Open-Source-Software zur Aufdeckung von Finanzbetrug: Senkung der Gemeinkosten bei gleichbleibender Qualität

The rule-based engine operates on a set of predefined criteria or rules established by analyzing common patterns and tactics employed in fraudulent activities. It methodically scrutinizes transactions and activities, searching for any instances that align with its established rule set. Upon identifying a transaction that matches these criteria, the system flags it for additional scrutiny or automatically blocks it. This fraud detection approach can be rapidly deployed since it relies on predefined rules rather than requiring extensive training data, distinctly applicable for ML engines. Rule-based algorithms are particularly effective for FinTech companies with well-defined, consistent transaction patterns and a clear understanding of the fraud types they are most susceptible to.

Machine learning engines leverage sophisticated algorithms that learn and evolve from data, identifying potential fraud adaptively and dynamically. Unlike static, rule-based systems, ML engines excel in their ability to uncover and adjust to new, sophisticated fraud patterns by continuously analyzing transactions in real-time. This steady learning process allows for detecting fraud that deviates from known patterns and ensures the system remains effective in the long run. However, the success of ML engines hinges on access to extensive datasets since the data’s breadth and depth directly influence the models’ accuracy and reliability. This approach is especially suitable for FinTech companies with a high volume and diversity of transactions, where conventional rule-based schemes may miss sophisticated fraud.