Fraud Detection in Banking Using AI

As banking and banking related services are an essential part of the government, it can be considered as one single organisation facing the general public who are at various levels or backgrounds which can be ranging from most disciplined ones to the most susceptible ones in terms of the risks being exposed to. Its duty of this organisation to keep the checks and balances, the forensics intact to minimize the losses as a department of its own.

Over history, the reach of banking and its operations to the local public has become more efficient, more exhaustive, simpler, scaled easier and automated. These are the fruits of the phenomenon of digitisation, information technology services, where the technology at large has taken control of banking operations. However, when we have such reliance in technology becoming a foundation for a bank to operate, there is always the threat of risks which the same technology possesses in scuttling its operations. This is one area of technology risks among the whole spectrum of risks which a bank is exposed off, which this section addresses.

As per the latest data, the global financial crime is massive in scale and accelerating in pace with an estimated value of USD 4.2 tn. Another study estimated that a typical organisation would lose 5% of revenue to fraud each year. The same number would amount to trillions when it comes to the size of this fraud which all banks in the world are vulnerable to. With the thief becoming more smarter a variety of fraud happens at various levels in banking which could be classified broadly into:

  • Credit Card Frauds: Fraud occurs when a fraudster purchases a product or service online and is not obliged to present the card or provide a PIN or signature. Card Not Present fraud can also be a type of identity fraud in which a fraudster takes over the digital identity of a victim and opens credit card accounts or runs up bills in the victim’s name.
  • Synthetic Identity Fraud: In this case, the fraudster creates and establishes credit using a false persona, which is often a sophisticated blend of fake and real personal details; for instance, using a fake name along with a real social security number, such as that of a child, to lend the persona credibility. Puppeting the persona, the fraud‐ ster patiently mimics legitimate financial behaviors—taking out loans, withdrawing cash advances, and running up bills, but always acting properly and paying on time to establish the credit-worthiness of the synthetic persona and grow the line of credit. Then, they perform what is called a “bust out” in which they max out the cards, max out the cash advances, default on all loans, and disappear.
  • Account takeover: Account takeover fraud is a form of identity theft in which a third party gains access to unique details of a user’s online accounts. By posing as the real customer, fraudsters change account passwords and phone numbers, buy goods and serv‐ ices, withdraw funds, and use the stolen information to access other accounts of the victim.

How AI/ML is becoming a solution to fraud

AI Consulting and Development

While we see the characteristics of the financial fraud in its unfold, one aspect is evident, banks or banking over time is becoming a warehouse of data of unimaginable magnitude in terms of customer behaviour, spending patterns and also the way a fraud is conducted. If the data can be analysed and patterned to the specific ways of how the fraudster conducts the fraud, the scope of AI comes into picture. With the current outing, a handful or products are available in the market that sequentially targets this space and builds products using this model. The fruits are impressing in terms of how the statistics show. The technologies are Machine Learning, Neural Networks, Deep Learning, Data science.


  • Reduce Financial Loss `
  • Fraudulent claims detection for insurance companies by 66%
  • Reducing Anomalous transaction investigation time by 70%
  • Detecting fraudulent transaction at 90% accuracy with 30% more fraud detection
  • 56% spammers detected at signup
  • 72% increase in review efficiency
  • 90% fake listing detected instantly
  • Aborting fraudsters from opening massive scale fake and malicious accounts

Let’s work towards building a future-ready enterprise 

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