In Asia Pacific (APAC), artificial intelligence (AI) and machine learning (ML) are increasingly being deployed in credit and risk functions for improved credit assessment, credit scoring and fraud detection. Moving forward, AI will no longer be an option for banks and financial institutions but rather a necessity, enabling them to meet rising customer expectations, tap new business opportunities, and address the rapidly evolving fraud landscape, data specialists and top finance executives said in a recent webinar. During Fintech Fireside Asia's latest panel discussion, C-level executives representing Union Bank of the Philippines, credit bureau TransUnion, lending startup Funding Societies and data solutions provider Mobilewalla discussed the state of AI adoption across APAC's financial ecosystem, delving into how predictive modeling and ML are now being used in the lending process. For Anindya Datta, Founder, CEO, Chairman, Mobilewalla, AI offers an opportunity to deliver innovative business models that can leapfrog traditional solutions and reach the unbanked, a potential that's particularly relevant in Southeast Asia considering that more than 70% of the region's adult population remain either unbanked or underbanked today. "A major part of decision making in lending is around figuring out how likely a person is going to pay back and whether they will pay back in time. Why it's so interesting In emerging markets, especially in APAC, is because the credit footprint is small [and a lot of people don't] have credit scores," Anindya said.

"We're seeing 10- to 12-week delivery times for laptops and computing devices," said Sue Workman, chief information officer at Case Western Reserve University in Cleveland. "Those used to take a day or two." For the rapidly approaching fall semester, the school is hustling to equip classrooms with video displays, microphones and other tools so students have the option of taking some classes from home, Ms. Workman said. Orders for both displays and microphones have been delayed, she added. The Morning Download delivers daily insights and news on business technology from the CIO Journal team.

Every week, Q.ai covers a different theme to introduce retail investors to different types of investing. These run the gamut from large and small cap stocks to particular sectors of the market to companies with solid value or growth potential. This week, we're going to take a look under the hood of some of the stocks in the Forbes AI Investor Industrial portfolio. Whether these stocks tinker, build, or drive commercial efforts behind the scenes, we're here to drag out the good, the bad, and the greasy for your edification. Q.ai runs factor models daily to get the most up-to-date reading on stocks and ETFs.

TransUnion, a global information and insights company, processes more than 1,000 transactions per second, and Chief Information and Technology Officer Abhi Dhar says that each and every one is important. TransUnion's business customers rely on it for instant access to information that allows them to make immediate financing decisions. And consumers need and deserve fast answers, Dhar says. That's why he's using machine learning to bolster TransUnion's IT uptime. Machine learning, a subset of artificial intelligence, allows the company's IT team to predict problems in advance, so they can proactively resolve issues before outages occur.

One in three councils are using computer algorithms to help make decisions about benefit claims and other welfare issues, despite evidence emerging that some of the systems are unreliable. Companies including the US credit-rating businesses Experian and TransUnion, as well as the outsourcing specialist Capita and Palantir, a data-mining firm co-founded by the Trump-supporting billionaire Peter Thiel, are selling machine-learning packages to local authorities that are under pressure to save money. A Guardian investigation has established that 140 councils out of 408 have now invested in the software contracts, which can run into millions of pounds, more than double the previous estimates. The systems are being deployed to provide automated guidance on benefit claims, prevent child abuse and allocate school places. But concerns have been raised about privacy and data security, the ability of council officials to understand how some of the systems work, and the difficulty for citizens in challenging automated decisions.