Think Beyond Data: Could GPT, Artificial Intelligence & Data Have Prevented The Banking Collapse?

Data-Driven Compliance
#chatGPT #OpenAI #ChatGPT3 #artificialintelligence #AI #machinelearning #HEXANIKA #ML #NLP #naturallanguageprocessing #dataengineering #datamanagement #automation #citi #yogeshpandit #datadriven #dataarchitecture
#PanditSentiment: Large Banks Rocked This Week. Why, How, and Are There Any Problems Ahead?

News and articles surrounding banking collapses have been all over recently as three or more financial institutions have failed or been saved so far this month. If you remember the housing crisis back in 2008, current events are probably starting to feel familiar, but on fast-forward. In light of the current banking crisis, one has to wonder if GPT (Gateway Performance Technologies), AI (Artificial Intelligence) and data analytics could have prevented or at least detected or mitigated this event. In many cases, technologies like these can provide a more proactive approach when dealing with risk management.

The Banking Collapse in Brief

Silvergate Capital announced its official closure on Thursday, March 9th, after announcing a $1 billion loss on its sale of assets. 

On Wednesday, March 8th, Silicon Valley Bank (SVB) announced a $1.8 billion after-tax loss and entered FDIC receivership during the same week. This event marks the largest financial institution failure since Washington Mutual’s failure in 2008, and the first failure of an FDIC-insured bank since 2020.

Finally, New York’s Signature Bank was ordered to close during the second weekend of March as well. 

Why Did the Banks Fail?

Several factors contributed to the failure of these three major banks, but before delving into the circumstances that affected each institution, it’s worth noting that all three were involved in the tech and cryptocurrency economies. After the collapse of FTX, cryptocurrency investors lost billions of dollars, which resulted in subsequent struggles to regain a portion of those losses.

In addition to the volatile state of the cryptocurrency market, the failures of both Silvergate and Silicon Valley Bank were, for the most part, led by bank-run challenges. Whether banking customers were prompted by economic hardships or business-related struggles, the result was a decline in bank deposits and an uptick in cash withdrawals. 

As the demand for cash increased, the above-mentioned institutions were forced to liquidate backing assets to meet the demand, which they did at significant losses. In both of these cases, bonds from the US Treasury accounted for a substantial portion of these liquidations.

Signature Bank was next in line when it came to experiencing a surge in withdrawals, an event that was likely brought about by panicked reactions to the collapse of Silicon Valley Bank. More than $10 billion was withdrawn collectively, which added up to nearly the entirety of the bank’s assets.

Consumers and financial professionals alike wonder whether this event could have been prevented and whether it’s possible to protect society from another financial crisis. All of these mounting concerns have moved focus toward the capabilities of AI and whether these programs can provide us with the enhanced financial security that we sorely need.

Could AI & Data Have Prevented This?

Artificial intelligence is constantly evolving and even at this moment, it’s capable of a great many things. But could it have been utilized to prevent the collapse of major banks?

The most reasonable answer is; perhaps. Currently, AI is capable of analyzing massive amounts of data in rapid succession and in doing so, these programs are able to pinpoint small variations in data patterns. Data variations have the potential to trigger further analysis and reporting, which can notify human bankers when irregularities occur. 

Whether these irregularities predict an increase in withdrawals, draw attention to fraudulent activities, or highlight significant risks, AI’s speed can begin addressing concerns faster than human analysts. Had AI analysis been applied this way and the right protocols were launched as soon as a risk management issue was noted, banks may have had more of an opportunity to prevent their losses from being so substantial.

Along with risk management, pattern analysis, and fraud detection, AI is capable of studying bank customers and their financial behaviors in order to enhance the functions mentioned. Creating customer profiles based on analyzed financial data can create a more in-depth assessment of risks, patterns, and security measures. 

What could management, auditors and regulators could have done to prevent this using technology?

Fortunately, AI and data analytics can provide us with an unprecedented level of insight into financial markets and operations by crunching large amounts of data quickly and accurately to identify patterns, trends, and potential vulnerabilities that might otherwise be hidden from view. This means that organizations, auditors and regualtors can gain a real-time understanding of their operations and risk management. Below are some examples:

  1. Risk Management alerting Losses due to interest rates going up: GPT, AI and data analytics could have easily been used to help identify patterns of first risky investments, probability of interests going upan setting alerts for potential losses and liquidity issues first by the management, then the auditors and then the regulators.
  2. Alert creating red flags on sudden increase in asset under management: By using these technologies, financial institutions and regualtors can monitor for any suspicious activity or unusual behavior.
  3. Alert to auditors and board of investment in risky assets: They can also use predictive analytics to develop strategies that could prevent large losses from occurring in the first place.

Future Banking Protections

The future of artificial intelligence and banking will likely be interwoven as part of the effort to prevent future financial crises. 

Today’s AI features present the following benefits for banks that choose to embrace innovation:

  • Enhanced banking personalization and customer experiences
  • Customized banking insights based on customer behaviors
  • Fraud detection and prevention
  • Cost-savings
  • Data-driven decision-making
  • Greater risk management 
  • In-depth credit analyses
  • Easier customer identification/authentication
  • Money-laundering prevention

Though so many opportunities are possible through today’s AI capabilities, the future of GPT & AI in banking stands to enhance the following customer care techniques, financial procedures, and bank management protocols.

  • Reduced customer bias/discrimination
  • Increased transparency & customer loyalty
  • Improved privacy and security measures
  • Advancements in fraud detection and preventative measures
  • Enhanced collaborative efforts between banks and financial or technological startups
  • More meaningful, in-depth reporting surrounding a myriad of datasets

If you’re interested in exploring the capabilities of artificial intelligence, visit Hexanika. Browse our Knowledge Center and feel free to contact us to learn more about our solutions and services or to request a demo.
Author: Yogesh Pandit

Leave a Reply

Your email address will not be published. Required fields are marked *

Fill out this field
Fill out this field
Please enter a valid email address.
You need to agree with the terms to proceed

12 − 9 =