AI Application in the COVID-19 Battle and Beyond

Across all media, we’ve been hearing about the massive strain the COVID-19 pandemic is having on our healthcare systems around the globe. Hospitals and healthcare facilities are overwhelmed with the sheer volume of patients needing care. The strain is also apparent in their ability to mitigate exposure to the virus and the exhaustion of healthcare staff. In an effort to manage and treat increasing numbers of contagious patients while limiting the possibility of spreading the virus, artificial intelligence (AI) is being deployed successfully in various ways around the world.

From supporting screening and triaging patients, to monitoring COVID-19 symptoms, to providing decision support for CT scans and automating hospital operational functions, AI is being deployed in the war against the pandemic. In China’s Wuhan Wuchang Hospital, for example, AI was used to establish a smart field hospital, which was staffed largely by robots that delivered food and medication to patients. The hospital monitored patients’ vital signs using connected thermometers and bracelet-like devices. Measures such as those help limit physician exposure to the virus and ease the workload of exhausted healthcare workers.1

And while AI is helping to optimize healthcare operations, digitizing as many steps as possible, it’s not replacing human clinical reasoning and decision making; rather, it’s being used as a decision aid to help improve efficiency, safety, and patient outcomes.

With AI’s ability to think like humans do—but at a larger scale and much faster without getting bored or tired—the possibilities for AI applications to aid us in critical decision making are endless.

All things considered, we’re just at the start of a technology that ultimately may be as transformative as MS-DOS or the cloud—with far-reaching implications that will impact our human way of life.

With AI’s ability to think like humans do—but at a larger scale and much faster without getting bored or tired—the possibilities for AI applications to aid us in critical decision making are endless.

For example, AI is already being used to tackle some of the world’s greatest economic and social challenges and many more mundane functions.

In agriculture, AI advancements show improvements in the growth of crops. In aviation, NASA’s Aircraft Operations Division (AOD) uses AI as surrogate operators for combat and training simulators. Robots in many industries are often assigned jobs considered too dangerous for humans. At the USC Center for Artificial Intelligence in Society, AI is being tapped to address socially relevant problems such as homelessness.

While those may be the types of initiatives that earn headlines, innumerable more routine applications are the ones that will likely change the business landscape as we know it and, in turn, how we do our jobs.

One of the areas where AI can and will have a significant impact is any business associated with data. We need look no further for examples than the insurance, financial services, and energy industries. Since the dawn of the Internet, the world has seen vast amounts of unstructured data. AI has the potential to create structured data and insights from this enormous pool of unstructured data.

AI in insurance

After years of promise, and some hype, AI applications are finally impacting the insurance industry. These changes have been fueled by an array of technologies, including image analytics and natural language understanding of text and speech. However, technologies and algorithms are not sufficient to create useful AI—relevant data and domain expertise are equally important. And with most in the industry more willing to capture and leverage unstructured data (text, image, speech, etc.), the stars are aligning for AI to leave its mark on insurance in many ways.

For example, technology developed for facial recognition in photographs is being tailored for insurance-specific applications. Image analysis algorithms can now reliably recognize car models, detect and quantify existing damage, identify vehicle parts, read license plates, parse documents, and more—all from photos and videos. Similar capabilities can automatically characterize a property’s structural damage from photos and drone aerial footage. For property losses, AI is being developed to help detect and recognize objects in a room, identify damage, and automatically find replacements on the Internet.

AI can also be used to counter the rise of user-supplied imagery that’s been manipulated with image-altering software possibly to hide preexisting damage (underwriting) or exaggerate damage (claims). Using algorithms for metadata verification and error/noise-level analysis, such modifications can often be detected. AI algorithms for image matching can now spot pictures downloaded from the Internet that are being passed off as genuine. These analytical capabilities have matured to the extent that photos that easily fool the human eye can be identified as fakes.

On the customer front, chatbots have been used for policy quoting and inquiry, but claim applications have been limited owing to the level of understanding, sensitivity, and empathy that chatbots must possess when interacting with policyholders who have suffered a loss. In recent years, substantial progress has been made in natural language understanding (NLU) and the development of increasingly empathetic chatbots. Beyond initiating a claim, these chatbots can ask open-ended questions and use AI to comprehend the answers. They can detect feelings and sentiments and tailor their responses to show empathy when someone is sad or appear calm when someone is upset. Coupled with AI advances in speech analysis and synthesis, these chatbots can also have voice conversations. This is a crucial development because complex situations can be communicated more easily through conversations rather than asking policyholders to type answers or explanations.

