Event Title: Launch of the OECD AI Policy Observatory
Event Host: OECD
Applying AI to the real world is much more difficult than applying it in digital ecosystems; this is what makes robotics use-cases in business so much more difficult than applications such as AI-enabled fraud detection.
Large enterprises are eager to use artificial intelligence software, but many of them aren't aware of the hardware required to execute many AI capabilities. To get a better idea of these hardware considerations, Emerj spoke with Victoria Rege, Director of Alliances & Strategic Partnerships and Graphcore, for Kisaco Research's AI Hardware Summit in Europe, which takes place October 29 - 30 in Munich, Germany.
In banking and finance, chatbots have the potential to improve the customer experience by allowing customers to check their account balances, transfer money, learn about interest rates, change their billing addresses, and more.
This article is based on a presentation given by Emerj CEO Daniel Faggella in Geneva, at the 2019 New Shape Forum: Weapons Governance for the Geneva Disarmament Platform. To learn more about Emerj's AI presentations and speaking, visit our presentations page.
AI may have a role to play in digitizing the paper-heavy mortgage process, facilitating more streamlined search and discovery for entities across a variety of digital and scanned PDF documents. We spoke with Dan Cortright, Senior Director of Product Management at Iron Mountain, about just that. Courtright discusses how AI could help approve loans quicker, better assess risk, and allow employees to pull up documents they need to respond to customer requests.
Event Title: NEXT Technology Conference
Event Host: Syracuse University
Financial institutions have challenges around data accessibility. They want to leverage their large amounts of data so their employees, such as customer service agents, can find the information they need quickly.
Many of the key processes in industries such as banking and insurance are still done on paper. That said, many large enterprises seem to be in the process of digitizing parts of these processes in order to prepare for forays into automation and artificial intelligence.
Event Title: 2019 New Shape Forum: Weapons Governance
Date: September 30, 2019 - October 1, 2019
When it comes to process automation, digital transformation leaders are now navigating the artificial intelligence hype. Although AI can yield some impressive results when it comes to digitizing processes that still involve paper and reducing the time customer service agents spend searching for customer information, leaders are perhaps too excited to jump into AI without knowing the fundamentals of what it entails.
Event Title: PayThink 2019
Date: September 18 - 20
Presentation Title: Artificial Intelligence and the Future of Payments - Critical Use-Cases and Trends
Event Title: Countering Terrorism Through Innovative Approaches and the Use of New and Emerging Technologies
Robotic process automation, or RPA, has dominated much of the automation conversation in the insurance industry for several years. RPA is able to capture manual steps that employees take to log into software, search documents, and enter data and replicate them.
We set out to create a report that would be particularly useful for executives and business leaders who are looking to get started with an AI initiative, as well as IT and management consultants who want to competently and effectively guide their clients through AI adoption.
Event Title: INTERPOL World 2019
Event Host: INTERPOL
Date: July 2 - 4, 2019
Ten years into the longest economic expansion on record, auto lenders are looking for ways to leverage new opportunities for growth and risk reduction.
We interviewed Jay Budzik, CTO at Zest AI, about the business value of machine learning for auto lending. We speak with Budzik about how underwriting, lending, and credit scoring is evolving as a result of advances in machine learning - both in terms of new data sources, and more advanced algorithms.
Oil and gas companies face many of the same challenges as large banks and established insurance firms when it comes to searching through their backlogs of documents. They want to use the data stored within these documents to make decisions on where to drill and determine whether or not they’re in compliance with laws and regulations.
Artificial intelligence is transforming a variety of banking functions and allowing tech startups to compete with some of the largest banks for market share of key services, including lending and wealth management. Business news and media sites have been heralding the downfall of the banking industry as we know it because fintech companies are going to feel comfortable leveraging AI long before banks.
Customer data is essential for insurance firms to stay competitive in the coming decade. Insurance companies at present have backlogs of data on past and existing customers in the form of policy agreements, applications, and claims forms. They’ve also collected millions of images showing car damage, property damage, and personal injuries.
Large banks deal with millions of documents every day across their corporate offices and numerous branches. Although one might assume that these documents are digital, in many cases, even the largest banks store old physical documents in file cabinets and boxes off the bank’s premises, and even those that are kept on-site might be relegated to storage units amongst hundreds of thousands of other documents.
Machine learning has far-ranging applications in the finance space broadly from document digitization to document search, chatbots to fraud detection. The insurance space in particular, however, stands to benefit from AI and machine learning applications in a few unique ways. They could help insurance firms with a challenge that’s at the forefront of the insurance world: attracting and meeting the needs of millennial customers.
In the last year, interest in so-called “autoML” has risen greatly in part due to its promise of bringing artificial intelligence to businesses that have been blocked from accessing it due to its serious time, talent, and budget requirements. Although machine learning may still be widely unavailable to small businesses, medium-sized businesses may find that autoML allows them to make use of it in the coming years.
The finance sector has proven itself an early adopter of AI in comparison to other industries. As such, the applications of artificial intelligence and machine learning in finance are myriad. Traders, wealth managers, insurers, and bankers are likely well aware of this in some form.
At Emerj, we pride ourselves on presenting objective information about the applications of artificial intelligence in industry. AI is applicable in a wide variety of areas—everything from agriculture to cybersecurity.
Episode Summary: This week we talk to Alejandro Giacometti, the data science lead at a company called EDITED, based in London. The company claims to help retailers with inventory optimization, and we speak with Giacometti about how artificial intelligence can be used to search the web for the product clusters and individual products of major retailers to help inform other retailers on which products might be popular.
Data is essential when it comes to building machine learning models for business applications. A strong AI strategy is predicated on data that is specific to the business problem a company is trying to solve, as outlined in our recent article: Data Collection and Enhancement Strategies for AI Initiatives in Business. When it comes to executing on that strategy, oftentimes the first thing a company needs to do is collect some or all of the data it will need to build the right machine learning model for its use case.
Episode Summary: What makes chatbots or a conversational interface actually work? What kind of work does one need to do to get a chatbot to do what one wants it to do? These are pivotal questions and questions that for most business leaders are still somewhat mysterious, but that's exactly what we're aiming to answer on this episode of the AI in Industry Podcast.