Fraud has been causing rising challenges for businesses. Over 72% of businesses cite fraud as a growing concern, and about 63% of businesses report the same or higher levels of fraudulent losses over the past 12 months according to a report by Experian Global. The challenge is not just about preventing fraud, but figuring out how to predict it before it happens, so it can be prevented from happening at all. But before we make strategies to combat fraud, it’s important to understand the barriers associated with fraud detection strategies.
Most organizations have fraud detection rules. But these manual rules are only good to find the one-offs fraud, they don’t unearth organized activities. Fraudsters have started being more sophisticated and have schemes that cannot be detected without a broad-spectrum investigation of fraudulent activities at an organizational level.
The complications around fraud can be classified into three categories. Businesses and organizations have one or more of these challenges, which from being small fraudulent activities, pretty quickly translates to big dollar losses. The three most distinct and common challenges are;
- Digital transformation
Digital transformation has been a blessing for businesses. The customer is always connected and generated a huge amount of data points, often in gigabytes to terabytes. While it might be helpful for getting insights into customers and partners, it does create a sequence of associated problems that are new.
- Data Silos – Each department starts its own fraud detection team, and their solutions are mostly departmental targeted (narrow focus).
- Data Overload – The fraudulent schemes can be a combination of various challenges, products, or fraud types. With no inter-department communications, this barrier can be hard to overcome. Making it easier for fraudsters to attack complex systems.
- Incomplete View – With a siloed view of data, it’s almost impossible to detect patterns. The manual rules to detect fraud become null void once it’s outside the area it is designed for.
- Difficult To Predict Fraud
The challenge in predicting fraud is just not in the inadequacy of the technology used, it’s also in the nature of fraud activities. And it’s only getting more complicated;
- Fraud is infrequent – Among all the transactions, it might just amount to 1% making it difficult to detect (proverbial needle in a haystack!). In some of the instances, the departments are not even aware that there is an anomaly.
- Faster Transaction Turnarounds – With the speed expected for transaction turnarounds, it is challenging to predict, detect, or recover from fraud. In order to succeed, the fraud detection mechanism has to be faster than the transaction speeds.
- New Fraud Schemes – The mutation of fraud schemes into newer variants is evolving faster than it can be decoded and identified as fraudulent. The fraudsters are a couple of steps ahead in ensuring the schemes go unobserved. Using newer channels and routes to stay undetected could help mitigate this challenge.
- Fraud Detection Is Costly
Capabilities to manage fraud requires huge investment. Most of the fraud detection strategies are expensive and evolving. Needless to say the effort to increase the focus on prediction is still an implausible story.
- False Positives – The benefits are seen as far lesser than the challenges. Even if the fraud is detected, the tradeoff is customer service and heavy manual investigations. Often leading to choice weighing more on choosing customer service.
- Loss Payout – Millions of dollars are reserved for payouts or refunds. Which can lead to poor customer service? Being able to process correct payouts in a timely manner can be integral to successful fraud detection.
- Reputation Risk – The major dilemma for organizations is ensuring the fraud detected is as close as possible. The wrongful indictment can cause reputation issues for not being customer friendly.
How Machine Learning and AI Help?
There are a lot of solutions that can help with having automated fraud detection and prevention solutions. This doesn’t just overcome the above challenges but opens the door for more productive fraud prediction strategies based on past data. The system has the capacity to learn with each instance and have smarter predictive modeling. There are promising solutions for different organizations and different types of challenges. And it just is getting better.