IFDDS: An Anti-fraud Outbound Robot

With the rapid growth of internet finance and e-payment, payment fraud has attracted increasing attention. To prevent customers from being cheated, systems often block risky payments depending on a risk factor. However, this may also inadvertently block cases which are not actually risky. To solve this problem, we present IFDDS, a system that proactively chats with customers through intelligent speech interaction to precisely determine the actual payment risk. Our system adopts imitation learning to learn dialogue policies. In addition, it encompasses a dialogue risk detection module which identifies fraud probability every turn based on the dialogue state. We create a web-based user interface which simulates a practical voice-based dialogue system.

Authors

• Zihao Wang Beijing University of Technology
• Minghui Yang
• Chunxiang Jin
• Jia Liu Xi'an Jiaotong University, Xi'an, China
• Zujie Wen Ant Group
• Saishuai Liu
• Zhe Zhang

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