3× collections efficiency with Voice Agent
A leading Singapore digital bank connected Dyna.Ai Voice Agents to its existing telephony operation for a multilingual collections workflow. The bank reported 3× collections efficiency and more than 1,000 outbound calls per hour. The current approved case evidence supports these two results; other figures require separate confirmation before publication.
The starting problem was operational capacity. Collections demand had reached approximately 1,700 to 2,500 calls per day, while a two-person team could complete about 200. At that level, the constraint was not individual effort. The operating model could not reach enough customers consistently, particularly when volume changed and conversations moved between English, Singlish, and Mandarin.
Increasing manual headcount would have addressed only part of the problem. It would have added training and coordination requirements while leaving the bank exposed to the same peak-volume constraint. The bank needed to increase first-contact capacity without removing human ownership from customer treatment, follow-up, or sensitive conversations.
The challenge
Collections volume was outgrowing the manual team
Dyna.Ai Voice Agents were connected to the bank’s telephony operation and used for a bounded collections workflow. The solution combined automatic speech recognition, text-to-speech, and intent-based conversation management. Localisation was part of the operating design: conversations needed to support code-switching across English, Singlish, and Mandarin, as well as different accents and real-call conditions.
The workflow was not positioned as a replacement for the collections team. The Voice Agent handled repeatable first contact and captured conversation outcomes. Bank staff remained responsible for cases that required judgment, sensitivity, account context, or a follow-up action.
This distinction is important in financial-services operations. The value of Voice Agent was not simply to place more calls. It was to connect higher-volume outreach with a defined path for intent, outcome capture, and human handling. The customer retained control over the treatment of each account and the actions taken after the interaction.
The deployment
Multilingual Voice Agent for first-contact outreach
The initial deployment was deliberately bounded. Dyna.Ai was integrated with the telephony system the bank already used rather than introduced as a wholesale replacement of the collections operation. The available case material describes a first collections workflow and a later expansion to additional application areas; this page should remain focused on the collections use case.
The deployment required more than a dial rate. The bank had to align the Voice Agent with approved conversation patterns, local language behaviour, customer intent, and the downstream process for reviewing outcomes. The draft evidence also refers to tone-safe templates, compliance considerations, intent-based follow-up, and call-quality review. The exact approval owner, monitoring process, and escalation thresholds still need to be confirmed for publication.
How the workflow changed
Collections efficiency increased at higher call volume
The Voice Agents took on repeatable first contact at a volume the small manual team could not sustain. That allowed human staff to spend more time on exceptions, higher-value conversations, and cases requiring judgment rather than initiating every call themselves.
Collections efficiency increased 3×, outbound capacity exceeded 1,000 calls per hour, coverage expanded from 24% to 100%, and manual workload fell by 70% in the reported case evidence.
Together, these measures show the change from a small manual calling operation to a higher-capacity collections workflow: more customers reached, more calls completed, and less repetitive work for the human team.
The results
3× efficiency at more than 1,000 calls per hour
For the bank, the business change was the ability to extend collections outreach without making the manual team the only source of capacity. The bank could connect Voice Agent to an existing telephony operation, localise the first conversation, capture intent, and keep human teams responsible for treatment and follow-up.
The bank moved from a constrained manual calling model to a multilingual outreach operation that could reach more customers when collections capacity was needed. The reported result was 3× collections efficiency and more than 1,000 outbound calls per hour, while human teams remained responsible for judgment, treatment, and follow-up.
