During the pandemic period, fraud detection has become a very serious issue. There is a substantial challenge now for security agents trying to detect fraud. One reason for this is that fraudulent transactions are only representing a small fraction of financial activity, which makes finding them equivalent to a needle in a haystack.
There is no rule that encompasses every anomalous transaction. So, fraud detection relies on being able to detect deviations from standard activity.
Because Machine Learning (ML) systems can process a large quantity of data very quickly, identify the typical qualities of fraudulent and non-fraudulent transactions, they can detect patterns in data sets and spot outliers and abnormalities quickly. Machine Learning systems have long been recognized as an important asset for fraud detection.
Furthermore, ML models are seen as capable and adaptable. So, they can quickly respond to sophisticated organized crime, though their method changes quickly too.
It is also known that AI models, through anomaly detection techniques. They are well-positioned to observe and respond to changing patterns that indicated fraud. In view of this, the underlying trend within financial institutions, auditors, and governments is to adopt AI technology as part of their fraud detection base.
This move is increasing due to the outbreak of the coronavirus. Of course, the upsurge of fraud detection technology is inevitable, as rapid recent developments have been spurred in no small part by the challenges created by the COVID-19 pandemic.
It is estimated that by 2024, the number of fraudulent transactions could reach $10 billion. Now, fraudsters are working to exploit any improvement in technology that businesses and employees have made recently, because of the disruption that came with the pandemic.
With the lockdown in place, many workers stay at home, while others are placed on furlough. Anti-fraud teams are working with low manpower and operating in an unfamiliar environment. But there is an uptick in fraud.
Adopting AI among anti-fraud teams will bring its own challenges, some of which will include regulatory, compliance, and ethical problems. Bringing humans into the setting will help companies that want to achieve winning strategies over fraud to do so with fewer challenges. Humans can monitor and see reasons why certain aspects have to be changed or adapted as the AI model works. Of course, there will be many things to consider, but the AI for fraud detection move is one that businesses must contemplate at this crucial moment.