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Algorithms like decision trees, random forests, gradient boosting or neural networks are designed to find complex, nonlinear patterns utilizing hundreds (if available) features of transactions. They thrive in environments where the volume and dimensionality of data is high. ML models address the shortcomings of rule based systems.
MANUAL RULES RISK ENGINE MANUAL
Rule based systems are limited by human comprehension (due to manual development of rules & necessary maintenance). Detection of fraud cases with limited complexity - there is a limit for number of rules & transactions’ features.Incremental number of rules - cost of maintenance grows in time (recalibration & adjusting to new fraud patterns).Continuous need of reverse engineering fraudsters’ attacks - new rules have to be developed as new fraud patterns emerge.Low threshold of entry - you don’t need a team of data scientists, machine learning engineers or MLOps - first rules can be easily implemented by the backend team since they are already familiar with translating business logic into code.No cold start problem - they are operational from day 1, there’s no need to gather training datasets that are required for machine learning algorithms.
MANUAL RULES RISK ENGINE FULL
Full explainability out of the box - if a certain rule triggered an alert for a particular transaction it’s 100% transparent why this happened.This is one of the reasons why their presence is still very strong - stakeholders trust them because they mimic the way in which they themselves would tackle this task. They mirror the way in which a human would process a transaction - the engine checks if a transaction meets any of the risky patterns expressed in the rules and if it does, it blocks it or sends it to be manually reviewed by humans. The rules are often expressed using “ if-else” statements present in almost all imperative programming languages and are easily interpretable. analyzing caught / prevented fraudulent transactions and developing new rules to cover all of their suspicious characteristics.following industry best practices - like blocking multiple transactions from a single account in a short period of time or the ones coming through VPNs or from risky areas,.
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Rules based systemsĪs the name suggests, those systems rely on hard coded rules that are set to flag transactions if they meet certain criteria. Here, we will focus on just one crucial piece, a single cog in the machine - the decision engine that determines whether the transaction is fraudulent or not. Those challenges tend to differ a bit depending on the chosen decision engine and business sector specificity, but they are not the main topic of this blog post. There are a lot of other engineering challenges in various areas ranging from infrastructure, backend and frontend programming.
MANUAL RULES RISK ENGINE CODE
Systems that guard merchants from fraud are a lot more than just serialized machine learning models or sets of rules expressed in code using many “ if-else” statements. A crucial cog in the machine - the decision engine In this piece I will shed some light on the main differences between the two approaches and which use cases fit one or the other better. Nonetheless, many fraud prevention systems still rely on hard-coded rules engines that consolidate the aggregate knowledge of fraud experts. The task of detecting fraudulent online payments is a perfect use case for applying machine learning algorithms that thrive in environments where data volume is high and the characteristics of fraudulent transactions cannot be easily detected using only a handful of features. How Machine Learning Models Can Outperform Rule Based Systems, Explainedīy Jakub Karczewski | machine learning engineer | nethone Introduction