How data and tech can reduce the risk of bank failures like SVB

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

The unexpected collapse of Silicon Valley BankOnce 16th largest lender in the USprompted customers worried about their financial security to withdraw billions of dollars in a matter of hours.

The disquiet led to US regulators taking control of two medium-sized banks, Signature Bank and First Republic, while European authorities were forced to intervene with the beleaguered Swiss giant, Credit Suisse. This turbulent period also saw the stock prices of many lenders fall and large sums of money transferred from firms considered risky.

While there is exposure in Asia has been the lowest everThe accelerating rate of bank failures currently roiling global financial markets has sparked discussion about the potential effects of US bank failures on financial regulation in Asia.

Martim Rocha, Global Director of Risk Business Consulting SAS Instituteinsisted on Importance of recognizing causes that may have long-term consequences within the Asian financial services sector.

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

Bank failure poses a threat not only to their customers but also to other financial institutions and businesses that may become Targets of fraud, scams and phishing attacks, Martim argued for establishing a risk-conscious culture between financial institutions and their customers, optimizing capital and liquidity, and meeting regulatory requirements to mitigate such risks.

The CEOs of both Silicon Valley Bank and Signature Bank called the events that led to their bank failures “phenomenal“, and something they could not prepare for. Have the benefits of data and advanced technology helped outweigh (and potentially avoid) the risks?

“By analyzing massive amounts of data from various sources in real time, AI-powered analytics can detect patterns that would have flown under the radar of their traditional risk management counterparts,”

Martim said.

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

Such devices will not only serve the banks internally risk management processesBut it could be beneficial for regulators overseeing financial institutions in Asia and end users of banking services to “identify potential risks and take appropriate action before they become serious,” according to Martim.

“These tools can analyze large amounts of data to identify patterns and anomalies indicates potential risks, especially some (a) new non-financial risk types, where traditional modeling approaches may not provide suitable answers,”

he explained.

“For example, they can help regulators conduct scenario analysis and stress testing to assess the resilience of financial institutions to different types of risk, both by generating scenarios as well as modeling risk measurement. Let’s help.”

Martim believes that striking the right balance between short- and long-term risk management strategies, positioning financial institutions to optimize capital and liquidity, and effectively meeting the demands of the governance framework is the key to financial stability in Asia. It will be important for institutions to avert any crisis involving bank failures.

“Additionally, they should be willing to learn from the data on failed banks. get deeper insight into the reasons Learn about these failures and take corrective action to prevent them from happening again. Now everyone knows why Silicon Valley Bank failed, due to liquidity mismatch, concentration risk and more. But the point is, is the bank measuring the liquidity risk at the right level of detail?”

He asked.

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

Added risk specialist.

In the light of recent world events The Explosion of Cryptocurrency Exchange and Hedge Fund FTXbank failure, Digital fraud on the rise in ASEANand global economic recovery after the pandemicMartim believes it is imperative for banks to manage risks in an integrated manner, “because credit risk and liquidity risk are interdependent, as evidenced by the Silicon Valley Bank failure”.

“The key step must be to ensure that their risk management strategy is born out of a clear understanding of the risks they face. They need the ability to actively control risk management processes, and do this while minimizing risk to the market. relies on the management of data and models to respond to the demands of

They said.

Martim also emphasized that banks need to make their data and technology stacks interoperable and accessible across the organization – helping them to respond quickly and in a targeted manner to address potential crisis issues, Armed with as much information as possible.

He concluded,

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