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AI in banking

Tackling a different set of challenges, the credit card industry in the United States is one of the most competitive. With a base of nearly 200 million consumers, companies are looking to AI as a tool in achieving competitive advantage.

For example, banks are continually introducing innovative offers and launching new products to target specific customers. In the past, this was typically done with scripts and programs seeking specific information, but AI has opened avenues once deemed unimaginable.

AI has also enabled a more accurate and better-informed assessment of potential customers with the added benefits of lower costs and faster turnaround times. A request for a credit card, for instance, can now get approved online any time of day, with AI processes and systems instantly predicting the riskiness of the potential customer in question. And based on consumer spend behavior trends, banks are also using AI to issue rewards and make travel-related offers.

Regarding detection and prevention, AI has been extremely effective by flagging transactions based on past customer behaviors, such as those atypical of the customer’s shopping habits or occurring in two different locations on the same day. With AI’s ability to learn over a period of time, an AI-built system can also record when a genuine transaction is tagged as fraud and approved by the consumer and make the correction for future reference.

Money laundering is another major challenge for financial institutions. The majority of traditional anti-money-laundering (AML) processes have been manual, resource-intensive, and inefficient in making an impact. While AI’s potential in combating AML is yet to be fully realized, businesses are increasingly using AI to enhance existing compliance and monitoring systems to better detect questionable scenarios and reduce false positives.

AI in the oil and gas industry

The oil and gas industry has always been on the cutting edge of technological innovation and leveraged big data (seismic imaging) before we called it “big data.” But advances in analytics, including AI and machine learning, are providing new ways of interpreting this data, yielding previously unknowable insights.

There are three main prizes for the energy industry to seize. First and foremost, health and safety: Automation, robotics, and drones are among the technologies that help ensure secure environments. Reduced accident and injury rates for the workforce will be an unequivocal boon for an industry that operates on geographical, geological, and engineering frontiers.

Second, a structural reduction in costs: Senior management in some companies sees digitalization as a means to cut the workforce by half, perhaps more. A measure of success will be increased production as headcount and costs fall. The wins in conventional development will likely be from improvements in project design and delivery—automated platforms with smaller, simpler topsides and automated drilling. Savings could also be recovered in the production phase by using advanced analytics on production data, smart production systems, remote working, predictive maintenance, 3D printing, and drone-based inspections.

Within the recently emerged unconventional resource plays (also known as shale gas, tight oil, and fracking), automation is among a number of factors that can speed up drilling and potentially save $5 billion annually. In the production phase, unconventional wells decline rapidly. Smart production management can be effective in helping reduce lease operating expenses and extending the economic life of mature shale wells, adding another $5 billion a year in savings.

The third prize is within the subsurface discipline, where geoscientists apply technical data, such as seismic, well log, and geological models, to find oil and gas. Cost savings of up to $7 billion annually can be achieved from faster drilling and processing of geological and geophysical work and fewer dry holes.

Potentially an even greater upside may stem from improved exploration and appraisal performance, perhaps derived through new understanding of well logs and chemical analysis or better processing of seismic data to identify and characterize subsurface structures that may contain hydrocarbons. Not only would this offer oil and gas exploration companies the bonanza of finding new resources in existing acreage, but anyone with a competitive advantage in exploration would have a strong advantage in areas of licensing or M&A.

While the ultimate goal is for machine learning and AI to be able to process data and spot hydrocarbon-bearing reservoirs with an almost perfect success rate, secondary benefits include making better, faster decisions on where and how to drill or saving time and money by opting not to drill at all. For an exploration-focused company, the improved speed at which it can make drill-or-drop decisions is transformational.

AI in the future

Tremendous progress has been made in AI research—from game-playing programs that perform better than human champions, to diverse algorithms for discovering complex patterns in data, to self-driving cars. With advances in computing capacity and storage and cost-effective consumption models like the cloud, real-world applications of AI have moved from academic domains into the mainstream. With data now available at enviable scales, depth, and rates, AI applications are poised to change our world.

  1. Tim Honyak, "What America can learn from China's use of robots and telemedicine to combat the coronavirus," March 18, 2020, CNBC, accessed on April 2, 2020.