The future of operational risk management: why businesses need to embrace new tech
Robert Rutherford, CEO of IT consultancy QuoStar, discusses the benefits of a technological approach to risk management.
The impact of technology on the business world is pervasive and constantly evolving, which has meant that companies must take action in order to stay both competitive and secure. However, with so many complex products and solutions on offer, it can be difficult for businesses to know where to begin.
For some companies, it may be tempting to prioritise client-facing technologies that promise immediate and measurable commercial benefits, but businesses also need to think about long-term transformations in key areas, such as operational risk management (ORM).
The impact of poor risk management can be devastating for firms not only financially, but also reputationally, yet some businesses seem willing to take chances in this area. Failing to update ORM processes and systems will leave these businesses vulnerable to increasingly sophisticated cyber threats, data breaches and fraud. Investing in new technology for ORM is therefore more than just common sense – it’s essential.
Automated detectives: anticipating risks
Identifying areas of vulnerability from vast swathes of data is definitely not a one-man job. A report by McKinsey notes that around 50% of financial services staff are currently dedicated to risk-related work, while just 15% are focused on analytics. Although, by 2025, it anticipates these figures will be closer to 25% and 40%, respectively. The integration of AI and data analytics systems in ORM will be responsible for this reversal.
In today’s world, data rules the roost, sparking a wave of advanced analytics tools that will become more valuable as more data is shared. Predictive analytics techniques, machine learning, and artificial intelligence can all help to efficiently build large and complex data sets. Working at a faster pace than any human, these solutions can be used to identify discrepancies long before they cause any serious problems.
While AI’s capacity for a rational, proactive response is still in the very early stages of development, organisations can already use real-time risk data to advance decision-making by establishing a framework that uses automated processes. For example, banks can now invest in robotic process automation (RPA) bots that will continuously scan their internal environment and collect data from predetermined sources. As a result of developments like these, time-consuming and costly manual auditing methods will eventually become a thing of the past.
Risk strategy is a team game
Breakthroughs in data analytics also mean that machines can now process data faster, more efficiently and without any bias. As such, it’s important for risk managers to see this technology as a tool to be exploited and leveraged, rather than as a threat. To this end, all areas of the business need to understand its capabilities in order to build a proactive working relationship with these solutions.
While defending against risks like cyber-attacks is vitally important, many at board and executive level are still unsure how to tackle this issue. This is alarming considering that 69% of financial services CEOs report they are concerned about cyberthreats, according to a 2016 survey by PwC.
Those responsible for risk management strategies can often find that there is a knowledge gap between them and the board-level decision makers, as executives tend to rely on external consultants for answers. However, it’s the board that will ultimately be held accountable for any failings, so effective communication between risk managers and decision-makers is essential.
Business leaders should therefore focus on creating a culture that not only prioritises risk management, but also one that encourages employees at all levels to engage with the systems they use. This top-down approach is the only way to ensure that everyone is properly prepared for the inevitable shift in ORM’s technological architecture and able to mitigate and manage the operational risks of the future.
Some businesses will struggle with what can potentially be a significant change to the way they operate, so shouldn’t be afraid to seek expert help on how to manage this transition. Failure to address risk would be a serious error, but mitigating risks in the wrong way can be equally as damaging.
Cause for concern?
Future proofing with technology like data analytics and AI shouldn’t make employees worry about job security. A company using new technology to manage risk will see a reduction in operating and auditing costs, an optimisation of its insurance coverage, as well as an increase in staff satisfaction. By introducing tools that are capable of automating manual processes, businesses will find that employees have more time to optimise their output and reconsider their relationship to ORM.
Without a doubt, the switch from human to algorithm-based risk assessments will present new challenges, some of which may be difficult to anticipate. This is simply the nature of change. What we do know is that developing a robust ORM strategy using new technology leads to more proactive and informed decisions, giving businesses the competitive edge necessary to grow in today’s marketplace. The field may be complex, however, there isn’t a better time to take ORM seriously and invest in the future.
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Dr Peng Wei: Designing the Future of Autonomous Aircraft
Air traffic is expected to double by 2037. According to the International Air Transport Association (IATA), the world will need 37,000+ new passenger and freight aircraft, and more than half a million new pilots—unless we come up with another solution. Right now, a George Washington University School of Engineering and Applied Science professor, Dr Peng Wei, is starting to research autonomous electric aircraft design.
NASA will fund the research, which will study how to minimise risks for electric vertical take-off and landing (eVTOL). As Airbus states: ‘Autonomous technologies also have the potential to improve air traffic management, enhance sustainability performance and further improve aircraft safety’.
Who is Dr Wei?
An assistant professor of Mechanical and Aerospace Engineering, Dr Wei has researched aircraft control, optimisation, and AI and ML applications in aviation. Over the next three years, he’ll lead the US$2.5mn NASA grant project in collaboration with researchers from Vanderbilt, the University of Texas at Austin, and MIT’s Lincoln Lab.
Why is His Research Important?
Even though the wide adoption of self-piloting cars, much less aircraft, is still far down the road, technologies that Dr Wei and his colleagues are researching will form the commercial transport of the future. But aviation manufacturers, in order to produce autonomous aircraft, will have to meet extremely high safety standards.
‘The key challenge for self-piloting capabilities is how the system reacts to unforeseen events’, said Arne Stoschek, Wayfinder Project Executive at Acubed. ‘That’s the big jump from automated to autonomous’. In the air, AI-piloted aircraft will have to manoeuvre around adverse weather conditions, such as wind and storms, and other high-altitude risks, such as GPS hacking, cyberattacks, and aircraft degradation. And the stakes are high.
‘If a machine learning algorithm makes a mistake in Facebook, TikTok, Netflix —that doesn't matter too much because I was just recommended a video or movie I don't like’, Dr Wei said. ‘But if a machine learning algorithm mistake happens in a safety-critical application, such as aviation or in autonomous driving, people may have accidents. There may be fatal results’.
What Are His Other Projects?
In addition to the new NASA research, Dr Wei has been awarded three other grants to pursue AI-piloted aircraft:
- A 2-year grant from the Federal Aviation Administration (FAA) in conjunction with West Virginia University and Honeywell Aerospace to investigate “learning-based” aviation systems
- A six-month SBIR Phase I NASA award with Intelligent Automation to mitigate airspace congestion at vertiports—the electric craft version of airports.
- A 1-year collaborative grant with the University of Virginia and George Mason University from the Virginia Commonwealth Cyber Initiative (CCI) to develop anti-cyber attack technologies and aviation video systems
Research like NASA and Dr Wei’s three-year programme will help improve how AI reacts and adapts to challenging air conditions. In coming years, autonomous aircraft will likely take off slowly, starting with small package delivery, then upgraded drones, and finally commercialised aircraft. But congestion issues will worsen until autonomous aircraft are the best alternative.
According to BBC Future, by 2030, commuters will spend nearly 100 hours a year in Los Angeles and Moscow traffic jams, and 43 cities will be home to more than 10 million people. The final verdict? Bring on the AI-operated transit.