Language barriers and manual wrap work bottlenecked a $7B loan servicer. Now, AI live-translates calls and auto-summarizes for agents.
A consumer financing company managing a $7 billion portfolio operates a large servicing contact center using a mix of onshore, offshore, and bilingual agents.
The organization needed to scale operations without increasing headcount, but inconsistent performance and language barriers among offshore staff...
“We've made a ton of progress rolling out new technologies so that we can continue to add loans to the books without adding heads to service them.”
Consumer financing solutions for home improvement and recreational products.
Generative AI platform for contact center automation and agent productivity.
Aqua Finance's Customer support is part of this use case:
Related implementations across industries and use cases
Rising volume buried agents in manual tasks. AI now fields routine chats and writes summaries, cutting wrap-up time in half.
Call surges bottlenecked support. AI voice agents now handle precise financial data with strict compliance and zero hallucinations.
High call volumes sent billing inquiries to voicemail. Now, voice AI handles routine payments and navigates insurer holds for human staff.
Vague surveys hid why 360K members called. Managers now use AI to analyze every conversation, score agents, and train support chatbots.
Manual QA left 90% of calls unchecked. AI now audits every interaction, accurately capturing mixed Hindi-English conversations.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Language barriers and manual wrap work bottlenecked a $7B loan servicer. Now, AI live-translates calls and auto-summarizes for agents.
A consumer financing company managing a $7 billion portfolio operates a large servicing contact center using a mix of onshore, offshore, and bilingual agents.
The organization needed to scale operations without increasing headcount, but inconsistent performance and language barriers among offshore staff...
“We've made a ton of progress rolling out new technologies so that we can continue to add loans to the books without adding heads to service them.”
Consumer financing solutions for home improvement and recreational products.
Generative AI platform for contact center automation and agent productivity.
Aqua Finance's Customer support is part of this use case:
Related implementations across industries and use cases
Rising volume buried agents in manual tasks. AI now fields routine chats and writes summaries, cutting wrap-up time in half.
Call surges bottlenecked support. AI voice agents now handle precise financial data with strict compliance and zero hallucinations.
High call volumes sent billing inquiries to voicemail. Now, voice AI handles routine payments and navigates insurer holds for human staff.
Vague surveys hid why 360K members called. Managers now use AI to analyze every conversation, score agents, and train support chatbots.
Manual QA left 90% of calls unchecked. AI now audits every interaction, accurately capturing mixed Hindi-English conversations.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.