Towards Effective Automatic Debt Collection with Persona Awareness (2023)

Abstract: Understanding debtor personas is crucial for collectors to empathize with debtors and develop more effective collection strategies. In this paper, we take the first step towards comprehensively investigating the significance of debtor personas and present a successful commercial practice on automatic debt collection agents. Specifically, we organize the debtor personas into a taxonomy and construct a persona-aware conversation dataset. Building upon it, we implement a simple yet effective persona-aware agent called PAD. After two-month online testing, PAD increases the recovery rate by 3.31% and collects an additional ~100K RMB. Our commercial practice brings inspiration to the debt collection industry by providing an effective automatic solution.

## introduction collecting overdue debts is challenging as it requires debt collectors to strategically handle various excuses from debtors during outbound calls @xcite . this is particularly difficult for novice collectors who lack experience in strategy planning @xcite . as a result, they often fail to collect debts within a few calls, leading to substantial financial losses. to assist novices, financial companies have invested significant efforts in developing automatic debt collection agents @xcite @xcite . these agents typically plan strategies based on debtors' intentions @xcite , conversation history @xcite , and repayment targets @xcite , advising novices by selecting relevant utterance templates. unfortunately, the existing agents fail to tailor their strategies to debtor personas, which comprise various elements of identity 1 @xcite , leading to ineffective collection. taking fig. 1 an example foot_0 , when dealing with a debtor facing financial difficulties but having a positive attitude towards repayment, advising him to repay in installments is more persuasive than warning him about damaging his credit score. this shows the significance of debtor personas, which can aid collectors in empathizing with debtors' characteristics and behaviors to develop more effective collection strategies. a natural idea arises: introducing debtor personas into automatic debt collection agents. in this paper, we take the first step in comprehensively investigating the significance of debtor personas in automatic debt collection agents. specifically, based on the outbound calls foot_1 , we systematically organize debtors' identities into a persona taxonomy by considering the relationship between debtor personas and strategies. furthermore, we introduce a successful commercial practice: a simple yet effective persona-aware debt collection agent @xcite . it is capable of dynamically summarizing debtor personas reflected in ongoing conversations, and integrating them into strategy planning and response generation by using the attention mechanism. as such, pad brings inspiration to the debt collection industry by providing a more efficient automatic solution: extracting debtor personas from the real-time collection conversations and generating collection strategies and responses to debtor excuses automatically. our experiments demonstrate that debtor personas have a universal and effective impact on various agents, contributing to both strategy planning and response generation. we successfully deployed pad for two months in a fintech company's consumer loan scenario to assist novices. the online testing results show that pad increases the recovery rate by 3.31% and helps to collect an additional ∼100k rmb. and the pad constantly helps novices when dealing with debtors of different personas, especially in developing collection strategies based on the debtors' repayment willingness. we believe that our work could promote the advancement of automatic persona-aware debt collection agents, highlighting the potential to cut the capital expenditure associated with coaching and training novices. in conclusion, our contributions are threefold: 1) we emphasize the importance of debtor personas in generating effective strategies and establish a persona taxonomy for the first time. 2) we proposed a simple yet effective debt collection agent called pad, which dynamically leverages the debtor personas reflected in ongoing conversations to generate effective strategies and responses. 3) our commercial practice reveals that leveraging debtor personas results in better response quality, a higher recovery rate, and significant financial benefits. ## persona taxonomy induction in fig. 1 , we have caught a glimpse of the significance of debtor personas. to methodically examine the correlation between debtor personas and strate-gies, we formulate debtor personas and collection strategies into two generalized taxonomies for the first time. next, we use the taxonomies to construct a persona-aware conversation dataset designed for our pad development and persona analysis. persona taxonomy. inspired by @xcite , we employ experienced collectors foot_2 to induce debtor personas based on 2000 conversations, creating a persona taxonomy. during the induction, 13 experienced collectors are employed together. three of them, who have the highest historical recovery rate, are chosen as coordinators.the remaining 10 experienced collectors are chosen as annotators. the induction consists of four stages: annotation scheme creation, persona annotation, scheme revision, and taxonomy induction (see appendix a for more details). basically, 1) the persona annotation scheme is created by coordinators who identify keywords from debtors' utterances. these keywords are conceptualized into debtor personas. 2) annotators then use this scheme to annotate debtor personas on the remaining debtors' utterances. 3) during the annotation, the annotation scheme is revised by the coordinators if necessary. note that stage 2 and stage 3 are conducted iteratively, where the annotation and scheme revision are repeated. 4) the coordinators finally structure and organize the annotated debtor personas into a taxonomy. our persona taxonomy is a pioneering effort in debt collection industry. it comprises four categories that reflect debtors' repayment ability (i.e., fh, fs, and ds) and willingness (i.e., cd). ## persona-aware debt collection agent as illustrated in figure 2 , our pad consists of two components, i.e., a persona extractor (pe) and a suggestion generator. the former aims to filter out irrelevant utterances and summarize debtor personas, while the latter provides the generated strategies and responses as suggestions to novices. ## empirical experiments we evaluate the effectiveness of personas and our pad, guided by three research questions: rq1: how does pad compare with existing debt collection agents? rq2: are debtor personas effective? rq3: to what extent can pad improve novices' collection performance in the online scenario? ## conclusion we share a commercial practice on automatic debt collection agents. our study involves organizing the debtor's identity into a taxonomy and presenting a successful implementation on the personaaware agent. we emphasize how our practice addresses a common problem in tailored strategy planning. this provides inspiration for the debt collection industry by offering a more efficient and effective automatic solution that leverages personas to improve recovery rates in online financial services. moving forward, we plan to further explore the potential of persona-aware agents in reducing the capital expenditure associated with training and coaching novice agents. ming zhong, yang liu, yichong xu, chenguang zhu, and michael zeng. 2021. dialoglm: pre-trained model for long dialogue understanding and summarization. in aaai conference on artificial intelligence. peixiang zhong, chen zhang, hao wang, yong liu, and chunyan miao. 2020. towards persona-based empathetic conversational models. arxiv preprint arxiv:2004.12316.