The Great Recession, commonly referred to as the 2008 recession, was a global economic disaster that cost the world economy more than $2 trillion. After the credit crunch of 2007–2008, a lengthy period of low growth and rising unemployment resulted from the recession.The credit crunch was the fundamental cause of the Great Recession since it characterised the situation in which banks all around the world ran out of money, which decreased the amount of loans available. Major US banks increased their sub-prime lending in the years before the crisis, which are mortgages with higher risks for which the banks demand a higher interest rate.
In this article it is important we talk about Artificial intelligence and Areas where AI is key to preventing Financial crises.
Artificial Intelligence (AI) interest has been increasing exponentially over the past few years, making it one of the top “buzz words” in all business sectors. A more advanced kind of AI, like IBM Watson, which can be used to assist firms forecast future events, automate difficult procedures, and maximise employee time, is being developed by companies like IBM and Google with significant investment.
Due to the many intricate dependencies and processes present in the global banking ecosystem, implementing AI could prove to be exceedingly difficult. On top of that, banks are producing petabytes of data—soon to be zettabytes—from a variety of internal and external sources, including contracts, emails, web portals, and more.
“Over the years, banks have adapted their operation with the latest technological innovations to improve their customers’ experience,” stated Ricardo Costa, CEO and Founder of LOQR. Traditional banks were able to change and keep up with the most recent technological advancements with the help of a few adjustments, including ATMs, card-based payments, and online banking. Now that we are in the AI-powered era, it is essential to comprehend how to use AI technologies to support the transformation of the banking industry.
There are three key areas where AI could play a part in preventing another financial crash:
1) Risk Management
2) Know Your Customer (KYC)
3) Fraud Detection
Risk management: It’s important to understand the risks involved in lending money, which is why there is a strict mechanism in place to make sure both the Bank and the beneficiary can afford to repay the loan with interest. Beginning in 1999, there was a sharp rise in sub-prime lending, which meant that banks were taking on more risk by making loans to borrowers who owed more money than their homes were worth.
As a result, a number of regulations were established, adding to the difficulty of compliance. Ensuring complete compliance is a difficult and time-consuming effort. By using AI to automate these procedures, banks may better monitor banking data in real-time and identify possible hazards before they have a significant impact on the wider ecosystem.
Know Your Customer (KYC): The financial crisis was greatly exacerbated by an inadequate assessment of the risks associated with lending to clients. Even with significant financial investments in risk management activities, it is challenging to depict the client in all of their complexity. But by scouring regularly updated public datasets for anything that can affect the customer’s creditworthiness, we can use AI to deliver a comprehensive assessment. We can create a more complete picture of the consumer and produce a risk score that is more accurate by analysing data from various internal and external data sources.
Three types of data exist: semi-structured, unstructured, and structured. Lenders could have a hard time understanding the people they are lending to due to a lack of applicant information. The necessary data is currently spread across several systems and manifests itself in various ways since large firms are still working to improve their data literacy.
It is not simple to distribute this information to the appropriate individuals, and interpreting it is made more challenging by the numerous formats. In order to create a more accurate profile of the client and their potential risk, AI may be used to tackle this issue because it can read and analyse data in any format, even unstructured data (such as text-based documents). All things considered, this opens up a completely new field of customer comprehension.
Fraud detection: Banks can evaluate patterns in transaction data and identify egregious conduct by using AI for enhanced pattern recognition. These algorithms have the capacity to spot tax evasion on behalf of both private citizens and large multinational corporations, as well as to assure compliance with trader neglect. It can also instantly identify when a trader is acting illegally. As a result, banks can restrict their loss by putting a stop to it as soon as possible.The average monthly purchase made by insider traders is $1.2 billion, according to SecForm.
Without the aid of automation or artificial intelligence, this is not a straightforward practise to spot. The ability to scrape numerous social media networks and run real-time anomaly detection algorithms on trader data is one capability that can be used to check that brokers are not violating contract terms.
In conclusion, Implementing a big, enterprise-wide system will always provide challenges, notwithstanding the rapid advancement of artificial intelligence. Such algorithms must be developed, tested, implemented, and evaluated by a group of highly skilled Data Scientists, Business Analysts, Data Engineers, and Banking SMEs.
To reduce the false-negative rate, organisations should emphasise a test-driven approach throughout the entire lifecycle. To position themselves to disrupt the market, they should use the newest business intelligence tools and emerging technology to view commercial and performance activity across all business areas, flagging anomalies and problems before they fully develop.