Viruses and financial panics share common characteristics. Today Singapore has lessons for both. The tiny country has avoided a crunch on hospital bed availability, so far. Singapore also has clues for how to fight the cash crunch authorities worry is coming next.
In both cases, data operations deployed to help save money in day-to-day healthcare and banking can save lives and jobs in a crisis. The key is to have early and actionable insights, and to be able to communicate those insights and actions to everyone involved.
Singapore’s health system appears to have seen the pandemic coming. Authorities took early action. They communicated with relevant leaders and the public clearly and in near real time. For financial panic, Singapore presented a similar framework recently to the World Economic Forum. Use artificial intelligence to gain early insights. Allow banks to share information with regulators in an easy to understand way. Keep everyone in synch.
But the devil is in the details.
Hospital beds and liquidity
More is being written about Singapore’s relative success is fighting COVID-19 than their economic work. Italy shows what happens when infections outpace health system capacity. Singapore’s actions have kept the infection rate within the range hospitals can manage. You could say Singapore is managing its hospital bed liquidity well.
Here are their lessons for financial liquidity. The Singapore framework for using artificial intelligence allows banks to keep enough cash on hand to cover risk, but not keep so much cash on the sidelines that the economy is strangled. If there’s not enough cash available, a bank can collapse and take the economy with it. Be too conservative, and you withhold a lifeline for businesses and individuals.
Artificial intelligence can be used to more rapidly identify how much cash is actually needed. But a famous problem with AI is that it typically doesn’t show how it arrived at a conclusion. This is a problem because governments have not seen eye to eye on how much risk is appropriate. That means armies of very expensive human accountants and risk experts are needed to determine how much cash needs to be on hand. These specialists will typically err on the side of being careful. This can strangle a bank’s ability to respond to rapidly moving events.
Singapore’s solution is focusing on AI whose conclusions regulators can better understand quickly on a single sheet of paper or screen.
It’s the user interface stupid
A great deal of technology fails to deliver on its promise by being hard to understand.
Back in the healthcare world, precision medicine based on genetics hasn’t panned out for the longer term trials of cancer patients. Research has found 75 percent of physicians or more believe molecular diagnostic tests can help better target patients’ cancer. But the same research shows these same physicians only order tests for as little as four percent of patients. In part this is because molecular diagnostic test results have taken too long to come back, are hard to read, and don’t present information in a way that’s actionable.
It’s a problem Steve Jobs was made for. Make data analysis easier to understand, and you make it easier to more rapidly use. This approach helped Singapore with COVID-19.
Getting on the same page
Singaporean officials took lightning quick steps as COVID-19 spread in part because the data they were seeing could be quickly and easily understood, and they found ways to communicate it precisely. Authorities issued decisive travel restrictions, ramped up laboratories for early testing and traded on their experience with SARS and H1N1.
They started stockpiling essential supplies for supermarkets two months ago. In early February, as things worsened in China, they created a mobile app that let people under home quarantine report their location to public health experts. The system initially covered 12,000 people.
But this kind of rapid cooperation was tested and made possible before headlines on the virus. Singapore invested in big data largely to find savings in a healthcare system trying to become more efficient in the face of an aging population. The same data gathering and dashboards that helped keep payers, providers and patients on the same page, are now being put to work to help manage resources in crisis.
There’s more to it than dashboards though.
Singapore’s model governance framework for artificial intelligence expands beyond banking liquidity. It’s an approach to ethics, privacy and legal liabilities that aim to make AI fairer, more transparent and human-centric – and always keeping the end user in mind. These are all important issues in a pandemic or financial contagion, but beyond them too.
Like hospitals and banks, companies and institutions with big stakes in Singapore exemplify and test elements of the larger issues in this framework.
Ride-hailing company Grab uses AI to steer drivers away from passengers who are likely to cancel so they don’t waste time and can focus on fares. Banks in Singapore and beyond use AI to flag suspicious transactions that could be connected to money laundering, identifying patterns that human eyes won’t see. AI helps Ngee Ann Polytechnic process its early admissions applications, cutting hundreds of staff hours and making it easier to shortlist candidates for face-to-face interviews.
They are all taking advantage of credible early intelligence and how to communicate it with everyone involved.
More lessons to come
Machines are now modelling revenues and the capital reserves necessary for Singaporean banks and financial institutions everywhere to absorb losses, keep credit flowing, while keeping on the right side of regulators. If it works, the right process on a global scale could free up billions at a time when liquidity is vital.
No country will be out of the woods for some time. A second, so-called boomerang wave of the coronavirus is expected to sweep through much of Asia. But Singapore’s prime minister and governments across the global are enacting more stimulus to support trade-dependent businesses amid the pandemic. More lessons will come for this crisis, and the crisis that comes after.
Simon Moss is CEO of Symphony AyasdiAI, an artificial intelligence company offering a software platform and applications to financial, health and telecommunications organizations looking to analyze and build predictive models in financial crimes and liquidity optimization.