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.
- 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.