Hello everyone, and welcome to our presentation.
We are presenting today a conversational agent to alert customers of suspected online-payment
fraud with empathetical communication skills.
This is a joint work conducted by my colleagues from Alibaba Group, Cornel University and
Zhejiang University.
My name is Jingya GUO and I work as a user experience researcher in Alibaba Group.
As people from worldwide grow more comfortable using payment apps, especially during the
pandemic, fraudsters have adapted their tactics to take advantage of the quick access that
payment apps provide.
In 2020, the total transaction value in the digital payments in US is projected to reach
4.4 trillion dollars, and the predicted lose because of online payment fraud will reach
200 billion dollars in 2024.
It is service providers responsibility to detect fraud and protect properties for both
online-business and customers.
When a suspected fraud is detected, a common practice is to reach out and call the customer
to verify the important information to identify a fraud.
For example, by asking the customer: “have you verified if the payee is actually your
friend’s account?”
In this scenario, voice-based conversational agents offer an efficient solution with low
cost for information collection, fraud detection and alert provision.
However, voice-based CAs are facing the challenges of conversation breakdowns that occur either
when the agent is not able to understand user’s input or when the user is just not feel like
continuing the conversation.
Besides, users may not feel obliged to repair the conversation breakdowns since in the alert
provision scenario the conversation is initiated by a CA.
And therefore, different from these smart assistants, our CA should take the initiative
to repair a conversation breakdown before users feel confused and abandon the conversation.
So how to build an effective CA to collect information for fraud detection?
Recent literature on chatbot-human conversation suggested that users preferred chatbots to
proactively repair a conversation breakdown using powerful UI elements, and chatbots with
active listening skills are more effective at engaging users in a conversation.
However, for a voice-based conversational agent who speaks via a phone call, it is especially
hard to manage conversations.
Users are reluctant to cooperate and they just simply do not trust the CA.
In this regard, we designed Shing: a conversational agent with empathetical communication skills
to converse with human customers.
Shing consists of several independent components including automatic speech recognition (ASR),
spoken language understanding (SLU), a model-based dialogue manager (DM), a risk detection module,
natural language generation and a text-to-speech (TTS) module.
We analyzed customer’s conversation with human service agents in fraud detection phone
calls and identified three indicators of conversation breakdown.
A question can be seen as the customer’s attempt of clarifying the query made by Shing.
Silence from a customer can be seen as an indicator of hesitating to provide more information.
Irrelevant answers represent the scenarios either when Shing is not able to understand
customer’s input or when customers evade the question.
Shing is designed to be a 28 years old female customer service agent
Based on previous literature and Shing’s persona, we selected repair strategies that
fit in our digital finance context.
We also include expressing empathy and backchannel as a repair strategy with an expectation of
increasing customers’ willingness in information provision and make Shing respond more like
a human.
Based on the challenges and characteristics of Shing mentioned above, we are interested
in whether Shing could be more effective in persuading customers to stop risky transaction,
encouraging customers to communicate more, and whether Shing is perceived to be more
trustworthy.
We conducted an online experiment to evaluate the effectiveness of Shing in a real-world
digital finance scenario.
We set up two versions of CAs, Shing and a traditional customer service CA
The basedline CA followed conversation management principles suggested by Google Dialogflow.
We used both behavioral log data and self-report survey to evaluate Shing.
Behavioral log data includes phone call transcripts, whether the customer stops the risky transaction,
and whether the customer hangs up the phone call.
Self-report survey metrics include trustworthiness towards the customer service, privacy concerns,
and risk awareness.
During the experiment period, Shing and the baseline CA took over 144 thousand phone calls
in total, in which more than half of the phone calls encountered conversation breakdowns.
There were 30 thousand phone calls with the baseline CA and 52 thousand phone calls with
Shing.
And in order to protect as much property as possible, out system first detect the transactions
that are probable to be risky with a priority of sensitivity
We then predict the transaction risk using a CNN model trained on an annotated conversation
dataset.
There were a total of 17 thousands phone calls predicted to be as risky and 65 thousands
to be as non-risky.
162 participants completed the survey.
All of them are native Chinese speakers, have a variety of occupations and educational backgrounds.
By analyzing the phone call transcripts, we found that in risky transactions customers
spoke more turns with Shing than with the baseline CA that repairs breakdowns by acknowledging
them and repeating the question.
Our results also showed that customers spoke slightly but significantly more words when
conversing with Shing than with the baseline CA in both risky transactions and no-risk
transactions.
We measured the stop transaction rate as subsequent customer behaviors after the phone call, which
indicates how many customers terminate the payment when the transaction is risky.
We found 29.6% of customers who conversed with Shing stopped the risky transaction,
which was significantly a larger proportion compared with customers who conversed with
the baseline CA.
Since we found that all customers in all risky transactions were also willing to provide
information
we only compared the differences of willingness in no risk transactions.
We found more than 55% of the customers were willing to talk with Shing and provide transaction
details
Despite the behavioral outcomes, we found that on average customers reported a tendency
that they trust Shing more than the baseline CA, have less privacy concerns and higher
risk awareness, but these results were not statistically significant.
Here’s what we’ve learnt from our experiment: First, we would like to know, are CAs effective
in persuading customers to stop a risky transaction?
We found in both conditions, there were around 30% of the customers took the advice and stopped
the risky transaction.
While on the other hand, this indicated that there were around 70% of customers who ignored
the suggestion from both CAs and continued the risky payment.
We also found that the customer’s word count slightly contributes to the prediction of
whether he will terminate risky transaction.
Because Shing works as a whole, it is impractical to evaluate the effect of each repair skill.
Therefore, future work can compare the effects of each repair strategies and their combinations
in a real-world context.
We fitted a logistic regression model to predict customers’ conversation willingness and
we found that Shing’s word count positively contributes to customers’ willingness.
This finding maybe because the repair skills used by Shing are more acceptable and less
repetitive.
For more detailed design implications, please refer to our paper and we’ll be so much
glad to communicate with all of you!