TY - JOUR AU - Wang, Zihao AU - Yang, Minghui AU - Jin, Chunxiang AU - Liu, Jia AU - Wen, Zujie AU - Liu, Saishuai AU - Zhang, Zhe PY - 2021/05/18 Y2 - 2024/03/28 TI - IFDDS: An Anti-fraud Outbound Robot JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 18 SE - AAAI Demonstration Track DO - 10.1609/aaai.v35i18.18030 UR - https://ojs.aaai.org/index.php/AAAI/article/view/18030 SP - 16117-16119 AB - 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. ER -