Banking Safeguards through AI


Consider three distinct scenarios:

  1. A person perched atop a tree or hiding in a thicket, outside a local bank, waiting for someone to exit the building after having withdrawn money. The person on the tree is awaiting the right moment to pounce and get away with the valuables.

  2. A person, pretending to be an executive of a local bank, rings up a customer and, after recounting some personal details of the unsuspecting yet nervous customer, lulls them into confidence and asks for sensitive information. Later, the fraud withdraws large sums based on that information.

  3. Another person cooks up a scheme where they defraud several unsuspecting people who’ve provided sensitive information pertaining to their bank accounts.

In the scenarios above, there is a progressive decrease in force used and a subsequent increase intact with which people defraud others and extract money. With the advent of technology, people can now steal with remarkable ease. It is a misconception that technical prowess is a prerequisite for cyber frauds—various hacking tools are sold to wannabe crooks with amateur technical skills.

 In the present era, every person in the scenarios mentioned above, who is trying to steal, is behind a virtual screen and possesses technical prowess that can be used to steal money without the risk of being apprehended or nabbed by a police officer or a civilian.

The risk of getting caught has decreased significantly, and, consequently, the difficulty of apprehending these cybercriminals has increased. The modern thief has gotten smarter, sharp, and more evasive. Modern problems require solutions. With the increase in internet transactions, the focus of criminals has shifted to digital platforms.

The advent of technology ushered not only a new class of criminals but also the tools to ward off cyber-danger. There has been rapid advancement in technological security to ramp up protection—especially in financial services institutions such as banks. The rampart of Artificial Intelligence (“AI”) ensures that the elusive modern thief is stopped in his tracks before any irrevocable damage is done.

It begs the question: How does AI ensure that the accounts of customers aren’t wiped clean? The answer to this simple question demands a deeper understanding of the task at hand and parsing the intricacies of cyber fraud.

Cyber Fraud: The Thief is Online

Until 2015, the most well-known way to defraud involved taking credit card numbers and printing them onto plastic cards and then using them at small-scale departmental stores. However, in 2015, the EMV (Europay, Mastercard, and Visa) payment method was stipulated for banks—every transaction required a PIN.

Another common financial fraud targeted the mobile phones of the customers rather than the bank itself. SIM swap attack happens when a mobile number is hacked. A criminal attacking the mobile phone gains access to all messages being received by the victim’s phone. After that, credentials needed to log in are obtained through defected apps and are used for fund withdrawals.


The victims are contacted by e-mail, text message, or telephone, and are then taken into confidence by the criminal posing as a representative of an institution. Further, the confidence is exploited to gather sensitive personal information—credit card and bank details, PIN. For instance, an e-mail will be received, by the victim, offering gift cards at a discount and then the debit and credit details will be stored for fraudulent withdrawal of funds in the future.


In recent years, hacking has taken a rather negative form and connotation. In essence, hacking is the modification of software in a way that uses the technology for purposes not originally intended by the developers. This class of miscreants evolved from causing little mischiefs to inflicting damages worth millions of dollars.


The word “vishing” comes from voice phishing and uses tools of deception to extract sensitive personal information. The distinguishing factor is the usage of Internet Telephonic Services rather than e-mail, telephone calls, or fake websites that phishers use. Phishing started as soon as Internet Telephonic Services did. The modus operandi involved scare tactics and emotional manipulation to trick people into giving sensitive personal info. Fake caller IDs are created (known as “caller ID spoofing”) giving the phone numbers an element of legitimacy. The vishers are notorious for stealing money, identity, and in some cases both.

ATM Skimming

Criminals place a device on the ATM which seamlessly fits onto the scanner and seems like a part of the Machine. The skimmer then collects information from every ATM card swiped on the Machine and stores it. The data so retrieved can later be planted on blank cards and used.  Sometimes a pinhole camera is installed to find out the PIN associated with the ATM card.

