According to the bank and the U.S. Department of Justice, 2019 saw one of the biggest bank data breaches ever when a hacker gained access to over 100 million Capital One customers’ accounts and credit card applications. (Source: The New York Times)
In December 2018, Huawei’s chief financial officer, Meng Wanzhou, was charged with conspiracy to commit bank and wire fraud, and mislead banks about Huawei’s relationship with a firm in Iran called Skycom, which is allegedly a Huawei subsidiary. (Source: The Washington Post)
Do you know how much money banks lose every year due to fraud?
According to the Financial Regulation News, the latest American Bankers Association (ABA) Deposit Account Fraud Survey Report suggests that the banking industry lost $2.2 billion due to fraud in 2016, of which $1.3 billion losses were related to debit card fraud.
Unfortunately, the technology available to banks and fintech companies is also available to sophisticated fraudsters and cybercriminals today.
With the financial industry connected globally more than ever today, detection of fraud is no mean feat.
Some fraudulent scams in the FinTech space include:
- Loan fraud based on forged documents and credit cards
- Insurance claims fraud
- Account theft
- Credit and debit card fraud
- Mobile fraud
- Identity fraud
- Money laundering
- Tax fraud with forged account statements
When a fraud is committed, the loss is not only borne by the exploited victim, but also by the financial institution involved, that takes a hit in the form of severe reputational damage.
Additionally, a huge penalty is charged on the bank by financial regulators.
This has resulted in Fintech firms consciously looking for solutions based on advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) for fraud detection.
Role of ML in detecting banking frauds and scams
An ML-driven system with smart data processing abilities can process data in real time and give the result within milliseconds, while manual checks may take days, if not weeks.
The end result?
Speedy decisions and reporting.
Thanks to the forecasting abilities of ML-driven software, it can predict fraud scenarios and trends by analyzing past patterns of fraudulent activities. The forecasting functionality of ML-based fraud detection algorithms gives you a rewarding advantage over your competitors.
Automation is that constant trend that remains on top after all these years and will continue to remain on top for years to come. Fraud detection ML algorithms ensure complete automation of various banking and financial processes, which eliminates human errors and any possible internal fraudulent activity that results from the involvement of humans.
Leading banks and financial institutions already use ML-based fraud detection solutions to combat fraudsters
Mastercard is a brilliant example of this.
It uses an ML-based fraud detection system to track and process various details related to the transaction, like the location of transaction and device on which it took place, and purchase data. The system analyzes account behavior in each operation throughout the process and provides real-time verdict on whether a transaction is fraudulent.
Fraud detection and prevention must be a dynamic and continuous process. Because fraudsters are always on the hunt for newer ways and technologies to steal money and sensitive data.
Their minds are like viruses — they evolve fast.
The Fintech industry needs to evolve rapidly and adopt newer solutions based on next-gen technologies like AI & ML for the prevention and detection of fraud.
Jellyfish Technologies assists banks and fintech firms of all sizes in leveraging sophisticated technologies like ML to detect and combat fraud and scams that surround the FinTech space.
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