanding platform for American Express, processing millions of conversations across AmEx's main servicing channels. You grew up in Romania and started programming when you were 10 years old. What attracted you to programming at such a young age? It started off as curiosity: I've always been intrigued about how things worked and since my family has just gotten a computer, I wanted to figure out how it worked.

There are few bigger targets for cyber criminals than credit card companies. Which is why the U.S. alone had over 270,000 reports of credit card fraud in 2019, double the 2017 rate. So what's a credit card company to do? Use artificial intelligence to sniff out fraud and block it. "We believe at American Express that we have the world's largest and most advanced machine learning system in the financial services industry," American Express' VP of risk management Anjali Dewan told me recently on the TechFirst podcast. "And these models are ... monitoring 100% of these transactions and returning 8 billion credit and fraud risk decisions in real time."