For NBFCs, lenders and collection agencies
Every borrower reached. Every PTP captured. Every call RBI fair-practice compliant.
The core problem of Indian NBFC collections isn't strategy — it's coverage. Industry data shows 70-90% of outbound dials go unanswered, and even dialled numbers compete for a shrinking pool of agent hours. Nexshift ships a complete AI collections stack: WhatsApp + voice in 10 Indian languages, PTP capture, payment links in-chat, RBI fair-practice guardrails on every message, and clean escalation only when a human genuinely adds value.
Industry benchmark: it costs ₹1,500 to reach 10,000 borrowers on WhatsApp via approved utility templates (at ₹0.15/message). The same reach via human calling costs an order of magnitude more and misses 70-90% of dials. The economics of collections already changed; most teams just haven't updated their playbook yet.
— from the Nexshift team
Stack
What this solves
The specific pieces of the workflow Nexshift handles.
WhatsApp-first for routine conversations
EMI reminders, balance queries, payment links, PTP capture, dispute intake — all handled on WhatsApp through Meta-approved utility templates. The borrower who ignores a call reads a message.
AI voice for borrowers chat misses
For borrowers who don't engage on WhatsApp (feature-phone users, lower digital literacy, older demographics), AI voice calls in Hindi, Tamil, Telugu or any of 10 Indian languages. Natural conversation, not an IVR tree.
RBI fair-practice compliant by default
Scripts respect contact-hour limits. Approved tone and language only. Opt-out handling automatic. Full audit trail — every message, every call, every outcome retrievable per your retention policy. Ships pre-configured for regulators who've asked.
PTP capture + payment-in-chat
Promise-to-pay commitment captured conversationally. Payment link shared on WhatsApp in the same thread — intent converts to payment without a channel switch. UPI QR, net banking, cards.
Clean human handoff
Disputes, hardship cases, large-ticket negotiations and anything flagged "always escalate" get routed to a human in your collections tool (Chatwoot, your CRM, a custom inbox) with the full transcript. Humans only touch cases where judgement is required.
Reach borrowers in their own language
Hindi, English, Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Bengali, Punjabi — plus natural code-mixing. A borrower spoken to in their own language engages at measurably higher rates than one spoken to in generic English.
Volume scales without headcount
Message volume grows with the loan book; the collections team does not. Festival peaks, month-end spikes, portfolio growth — absorbed by the AI layer. Human agent time is reserved for cases where it moves money.
Analytics the compliance team can actually use
Collection rate by bucket, PTP-kept rate, cost-per-recovery, agent-vs-AI performance, cause-of-loss on lapsed accounts. Dashboards exportable, queryable, tied to your LMS/LOS data.
How a lead moves
End-to-end flow, no human in the middle for routine steps.
- 01
Pre-due reminder
3-7 days before the EMI due date, a WhatsApp reminder goes out via an approved utility template. Includes the amount, due date and a direct payment link.
- 02
Due-date nudge
On the due date, a second message. For borrowers who respond with a question (balance, policy, payment issue), the AI handles the conversation.
- 03
Post-due AI call
Borrowers who haven't paid 2-5 days post-due get an AI voice call in their preferred language. Confirms awareness, captures PTP, shares a payment link on WhatsApp during the call.
- 04
Human escalation on judgement cases
Disputes, hardship, hostile interactions, or any case the AI flags as "needs judgement" routes to a human agent in your CRM with the full context. Agent walks in prepared.
- 05
Post-recovery close
Payment received → thank-you message, PTP commitment cleared, lead moved to "resolved". Analytics capture outcome vs. prediction. Dashboards show what worked and what didn't for next cycle.
Proof
What this looks like in production.
Common questions
The specifics teams ask before deploying.
Is this compliant with RBI fair-practice conduct norms?
Yes. Scripts conform to RBI fair-practice collection norms by default — contact-hour limits enforced, approved tone only, opt-out handling automatic, no harassment language, no threats. All outbound messages use Meta-approved utility templates (so nothing off-script reaches a borrower). Every call is recorded with a transcript, retained per your retention policy. We've shipped this with NBFCs that have been through real regulator audits.
How does PTP (promise-to-pay) capture work?
The AI asks when the borrower plans to pay, captures the specific date and amount, writes it to your CRM as a PTP record, and schedules a follow-up on that date. If the PTP is kept, the account closes. If not, the AI follows up on the committed date, not before — respecting the borrower's stated intent. PTP-kept rates become a measurable number instead of a vibe.
What languages does the voice bot actually sound natural in?
Hindi, English, Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Bengali, Punjabi — tuned for Indian voices with proper script, correct pronunciation, and natural code-mixing (Hinglish, Tanglish). We can tune accent + vocabulary to your region if the borrower base skews to a specific state.
What about borrowers who refuse to engage with WhatsApp or AI?
They escalate to a human agent in your team. The AI's job is to handle the 60-80% of conversations that are routine (reminders, balance queries, payment confirmations) so your human agents can spend time on the 20-40% that genuinely need judgement — disputes, hardship cases, high-value negotiations. Nothing in this system removes humans from the hard conversations.
Does this integrate with our existing LMS / LOS / CRM?
Yes. REST API integration with any system that exposes borrower records — your LMS, LOS, collections CRM (whether that's a bespoke system, Credgenics, Lentra, or a custom build). We read overdue accounts, write back outcomes, PTP commitments and dispute flags. Weekly file-based sync works for systems without APIs.
What does this cost compared to our current recovery team?
Collections has two cost components: fixed (agent salaries, infrastructure) and variable (per-dial, per-message). Nexshift converts the fixed cost into variable — pay per connected minute of voice, pay per Meta-tiered WhatsApp conversation. For most NBFCs, this is a 40-60% reduction in cost-to-collect at steady state, with coverage going from ~15% of borrowers reached to effectively 100%.
Walkthrough on WhatsApp
See the AI Collections for NBFCs flow running on your actual leads
Chat with us on WhatsApp — we'll set this up on your real pipeline and walk you through it live.