Most of the financial crises in the past have been caused by a variety of factors, such as loosened lending standards and stock crashes. Businesses can use AI in finance to enhance their lending standards and improve investments to prevent the next financial crisis.
A financial crisis refers to a situation where asset prices see a steep decline, businesses and consumers are unable to pay their debt, and financial institutions witness liquidity shortages. Financial crises have been around since the very first century. The very first reported financial crisis was in AD33 and was named “the financial crisis of 33AD.” And since then, the world has witnessed several financial crises. Some ended in a few years, while some others lasted for even a decade. Most of these financial crises occurred because of loosened credit lending standards and stock market crashes. That’s what AI can prevent in the future. Algorithms can identify faults in lending standards and predict potential market crashes. Thus AI in finance can even help prevent financial crises of the future.
Preventing future crises with AI in finance
Learning from historical financial crises data, AI with its predictive nature can assist financial institutions to improve their lending standards and make wise investments in stocks.
Improved lending standards
Everyone in the finance industry might remember the financial crisis of 2008. It can be said that it was the second most severe crisis after the great recession. The financial crisis of 2008 occurred due to subprime mortgages. Finance institutions started lending credits to people without checking their credit scores. And this resulted in a housing bubble, where everyone in the USA began investing in housing. This housing bubble then burst and led to the financial crises.
Financial institutions can now prevent such financial crises by strengthening their lending standards. ML algorithms can monitor digital footprints, income, and social media activities of people who apply for loans. By monitoring such details, ML algorithms can provide accurate credit scores. This can help banks and other financial institutions to determine who will be able to repay their debts and who won’t.
Enhanced investment predictions
Another example of an acute financial crisis is the Credit Crisis of 1772. The Credit Crisis of 1772 occurred due to wrong investments in stocks. Alexander Fordyce, a member of the bank Neal, James, Fordyce, and Down was under huge debts then. He was involved in speculative selling of The East India Company stocks. The prices of stocks fell drastically, and Fordyce got under huge debts. To avoid repayment of debts, he fled to France. This led to the Credit Crisis of 1772. This crisis resulted in the collapse of almost every private bank in Scotland, the Dutch Republic, and London. And it caused a loss of savings worth £300,000 to banks.
ML algorithms can collect market trends data such as stock prices, trades, and demand to predict a steep rise or fall in shares. Such predictions can help businesses to invest in shares and properties that can give optimal profits and avoid crises like the Credit Crisis of 1772.
With capabilities to detect and predict from large datasets, leveraging AI in finance can surely help us to prevent the next financial crisis. But in today’s digital world, financial crises can also occur because of cybercrimes. Loss of transaction details can result in a financial crisis. Hence, banks and other financial institutions need to make their transactions secure. Finance institutions can amalgamate AI with blockchain to enhance their cybersecurity.
