Systematic improvements in the present-day Banking Early Warning Systems use advanced AI/ML algorithms
A decade and counting. It has been over a decade and a half since the Lehman Brothers collapsed. An event marked the onset of the global financial crisis of 2008.
2008 came with financial shockwaves, the worst in 70 years since the Great Depression. Hundreds of thousands of people lost their jobs overnight. Countries needed financial aid to bail out their chief financial institutions while countless US dollars were pumped into the system in a desperate attempt to revitalize the economy.
It took years for countries to overcome the shock and several commissions were tasked to prevent such disasters in the future. One such EU-funded commission that came into the limelight gave birth to what we call today Early Warning Signals or EWS system.
But the real question is, 'Can software prevent the next global financial crisis? And are we ready for it?' Here’s an insider’s outlook.
The Report: Knowing When to Alarm
The commission unit tasked with formulating the Early Warning System concluded, “Systemic events are intimately related to a banking crisis. Even a limited banking crisis may lead to the failure of the banks involved, causing a collapse of the entire Economic System.”
This helped in the formulation of the earlier versions of EWS where banks closely monitored the credit structure, and the framework for Basel III was created.
Most recently, India is on a mission to combat bad loans. The state’s central bank, Reserve Banks of India (RBI), made it mandatory for banks to implement the software to monitor the situation closely. This came as a boon for the economy as Early Warning Systems do work, even on a scale. Over the years, bad loans have decreased substantially, and a positive sentiment exists in the market for EWS and related systems.
These systematic improvements in the present day EWS, that is now powered by advanced AI/ML algorithms, give machines power to suggest suitable measures in order to curb losses as fast as possible.
EWS as a Building Block
Early Warning Systems, today, act as a building block of most banking infrastructures. These systems generally capture raw data from various sources and segregate these data into three major forms. This enables banks to have a bird’s eye view of the entire credit structure.
The system also gathers micro data trends, enabling it to produce signals at all levels. An imminent example of the same in recent times is the global crisis of 2020 triggered by the COVID-19 pandemic. Most nations had the exact blueprint for investments to avoid derailing the entire economy.
The Final Call: Can a Piece of Software Prevent the Next Financial Crisis?
The short answer to this question is yes. EWS along with advanced AI/ML algorithms make an undefeatable combination and can even disrupt some of the major banking frauds in milliseconds. The same is true for financial ecosystems and with the combination of other banking and analytics software, the next crisis could be prevented.
But, these systems need to be proactively built. Moreover, for this to bear fruits, governments' should accelerate efforts of building an integrated financial ecosystem.