Use of AI in fighting Fraud

AI or artificial intelligence is a frontier that has so much to offer besides managing your devices, trading apps like HBSwiss, your vehicles, and homes.

Artificial Intelligence has shown immense promise in fighting fraud; MasterCard and RBS WorldPay are witnesses to the use of AI in detecting and preventing credit card frauds.

Why is AI effective in fighting fraud?

AI is been successfully used in the US in banks and other retailers for the following reasons:

  • Individual profiles: AI can build individual profiles which will sieve out any unusual and suspicious-looking transactions.
  • Constantly learning: In AI constant learning is happening which allows it to foil all kinds of cyber threats.
  • Detects telephone fraud: This technology analyses the location, type of call and the Unique caller identity to detect fraudulent calls.
  • Fast: AI can quickly scan vast amounts of transactions and gain insights not visible to human eye.
  • It is intuitive: Unlike algorithms which cannot take the initiative to act or move from a fixed path, AI can react to the situation.

Three ways AI is used to fight fraud

  • Rules and reputation lists
  • Supervised machine learning
  • Unsupervised machine learning.

Rules and reputation lists

Rules are human encoded statements that detect any unusual transactions and behavior pattern instantly. Reputation lists are lists that consist of specific IP addresses, devices and other traits Unique to each account. Hence, if any transaction takes place from a bad list the transaction is blocked.

Advantages: It can be put into practice quickly and at a low cost; you do not need any expertise to implement it. It needs to be updated constantly.

Disadvantages: This system traps only the novice frauds and not the experts as they know that by creating thousands of accounts they can understand the rules and reputation lists of a system and can easily overcome them. Cloud hosting services, VPNs, mobile devices and anonymous email providers do not come under the purview of the reputation lists and thus the fraudsters can escape detection.

Supervised machine learning (SML)

Machine learning is a kind of AI that is been used to fight fraud quite successfully. It looks at all parameters in totality and not individually.

Disadvantage: In this system, initially the models must be fed with historical data to differentiate between fraudulent accounts and good accounts and activity. This is a huge disadvantage because it can only detect frauds which are similar to previous attacks. Cyber criminals can easily find ways to overcome this drawback.

Unsupervised machine learning (UML)

UML’s can generally detect the pattern a fraudster is following because hackers always have a modus operandi that they can’t break away from. As a result, any new pattern is instantly detected by UML. The probability of false positive is limited because an informed decision is made based on the information available and not on set pattern.

  • Advantages: Its biggest plus is its ability to detect new patterns and identify all the associated accounts and provide a complete picture of the fraud.
  • Disadvantage: It is not efficient at targeting low-level scams. It is difficult to implement while also been expensive.

Final thoughts

To maximize the use of AI in fraud detection there should be a mix of all the three ways as the drawbacks in one can be mitigated by the advantages in the other.

Hopefully, the use of AI will prevent a Cablegate in the financial world.