Artificial intelligence can support risk management efforts

By Emily Newton
Companies face risks daily, but artificial intelligence can be a vital technology for curbing those threats, helping businesses take corrective action
Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine that discusses the latest innovations in science and technology.

A well-trained artificial intelligence (AI) algorithm can do an excellent job of spotting things that could put companies at risk, whether they’re fraudulent payments or disgruntled employees. AI works well for spotting strange patterns in large datasets. 

That’s why companies increasingly use AI as part of their cyber security defence strategies. Algorithms learn what constitutes normal network activity and issue alerts about deviations. 

Banks and other financial institutions also commonly use AI to mitigate the risks associated with extending credit, approving loan applications, and allowing large transactions on credit and debit cards. AI algorithms learn which factors could make a customer an above-average risk, then inform banking employees when necessary. 

Here’s a closer look at some specific benefits of using AI to optimise risk management efforts. 

Reduced Likelihood of Product Defects 

Defective products can cause a host of risks for a manufacturer. In the best cases, people catch the issues early in production or before the goods arrive on the market. Even then, the products cause wasted materials and time. Defects that only come to light in sold goods could harm the company’s reputation or even hurt customers. Frequent or widespread problems could attract regulatory scrutiny.

However, numerous companies use AI to manage risks associated with flawed goods. John Deere installed the technology to detect porosity in welded metal. People generally use sight and sound to find that issue, but it’s often easy to miss. The AI solution features a welding robot and a camera placed close to the weld. Algorithms examine a streaming feed of footage and automatically stop the robot after detecting a problem. Technicians can then take a closer look. 

A team at Fujitsu Laboratories created a similar solution that screens product images to find issues. They trained the AI algorithm on images of items with simulated problems, which was faster than using content from real defective products. One of the advantages of the innovation is that it can pick up various kinds of defects, such as frayed threads, wiring errors or scratches. 

The company tested this approach on printed circuit boards. It caused a 25% reduction in the number of worker hours needed to inspect products. Another impressive detail was that the system achieved 98% accuracy across various items. Not all companies have the resources to invest in AI and cut risks. However, these examples show why the option can be worthwhile when feasible. 

Fewer Worker Safety Breaches or Mistakes 

Most workplaces have rules for employees to follow for safety reasons. However, people may not follow those procedures for any number of reasons. Some might merely forget, while others may have missed the day of training that covered a rule’s specifics. Workers may also get fed up and purposefully decide not to follow the regulation to make a statement. However, these situations and others like them can expose workplaces to additional risks. 

A workplace’s risk management strategy will vary depending on what it needs to protect. That’s why risk and vulnerability assessments are critical parts of comprehensive security. The risk landscape is always evolving, and the measures that were necessary a few years ago may be irrelevant now. 

However, in specific industries, such as medicine and construction, it’s a given that employees in certain roles must don personal protective equipment (PPE) to protect themselves. Some AI solutions can spot instances when people don’t use PPE but should. 

One product on the market uses AI to analyse whether workers wear harnesses and tether them while performing tasks at heights. Researchers also developed a system that relies on AI to tell if health care workers wear their protective gear correctly. Michael Wilson, a professor and cardiothoracic surgeon at Macquarie University, created the product.

He explained, “The complex artificial intelligence behind the system immediately converts moving images from a webcam to mathematically represent someone putting a mask on their face, and then feeds the relevant information back. For example, ‘You need to put your goggles on next.’” While discussing this project, Wilson mentioned a study that identified more than 90 potential mistakes associated with using PPE. 

Minimised Supply Chain Shocks

Supply chain shortages can have devastating and long-lasting effects. Sony had to halt production of six digital cameras three times throughout November and December 2021 due to the ongoing chip shortage. Those halts caused a 4% drop in the product segment’s sales for December. It’s not always possible to predict supply chain strain, but AI can supplement the analyses humans make. One way it does this is by optimising demand planning.

A study found that people made 20% more demand planning errors during the COVID-19 pandemic than usual. However, when they used AI to help, forecasting mistakes decreased by more than 33%. Artificial intelligence can quickly analyse massive amounts of data, meaning it often picks up on things humans overlook. That characteristic makes it particularly valuable for risk management. 

AI can also help brand leaders detect instances where supply chain partners fail to uphold specific values. A manufacturer may forbid all its suppliers from engaging in child labor or say they must take particular sustainable actions. Any evidence that these things don’t occur could put a partner in violation of its contract and land the associated brand in hot water due to its links to the offender. 

In October 2020, Audi began using AI to detect possible supply chain risks among its network. The company analyses publicly available supplier news from media outlets and social feeds. The idea is to pick up on matters ranging from pollution to human rights violations. If Audi representatives know about those things early, they have more chances to act before parties try to cover up the issues. 

Supply chain risks are not always easy to pinpoint. They also vary depending on a company’s circumstances. A business that gets most or all of its supplies from other countries faces different risks from one that only uses local suppliers. Even so, AI can remove some of the difficulties of building or maintaining supply chains that stay strong despite occasional challenges. 

AI as Part of a Broader Risk Management Strategy

People should not rely on AI as their sole option for risk management. However, these examples prove why artificial intelligence can be a valuable part of an all-encompassing strategy that helps companies cut risks while boosting the likelihood of favourable outcomes.


Featured Articles

How Gen AI is Taking the FinTech Sector by Storm

Gen AI is taking the technological bounds of fintech into the future, helping fintechs become more efficient, but where exactly is it being implemented?

Preparing the Workforce for an AI-Native Future

AI Magazine speaks with Clyde Seepersad of the Linux Foundation about how businesses can create more sustainable, effective and responsible AI use cases

AWS Bedrock Gets Anthropic's New Claude 3.5 Sonnet Model

Amazon Bedrock and Anthropic upgrade their partnership to build Gen AI applications... with Gen AI assistance by adding Claude 3.5 Sonnet to copilot tasks

What Dell and Super Micro can Bring Musk’s xAI Supercomputer

Cloud & Infrastructure

Toshiba Takes Another Step to Ushering in Embodied AI


Why AWS is Investing $230m in Credits for Gen AI Startups

Cloud & Infrastructure