How Data and Tech Can Mitigate Risks of Bank Failures Like SVB

How Data and Tech Can Mitigate Risks of Bank Failures Like SVB

by May 22, 2023

The recent collapse of a major bank affiliated with financing tech companies, and the subsequent failures of two other American banks in under two months, has sent shockwaves through the global economy, with effects felt as far as Europe and Asia. 

The unexpected downfall of Silicon Valley Bank, once the 16th largest lender in the US, prompted a rush of customers withdrawing billions of dollars in a matter of hours, concerned for their financial safety.

This unease led to American regulators taking control of two medium-sized banks, Signature Bank and First Republic, while European authorities were forced to intervene with the troubled Swiss giant, Credit Suisse. This tumultuous period also saw the share prices of many lenders nosedive and large sums transferred away from firms perceived as risky. 

While the exposure in Asia has been minimal thus far, the accelerated rate of the bank failures is currently stirring global financial markets and has kindled discussions about the potential implications of US bank failures on financial regulation in Asia.

Martim Rocha

Martim Rocha

Martim Rocha, Global Director of Risk Business Consulting at SAS Institute, emphasised the importance of recognising the causes that could have longer-term consequences within the Asian financial services sector.

“Recent bank collapses, such as the Silicon Valley Bank collapse, have been attributed to various factors, including mismanagement, fraud, and inadequate risk management practices.”

The bank failures pose not just a threat to their customers but also to other financial institutions and businesses that may become targets of fraud, scams, and phishing attacks. Martim argued for the establishment of a risk-conscious culture among financial institutions and their clients, optimising capital and liquidity, and meeting regulatory requirements to mitigate such threats.

The CEOs of both Silicon Valley Bank and Signature Bank called the events that led to their bank failures as “unprecedented“, and something they could not prepare for. Could the benefit of data and advanced tech have helped foresee (and potentially stave off) risks?

“By analysing vast amounts of data from various sources in real time, AI-driven analytics could have detected patterns that would have flown under the radar of their traditional risk management counterparts,”

said Martim.

“Traditional risk management techniques have their role and will continue to be used, but should be complemented by machine learning techniques to improve detection, accuracy, and speedy reaction.”

Such tools would not only serve banks’ internal risk management procedures, but could be beneficial to regulators overseeing financial institutions in Asia and end-users of banking services “in identifying potential risks and taking appropriate actions before they become critical”, according to Martim. 

“These tools can analyse large amounts of data to identify patterns and anomalies that may indicate potential risks, specifically some of [the] new non-financial risk types, where the traditional modelling approaches may not provide suitable answers,”

he explained.

“For example, they can help regulators in conducting scenario analysis and stress tests to assess the resilience of financial institutions to different types of risks, both supporting on generating the scenarios as well as on modelling the risk measurement.”

Martim believes that striking the right balance between short- and long-term risk management strategies, positioning financial institutions to optimise capital and liquidity, and efficiently meeting governance framework demands would be crucial for financial institutions in Asia to avert any crisis associated with bank failures.

“Additionally, they must be willing to learn from the data of failed banks to gain deep insights into the causes of these failures and take corrective action to prevent them from occurring with them. Now everyone knows why Silicon Valley Bank has failed, with reasons ranging from liquidity mismatch, concentration risk and more. But the point is, is the bank measuring liquidity risk at the right level of detail?”

he asked.

“With the speed on how market conditions can change and have changed in recent years, scenario analysis and business forecasting should be a mandatory practice on every management team,”

the risk specialist added.

In light of recent world events like the implosion of cryptocurrency exchange and hedge fund FTX, the bank failures, surging digital fraud in ASEAN, and global post-pandemic economic recovery, Martim believes it is imperative for banks to manage risks in an integrated manner, “as credit risk and liquidity risk are interdependent, as evidenced by the failure of Silicon Valley Bank”.

“The key takeaway should be to ensure their risk management strategy is born from a clear understanding of the risks faced. They need the ability to proactively govern risk management processes, and this rests on managing data and models to respond to market demands while minimising risk,”

he said.

Martim also stressed that banks needed to make their data and technology stacks interoperable and accessible across the organisation – assisting them to react quickly and in a targeted manner to address potential crisis issues, armed with as much information as possible.

He concluded,

“It is through eliminating silos that banks can get the balance right between integrity of information, accuracy, and speed, so that they can remain vigilant in the face of digital fraud, economic headwinds, and regulatory changes”.