AI and Machine Learning: Digital eye of protection

What began as a research project in Dartmouth in 1956 for learning and problem solving, artificial intelligence paved the way for the advancement and automation of computers. The goal was to make a computer think like a human and how they would act—AI progresses through deeper learning of how the human brain thinks, reacts and decides. The ultimate goal is to develop machines that learn on their own without any human assistance.

Machine Learning is a subset of AI. It is an application that provides machines with the ability to learn and improve based on experiences. Experiences, observations, data serve as tools for learning and assist in finding patterns in data set and make informed decisions based on that. This application has the potential to process large quantities of data and look for patterns within to assess risks and has wide applications.

Tools for secure banking

AI and Machine learning primarily use data for problem-solving, and the Banking sector is where large chunks of data are crunched every hour. The Banking sector thrives on the knowledge of the market and customer behaviours. It helps provide customers with personalized offers and schemes, and for that, studying usage patterns becomes important. A personalized touch increases customer satisfaction. The patterns serve as the basis for various models, which further add to the knowledge of the institution

Additionally, AI and Machine learning help predict upcoming trends, ramp up support, and reduce costs. However, the shifting of transactions online has also shifted the focus of criminals and fraudsters—numerous cyber frauds and crimes are reported every year, costing the Banks and the people large sums.

Therefore, banks turned to the technological sector for assistance. Following are some of the ways the combination of AI and Machine learning help secure the process of banking:

  1. Risk Management – Machine learning apps use chunks of data to study the market and then forecast based on the identified patterns. The working of the process involves running the data through various scenarios and identifying cases where the outcome may be financially uncertain. After which, the situation is readily dealt with. The process is trial and error that tests every possibility of the market to identify the ones which are more profitable and less risky. In addition to forecasting, AI can also study past data patterns of consumers to predict futures needs and identify suspicious data patterns that may trace back to an illegal source.

  2. Wealth and Portfolio Management – The ability to study patterns and make predictions also extends to portfolio management. The system uses salary details and spending patterns to devise an effective and safe strategy to invest the money of the customer into mutual funds. Again, the spending patterns also help monitor any suspicious activity.

  3. Identifying unusual patterns – The consumer data – spending patterns, the place from where most of the transactions are made, and frequency of transactions can help the system establish an online identity for consumers, like in video games where each character has their quirks and eccentricities. Any deviance, and dissonance, from the pattern, is flagged by the system, which can alert the authorities into action.

  4. Fixing security flaws – Even the most robust antitheft security systems are prone to be hacked and might have several loopholes which the hackers can exploit and breach the system. AI promptly identifies these loopholes and patches them before damage can be done. It continuously improves and augments the security of the security systems.


The shift of the commercial trade from the traditional methods to a more digitized format has led to an increase in online transactions. The shifting trends have led to a change in the ways of criminals—the thieves have moved online and are causing more damage than ever before. There is a need for a fool-proof and a more robust security system that can fend off digital threats. AI and Machine learning have been seen as this virtual light of hope that has the potential to reach incredible levels of efficiency.

Through decades, AI and other technologies based on it have developed and evolved with the growing need of the masses, and the desire for rapid moving and efficient infrastructure. It is the most sophisticated form of technology we have—it can mimic humans. It diverges from the conventional system of breaking data into keywords and processing and attempts to understand the semantics.

AI has wide applications that can utilized in different fields ranging from Banking to Defence. Its reliance on data has made it the most sought-after tool for security in the Banking sector and has shown remarkable results. Banks are safer than ever before. Their efficiency and infrastructure have made life easy for both the employees and the consumers. The sophisticated system of AI aids the smooth functioning of the banks while maintaining top safety standards.  

Frequently Asked Questions

  1. What are the most common cyber frauds?
  2. What is Machine learning?
  3. How does AI help prevent cyber frauds?
  4. How is Internet Banking made safe?


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