Intelligent automation (IA) technologies have great potential to reduce financial losses across all businesses, from multinational corporations to small family businesses and sole traders.

Based on my research, I have identified three categories of financial losses that IA could help prevent: fraud, error and work-related accidents and illnesses. If IA were to eliminate 100% of the losses, I estimate that roughly $10 trillion could be saved overall, which is half the GDP of the U.S. The potential impact on our world could be enormous.

Frauds

Increasing amounts of money are being lost due to fraud. The total cost per year is over $5 trillion worldwide, or roughly 6% of global GDP, and has more than doubled in the last decade, according to research from Crowe and the University of Portsmouth’s Centre for Counter Fraud Studies (CCFS). The researchers suggest three reasons for the recent rapid increase in fraud: the erosion of collective ethical norms due to individualism, the increasing complexity and vulnerability of systems and processes and the lag between technological innovation and regulation.

IA can help by automatically logging every action, increasing transparency and facilitating compliance. At a more advanced level, machine learning can spot patterns in the data generated by this logging and use it to make predictions and raise alerts about potential fraud scenarios.

Errors

Medical errors, as well as causing unnecessary death and injury, also incur economic costs of almost $1 trillion per year in the U.S. alone. This is not counting mistakes purely in billing, which add up to another $68 billion of wasted U.S. healthcare spending per year. These figures are for the U.S. alone, and it could easily be estimated that global figures might be two or three times higher.

This is in the medical profession alone, so consider the magnitude of the figure extrapolated across all industries.

IA programs cannot become tired or distracted. They can be used to provide an extra error-checking step in transactions and calculations without incurring additional ongoing costs in time or labor. They can also be used to flag transactions that appear anomalous according to complex criteria, alerting a human expert who can check whether the anomaly is an error or a valid exception.

Accidents At Work And Work-Related Illnesses

Accidents at work and work-related stress illnesses cost the global economy $3 trillion per year, according to the International Labour Organization (ILO).

IA can play a vital role in reducing this figure. Robots can prevent accidents by taking on some of the more physically dangerous tasks. Robotic process automation and low-code platforms can reduce stress by automating the more repetitive and boring tasks, freeing up humans for more creative, engaging and fulfilling work.

In cases where IA cannot yet take on dangerous tasks itself and avoid the need for humans to do them, it can still help reduce accidents at work by automatically checking compliance with safety regulations and reminding users of precautions they may have overlooked. As for stress-related illnesses, a wide range of IA programs exists for monitoring employees’ stress levels and mental health. Sentiment analysis can be applied to large datasets of emails, instant messages and phone calls. Biometric data such as heart rate can be collected from smartwatches. Computer vision can monitor users’ facial expressions in real time. Machine learning can analyze all this data to look for patterns that indicate or predict stress and help managers to address them.

What’s holding IA back?

While IA has already been adopted by more than 50% of organizations around the world, just under a third of these organizations use it for multiple business functions, according to a recent McKinsey survey. Scaling IA transformations is the holy grail of achieving a higher impact and being able to get the benefits from it. There are some levers organizations can leverage to increase the speed of IA adoption in terms of identifying opportunities, designing the programs and maintaining the programs.

Conclusion

Even if we can only realize half of the losses that IA could help avoid over the next 10 years, that is still $5 trillion of savings. These could be redeployed, using regulation or taxation, to areas where they will have the greatest impact.

As an example of the scale of the potential effects, this $5 trillion that could be saved by realizing just half of the potential of IA would be enough to end hunger and malnutrition worldwide. Alternatively, it could more than double the global investment in education, increase global healthcare budgets by more than 70% or increase environmental investment by a factor of 20. IA is a vast untapped resource, and the savings can have the potential to improve the world almost beyond recognition.