How Artificial Intelligence Rescued The Banking Sector During Covid-19
Did you know that the average person interacts with Artificial Intelligence (AI) at least a dozen times a day? If you asked Alexa about the weather, watched a new series recommended by Netflix or made an online transaction, you have just interacted with Artificial Intelligence. The technology is ubiquitous today as it has been embraced by businesses and industries for myriad applications. The banking sector, in particular, has profited immensely by leveraging Artificial Intelligence.
Due to the surge in online banking and digital transactions caused by the Covid-19 pandemic, banks had to pivot quickly to ensure that customer experience and business continuity was not affected. This meant maintaining higher vigilance towards online incidents such as fraud detection and cyber-attacks. Furthermore, as the number of digital wallets facilitating online payments increased, banks needed to encrypt sensitive customer data to prevent fraudulent activities such as identity theft. It seemed like a big ask, however, the banking sector leveraged Artificial Intelligence — not just to adapt — but to innovate!
Firstly, it enabled banks and financial institutions to combine historical data with the predictive ability of Artificial Intelligence to identify cyber-attacks, fraudulent transactions and bad actors in real-time. Moreover, Artificial Intelligence outperformed human decision-making in terms of accuracy and speed across several functions, making it the ideal method to automate security protocols. Yes, it’s the age-old story of automation over manual effort. However, this time it’s not just about optimization or cost-savings — Artificial Intelligence can pinpoint a fraudulent transaction out of a thousand genuine ones within seconds!
Fraud Detection in the banking industry is concerned with the real-time detection of anomalies in a financial ecosystem. By leveraging Machine Learning models, banks can “learn” what resembles a normal transaction — and anything that does not satisfy the criteria is blocked and earmarked for bank personnel to review. Machine Learning algorithms essentially look at previously collected information to identify trends and patterns, and use that to identify the fraudulent transactions from the regular ones. It’s like finding a needle in a digital haystack.
No wonder, banks are seeing the benefits of having an automated fraud detection system, particularly during high sales periods. Traditional fraud detection processes (read: manually-driven processes) could not possibly scale up to effectively monitor all activities. In addition, bad actors and cyber-criminals are constantly developing new ways of concealing their fraudulent pursuits. Hence, it was always going to be a challenge for us humans to pick up on new financial risks in real-time, whereas AI-based anomaly detection models can pick up on the slightest aberrations from a customer’s normal behavior. Using previously collected data — such as your past transaction amounts, location history, purchasing pattern— Artificial Intelligence-based systems can alert banks of suspicious activity instantly.
For example, if you’re someone who generally spends more money during the first week of the month, and primarily for bills and fixed costs, then buying a Dolce & Gabbana handbag on Valentine’s Day will certainly raise suspicion at your bank. There’s even a chance they may cancel the transaction — better safe than sorry, right?
However, in order to effectively understand whether a transaction is fraudulent or not, Artificial Intelligence systems need to analyze and assimilate financial data — lots of it! This brings us to the next aspect where Artificial Intelligence has aided the banking sector immensely — Data Protection.
Most banks use massive financial data sets to train their Machine Learning models, which, in turn, drive their AI-based anomaly detection systems. These data sets must be governed and secured in a manner that enables analysis in real-time, while also ensuring complete protection from hackers. Once again, Artificial Intelligence can be leveraged to identify cyber-attacks or suspicious activity, thanks to its ability to assimilate information and identify deviations.
Let’s take the example of securities in the banking sector. Securities are financial instruments issued by companies or governments that can be traded between parties. Stocks, bonds, mutual funds, exchange-traded funds, etc. can all be considered as securities. Now, a severe lapse in security could allow a cyber-criminal to hack into a bank’s server and access sensitive customer data or distort files with details of securities. However, a more malicious hacker could clandestinely disrupt an entire firm’s financial portfolio by prematurely exiting certain positions (selling off a share that might have given good returns in the future) or triggering a default (buying or selling off a share before it is actually triggered). By identifying these aberrations in real-time, Artificial Intelligence helps banks safeguard their (and client’s) wealth and sensitive data.
In fact, in 2016, a hacker managed to access a bank executive’s email account and instructed employees to transfer money into a bank account he controlled. The bank lost over $75 million! It is extremely crucial for banking institutions to detect and prevent cyber-attacks in real-time — and it is hardly possible to do so without using AI.
Although bad actors are constantly finding new methods of stealing people’s identities and engaging in fraudulent activities, banks need to evolve and ensure that all transactions are legitimate and sensitive customer data is safe. Automating risk management through Artificial Intelligence, especially for fraud and anomaly detection tasks, has transformed the scope of efficiency for the banking domain. Industry leaders such as J.P. Morgan Chase and BBVA had started deploying experimental AI-based security systems to detect credit card fraud a few months before the pandemic came in focus. Today, most banks use Artificial Intelligence to highlighting suspicious transactions and behaviors.
With time, Artificial Intelligence-based security systems will grow even more robust, helping the banking industry tackle persistent challenges such as identity theft, fraudulent transactions and cyber-attacks with more confidence.