Bank of America Merrill Lynch has launched a new fintech solution that brings together artificial intelligence, machine learning and optical character recognition to help companies match incoming payments with invoices. Under the name Intelligent Receivables, the solution is developed by HighRadius, a US-based fintech company. It is targeted at large or complex companies where the remittance information is either missing or received separately from the payment, which according to the bank is a source of big frustration to its clients. Using AI and other new technologies, Intelligent Receivables can help these companies improve their straight through reconciliation (STR) of incoming payments and post their receivables faster. It does so in four steps: first, the solution identifies payers and associates their payments to remittances that are received separately. Third, it uses this enriched remittance data to match payments to open receivables.

This story was delivered to BI Intelligence "Fintech Briefing" subscribers. To learn more and subscribe, please click here. Bank of America Merrill Lynch (BAML) has revealed that it is implementing enterprise software fintech HighRadius' artificial intelligence (AI) solution to speed up receivables reconciliation for the bank's large business clients. Large companies with numerous customers often receive payments without accompanying contextual information, like which customer or debtor it's come from, or precisely what the payment is for, which makes balancing a company's books, i.e. reconciling, a lengthy and resource-intensive task. HighRadius' solution uses AI, machine learning, and optical character recognition to identify a payer, match them to an uncontextualized payment, and match that to an open receivable.

You may have heard about machine learning from interesting applications like spam filtering, optical character recognition, and computer vision. Getting started with machine learning is long process that involves going through several resources. There are books for newbies, academic papers, guided exercises, and standalone projects. It's easy to lose track of what you need to learn among all these options. So in today's post, I'll list seven steps (and 50 resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.

Trade finance giants HSBC is working with IBM to develop a cognitive intelligence solution combining optical character recognition with advanced robotics to make global trade safer and more efficient for thousands of businesses. HSBC's Global Trade and Receivables Finance (GTRF) team facilitates over $500bn of documentary trade for customers every year, and in doing so must manually review and process up to 100m pages of documents, ranging from invoices to packing lists and insurance certificates. Newsweek is hosting an AI and Data Science in Capital Markets conference on December 6-7 in New York. The new solution uses IBM's analytics technology, including intelligent segmentation and text analytics, to identify, digitise and extract key data within these documents before feeding it into the bank's transaction processing systems; boosting accuracy whilst freeing up staff for more value-adding activities, said a statement. Natalie Blyth, HSBC's Global Head of GTRF, said: "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed. By digitising this process we will make transactions quicker and safer for both buyers and suppliers, leading our industry forwards, and we will reduce compliance risks through an enhanced ability to manage huge volumes of data."

Bank of America Merrill Lynch (BAML) is launching a new solution – intelligent receivables – that uses artificial intelligence (AI) and other software to help companies "vastly improve" their straight-through reconciliation (STR) of incoming payments to help them post their receivables faster, reports Banking Technology's sister publication Paybefore. Intelligent receivables is designed for large or complex companies that are seeking to reduce costs, decrease days-sales-outstanding, and improve cash forecasting and their end-customer experience, the bank says. The service is "ideally suited" for companies that manage a large volume of payments where the remittance information is either missing or received separately from the payment. Incomplete remittance information typically leads to an arduous and costly reconciliation process, says Rodney Gardner, head of global receivables in global transaction services at BAML. "Our solution brings together AI, machine learning and optical character recognition, setting a new bar in accounts receivable reconciliation and payment matching," adds Gardner. Intelligent receivables is currently available in the US and Canada.

When Manish Bhatia began working on Amazon Mechanical Turk as a side gig in 2010, he was surprised to find himself completely fascinated by the work. Contrary to frequent coverage depicting the piece-work platform as a digital sweatshop offering low-skill tasks, he thought the microtasks were intellectually stimulating. Many involved training machine-learning algorithms to do things like make purchasing recommendations based on past behavior or categorize content by genre; Bhatia enjoyed thinking of himself as the "AI behind the AI" and knowing that he was doing something to shape the future. The only problem was that he wasn't getting paid. Miranda Katz is an associate editor at Backchannel.

Bangalore: HSBC, the world's leading trade finance bank, is working with IBM to develop a cognitive intelligence solution combining optical character recognition with advanced robotics to make global trade safer and more efficient for thousands of businesses. HSBC's Global Trade and Receivables Finance (GTRF) team facilitates over $500 billion of documentary trade for customers every year, and in doing so must manually review and process up to 100 million pages of documents, ranging from invoices to packing lists and insurance certificates. The new solution uses IBM's advanced analytics technology, including intelligent segmentation and text analytics, to identify, digitise and extract key data within these documents before feeding it into the bank's transaction processing systems; boosting accuracy whilst freeing up staff for more value-adding activities. "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed," said Natalie Blyth, HSBC's Global Head of GTRF. "By digitising this process we will make transactions quicker and safer for both buyers and suppliers, leading our industry forwards, and we will reduce compliance risks through an enhanced ability to manage huge volumes of data."

U.S. Postal Service (USPS) crates sit on the floor at the Brookland Post Office in Washington, D.C., U.S. No customer data was stolen in a recent data breach, USPS officials say. WASHINGTON -- The U.S. Postal Service is warning that it will likely default on up to $6.9 billion in payments for future retiree health benefits for the fifth straight year. It is citing a coming cash crunch that could disrupt day-to-day mail delivery. The post office says it expects cash balances to run low by October. Postmaster General Megan Brennan stressed an urgent need for federal regulators to grant the Postal Service wide freedom to increase stamp prices to cover costs.

Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

When thinking about practical applications for artificial intelligence in your business, it's easy to assume that you need vast amounts of data to get started. AI is fueled by data, and so it only makes sense that the more data you have, the smarter your AI gets, right? When it comes to extracting intelligence by applying AI to data, context matters. In other words, you can build the biggest data lake imaginable, but if you don't know what you're trying to find and you don't have the right data to do it, then you're not going to get where you want to go. That's because AI is not some magical black box that can ingest mountains of data and then just spit out results.