Globalisation has increased the length and complexity of supply chains around the world. This is just one of the so-called complex and connected risks that will benefit from the advent of artificial intelligence, according to Brendan Plessis from Sompo International.
For the insurance industry, the challenge is to ensure we remain a key player in the management and mitigation of complex and connected risks. That means investing in our own AI-based technologies and ensuring we do not fall victim to the global skills shortage.
In an era defined by rapid technological advancements, the integration of artificial intelligence (AI) into aspects of our working and private lives has been nothing short of revolutionary. A topic once confined to the realms of science-fiction (and more appropriately should be called “Applied Statistics” if you follow Ted Cheung), as now become a tangible and highly disruptive influence on the world. And while AI’s impact to date has been arguably marginal, its potential is huge to the point at which some commentators predict it could change our relationship with work and the workplace fundamentally. This leads us to the question of how AI will impact on what we term complex and connected risks.
Risk management is one of the domains that will be profoundly impacted by this transformation. Already, AI systems are being mooted that could monitor a construction site to identify workers failing to wear a hard hat or observe passengers on a railway platform, watching for behaviours that might indicate a person is preparing to commit suicide by throwing themselves onto the tracks. With such powerful capabilities, it seems logical that the use of AI will reshape our thinking in areas of complex and connected risks such as international supply chains and cloud computing services.
Evolution of the connected risk landscape
Today, our world is smaller and more connected than ever before. The modern risk landscape is characterised by its multifaceted nature, in which traditional physical risks intertwine with novel challenges and intangible assets such as brand and reputation.
Complex risks, often stemming from intricate market dynamics or regulatory changes, require a comprehensive approach that goes beyond conventional risk management. Additionally, the proliferation of interconnected systems has given rise to connected risks, where the ripple effects of an incident can cascade across industries and continents. We saw, for example, how the pandemic impacted on the production of semi-conductors in Asia creating long lead times for new cars and video game consoles among others.
How do we think AI can give us a greater understanding of complex connected risks and allow us to manage them more effectively? AI’s prowess is in data analytics and pattern recognition, which is well suited to analysing these sprawling trade networks. By sifting through vast data sets, AI algorithms can identify subtle correlations and anomalous patterns that might elude human observers, which in turn will empower organisations to proactively address complex and connected risks before they escalate into crises. Having said that AI can never have true consciousness as a human would (cogito, ergo sum): believing so creates the hallucination effect.
Supply chain strategies
AI’s ability to radically improve supply chains is just beginning to be understood. Through predictive analytics, AI can optimise demand forecasting and inventory management, thus reducing stock-outs and excess inventory. Real-time tracking powered by AI and IoT devices will ensure visibility across the supply chain, enabling prompt responses to disruptions. Route optimisation algorithms leveraging traffic and weather data will minimise transportation costs and delivery times. Supplier performance evaluation and risk management will benefit from AI analysis, helping in the selection and mitigation strategies. In warehouses, operations will be streamlined through AI-driven automation, improving picking, packing, and storage.
These are just some of the ways in which AI can improve the management of the supply chains themselves. In addition to this, there is also AI’s ability to provide risk management input.
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Predictive analytics and early warning systems
Emerging and interconnected risks often lack historical precedent, making their identification and management a significant challenge. AI’s predictive analytics capabilities, combined with real-time data feeds, enable the development of early warning systems. These systems continuously monitor indicators, detect anomalies, and issue alerts when potential risks are detected. As an example, it is now possible to use AI-powered tools that will seek the route with the lowest possible risk for a shipment of goods. New IT platforms like Risk Ledger allow cyber security teams to monitor a business’s suppliers for cyber risks that could impact on their own business.
Having the right skills is a necessity for any business. But since the pandemic triggered a wave of onshoring – moving manufacturing away from Asia back to Europe and the US – the shortage of skilled workers and higher wage bills have become an issue for businesses. The question is not whether ChatGPT or Bard will take someone’s job; instead, it’s becoming ‘Can ChatGPT/Bard fill a vacancy within an organisation?’ Recently, Forbes magazine noted that ‘AI tools might alleviate the shortage of skilled workers by automating routine tasks and augmenting human capabilities’. The magazine quoted cyber security as an example of this already happening. A global shortfall of 3m–4m workers has triggered a wave of AI-based automated threat detection and response.
In the face of the evolving risk landscape, AI emerges as a transformative force that will reshape how organisations perceive, assess, and mitigate risks. In the arena of complex and connected risks, such is the scale of the challenge, there will be no silver bullets in terms of AI’s use. Rather, the market will find numerous applications in which AI can be beneficial, effectively tackling the problem from many different routes.