The sophistication of modern fraud threats means that traditional rules-based systems struggle to adapt to this constantly evolving landscape. Moreover, they often block a significant proportion of genuine customers by misidentifying them as suspected fraudsters.
Companies need to strike the right balance between detecting fraud and offering customers a simple checkout or onboarding process. The most effective way to modernise prevention strategies lies – as could be expected – with Artificial Intelligence (AI), but in combination with the power of machine learning (ML).
Benefits of AI and ML fraud prevention
A combination of AI and ML helps companies accurately identify fraud while reducing the number of false positives, without adversely affecting the customer experience. The best fraud solutions combine ML with unambiguous rules and device identification for high accuracy and reliability.
Unlike rules-based fraud prevention, the performance of a Machine Learning model improves with more data. The larger the data set used to train the model the more accurate the model will become. Another benefit of Machine Learning is the ability to find correlations and patterns in data that are not immediately apparent to a human fraud specialist. This capacity to identify trends and underlying associations, is one of the main reasons why AI outperforms any other type of fraud solution.
AI fraud prevention software also has several advantages, including:
- Greater accuracy in identifying both fraudsters and trusted customers.
- Better customer experience and faster threat detection, as AI systems operate in real time and classify transactions almost instantly.
- Cost reduction because only one fraud platform is used and there are fewer referrals.
- Scalability: long-term growth and peak periods are easier to manage as no additional staff are required.
- Quick response to trends due to the flexibility of the ML model.
- Fewer chargebacks because the software detects even the most subtle signs of chargeback fraud, even before it occurs.
- No downtime because AI continuously monitors and analyses sales unlike human fraud agents.
Evidently, companies also face fraud prevention challenges. Examples include rising costs due to the use of multiple types of fraud prevention software; increases in referrals that increase delays and costs; insufficient manpower to quickly adapt models, rules and scores to new forms of fraud; the inability to align fraud prevention and revenue growth; or a lack of physical biometric identity verifications.
This holds companies back from successfully managing fraud costs and risks. And only by understanding these challenges around fraud prevention management, you can find the fraud prevention solution in ML.
Would you like to improve fraud prevention in your organisation? With Aidrian, our powerful AI fraud prevention solution, you can control online fraud and increase revenue.