Fraud detection is a tough challenge. Fraudulent transactions are extremely infrequent; they account for a small percentage of the total activity within a company. The problem is that without the correct tools and systems in place, a small number of activities can soon develop into severe cash losses.
The emergence of new digital payment channels and technology has created a breeding environment for sophisticated fraud opportunities and bad actors, creating new fraud detection, prevention, and prediction issues.
Unfortunately, the fight against financial fraud is an ongoing challenging process.
The reason is the combination of three factors. Firstly, we have numerous transactions in the data structures. The increasing volume of digital transactions and payments puts strain on fraud detection and prevention systems, which must process, analyze, and authorize a greater volume and diversity of payment traffic.
Secondly, the sophistication of “bad actors” is increasing which strongly threatens the business world. Criminals are using sophisticated tools and cutting-edge cyber strategies to bypass older security systems in the ongoing arms race between fraudsters and financial institutions.
Lastly, the identification of verification is difficult. Identity theft is a serious form of cybercrime that affects both individuals and businesses. For instance, real name theft, synthetic theft, and account takeover are the most prevalent types of identity theft, in which stolen information is used to open fraudulent new accounts, such as bank checking accounts, credit card accounts, or mobile phone accounts.
However, the widespread digitalization did not come as an unreversible fraud-feeder environment. The biggest combat against fraud is the 21st century’s game-changer – Artificial Intelligence.
In fact, Businesses have benefited from using AI to detect fraud by enhancing internal security and simplifying corporate procedures. As a result of its enhanced efficiency, AI has emerged as a crucial instrument for preventing financial crimes.
For example, Artificial Intelligence (AI) may be used to analyze large amounts of data in order to find fraud patterns, which can then be used to detect fraud in real-time. Thus, when fraud is suspected, AI models can be used to either reject or flag transactions for additional investigation. Furthermore, it can grade the likelihood of fraud, allowing investigators to focus their efforts on the most promising cases.
To be more specific, supervised Machine Learning uses alert reduction algorithms to drastically reduce false positive alerts and alert storms, allowing teams to concentrate their efforts on the most important warnings. Users also notice a decrease in the number of legitimate transactions that are incorrectly refused. On the other hand, even when criminal actors adopt new approaches and schemes, AI systems efficiently capture false negatives and minimize the number of fraud cases.
As a result of the use of AI, fraud prevention expenditures are being reduced. Financial organizations can benefit from risk scoring and fraud protection systems in a variety of ways. While the major benefit is lower fraud-related monetary damages, these technologies also benefit the bottom line by lowering expenses connected with manual fraud investigations, enhancing customer happiness, and lowering customer attrition.
Combining supervised and unsupervised machine learning as part of a bigger Artificial Intelligence (AI) fraud detection strategy can help digital businesses discover automated and more complex fraud attempts faster and more accurately. The digital age is, undoubtedly, an open space for innovations and advanced technology, but businesses should not underestimate the risks and challenges. To be on the safe side, companies should extract the possibilities digitalization carries with it and learn how to control it to their advantage.