![]() This returns better results with lesser effort. Understanding Active PU Learning Active learningĪctive Learning is derived from a simple idea - if the cost per tag runs high, only those samples that have the highest improvement impact on the current algorithm should be labeled. Even within the scope of the same accuracy requirements, the model returns a 3x improvement in the identification rate when compared to the baseline model using Isolation Forest. ![]() CREDIT CARD TRANSACTION RISK ENGINE MANUALGenerally speaking, judging a sample to be black is easier since usually, some type of evidence follows the sample, but for a white sample, one must disqualify any evidence, something that is rather difficult to achieve in practice.įaced with these issues, the Alibaba tech team devised an Active PU Learning method which combines active learning (referred to as AL), and Two-step Positive and Unlabeled Learning (referred to as PU), overcoming the issues posed by manual labeling while developing a distinct identification model for cash-out risks for credit card transactions. ![]() Labeling also incurs errors - humans are humans, and even the best of experts are prone to making mistakes in how they judge samples. This makes it difficult to sample labels in large volumes. Labeling costs run high, and on average, manually labeling each sample can take an engineer with the requisite knowledge anywhere from five to fifteen minutes. Manually labeling samples based on operating history followed by supervised learning based on labels is another option, but that too faces some trade-offs. Graph algorithms, on the other hand, often require massive computing power to effectively process the millions of daily Alipay transactions, translating into higher operational requirements and computational costs. Anomaly detection models such as Isolation Forest require more effort on input features and perform inconsistently in terms of top scores if the number of features increases. Though unsupervised models do not require tags, they aren’t the easiest to work with either. Hence, most cash-out risk identification systems rely on unsupervised models such as anomaly detection and graph algorithms. However, users don’t report to Alipay or the bank which transactions are carried out for the purpose of cash-outs, much less those who do so illegally.Ĭommon supervised algorithms are ineffective without tags. When users report stolen accounts or fraud, they also identify and report the transactions that are a result of such activity, which are then escalated further using historical data tags. ![]() Cash-out risks lack an active external feedback mechanism, i.e., no black and white tags on samples. Common concerns like stolen phones and identity thefts are usually easier to model under the supervised learning framework, while the construction of risk identification models for illegal cash-outs is usually more difficult. The array of risks that accompany online transactions pose numerous challenges to modeling. Tackling Illegal Cash-outs with AI Detect As an intelligent risk identification algorithm system, AI Detect incorporates not just traditional supervised learning algorithms like Gradient Boosting Decision Tree (GBDT), but also a variety of feature generation algorithms based on unsupervised deep learning. CREDIT CARD TRANSACTION RISK ENGINE CODEFrom the moment an Alipay user’s payment QR code is read by the scanner to the completion of payment less than a second later, Alipay’s risk control system performs numerous transaction scans verifying that the account has not been hijacked or misappropriated, and that the transaction is genuine in nature.Īlipay uses a state-of-the-art risk control engine named AlphaRisk, at the core of which lies AI Detect. ![]() The centrality of mobile payments in Chinese consumers’ lifestyles makes ensuring safety and reducing transaction risks a critical task. Alipay, Ant Financial’s mobile wallet app, leads the Chinese market with a 54 percent market share of mobile payments. A majority of transactions in Chinese cities are now cashless, with the volume between January-October 2017 reaching $12.8 trillion, more than 90 times the size of the mobile payments market in the U.S. Though adoption of mobile wallets has been slow moving in the United States, they are a way of life in China. The development of mobile Internet and smartphones along with the rise of fintech has given way to one of the most transformative consumer-facing technologies today - mobile payments. ![]()
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