Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their structures are known in an application. Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person. This prior work demonstrated significant recommendation performance improvements when various CARS-based methods have been deployed in numerous applications. However, some recommender systems applications deal with a much bigger and broader types of contexts, and manually identifying and capturing a few contextual variables is not sufficient in such cases. In this paper, we study such ``context-rich'' applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few most important contextual variables, although useful, is not sufficient. In our study, we focus on the application that recommends various banking products to commercial customers within the context of dialogues initiated by customer service representatives. In this application, we managed to identify over two hundred types of contextual variables. Sorting those variables by their importance forms the Long Tail of Context (LTC). In this paper, we empirically demonstrate that LTC matters and using all these contextual variables from the Long Tail leads to significant improvements in recommendation performance.

When credit card giant American Express began offering bank accounts for the first time last year, it had a foundation of fraud detection to bring to an entirely new product arena. That meant in some cases, the company could port over AI and machine-learning models used to spot phony identities or dodgy transactions for its credit card products to its consumer and business checking accounts. But it's been a process, and now, AmEx plans to invest in bringing additional AI techniques used to protect against credit card fraud to its banking products. "We have models which run to detect whether it's you or whether somebody else is logging into your account. Very straightforwardly, we transferred it to the banking product," said Abhinav Jain, vice president for Global Fraud Decision Science at AmEx, who is responsible for the company's fraud detection models.

We're looking for an experienced Data Analyst to join our team and create insights that drive the growth of our bank products. Nestled within the broader Merchant Bank Tribe, the Growth Team is responsible for ensuring that small businesses discover, choose and start using SumUp's banking products. Together we define strategies to increase the adoption and usage of SumUp's banking products, and validate these strategies through regular experimentation. If you're excited about giving small businesses access to the banking services they deserve, we'd love to hear from you. You'll be great for this position if We believe in the everyday hero.

Digital-first banks have acquired eminence in India today and the financial inclusion reforms are ready to roar. Take a look into the scenario of the banking ecosystem and its expeditious journey towards branchless banking in India through this article. Branchless banking has been outlined as the strategy that delivers banking services to people outside the traditional physical premises through kiosks, mobile phones, or other channels. Banks across the country have often been seen grappling with various strategies to transform the current banking system in tune with the ever-evolving digital age. The pandemic led many physical banks to the realization that the digital customer experience was substandard.

Finn AI, the world's leading AI-powered conversational banking technology provider, today announced that one of the world's largest financial services corporations has chosen Finn AI to help improve customer service and to enhance customer acquisition workflows. Under the terms of the engagement, Finn AI has developed a virtual assistant to pre-qualify prospects for the company's personal and small business banking products, ensuring human sales agents are engaged only when an inquiry is sales-based, thus reducing the cost to acquire. The virtual assistant also interacts with customers outside of regular call center hours, allowing the financial institution to extend their support hours to a 24/7 model. Additionally, the virtual assistant is used to expedite the product application process for new and existing customers, providing information about banking products and in-the-moment guidance, through to applying for a product. "This customer engagement is an excellent example of how Finn AI can help financial institutions achieve very specific, and often complex, business objectives," said Jake Tyler, CEO at Finn AI. "This particular use case leverages a number of our pre-built Customer Acquisition features including Smart Routing, Product Comparison, and Product Recommender. Used in combination, the customer is able to reduce distractions to sales agents while improving the consumer experience."

Here we look at Amazon's Machine Learning cloud service. In this first article we will look at logistic regression. In future blog posts we will see what other algorithms it offers. Remember that logistic regression is similar to linear regression. It looks at a series of independent variables and calculates one dependant variable.

Artificial intelligence (AI) is entering the mainstream at different paces for different industries. The insurance industry has so far outpaced the banking and asset management industries in terms of how frequently they use AI services to make important business decisions. While 54 percent of insurance companies are already using AI for these purposes, only 34 percent of banking institutions are doing the same. But banking's adoption of AI is projected to grow rapidly over the next few years. According to a survey from Narrative Science, 32 percent of financial institutions are using AI technologies for multiple banking purposes, and more than half of non-adopters plan to embrace AI by the end of 2018.

Varo Money, a fintech startup, is using AI (machine learning) along with mobile banking to improve customers' financial health. It recently raised $45 million from a private equity giant, Warburg Pincus and The Rise Fund, a global impact fund. The following are some of the features of Varo Money product which looks to have been created using machine learning algorithms/techniques. Given the fact that customers use mobile for all practical purposes and the information required for Varo Money AI products could easily be accessed from mobile device given the usage of mobile phones/tablets, it may not be difficult to comprehend why Varo Money is combining machine learning with mobile banking for offering their banking products to their customers. Did you find this article useful?

Capital and investments still hinge on financial institutions that are well known to Wall Street. In this digital age it is the adjacent industries who are adopting advanced technologies proactively. Outside the banking walls the world is becoming digitally intertwined. The fast flow of widespread information is transforming the way goods and services get transacted. Could this be the future of banking?

Extensive automation of business processes in international banking using artificial intelligence, robotics and machine learning, which I'm going to collectively call robotic process automation (RPA), is set to transform the way banks and financial services firms do business – particularly in areas such as trade finance and similar complex banking products. A wealth of data is generated day in, day out in the financing of international trade – from documentary credits to guarantees, loans and cross-border payments, and today there's still a huge amount of manual processing involved in managing the process. Clearly, as banks seek to reduce costs and increase efficiency, they're looking to automate as much as possible. Recent studies by Accenture have found that AI adoption could as much as double economic growth rates within the coming decades, with the potential to increase productivity by more than 50% compared to today's old fashioned and legacy IT systems. Robotic Process Automation (RPA) involves the creation of software robots that act as virtual workers who can be rapidly "trained" by business users in an intuitive manner.