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Behaviour-Led Collections, End to End

Stop chasing the paid. Start reading the rest.

Batch-based risk models that refresh quarterly. Template-based reminders that do not change between borrowers. Channels that do not share what the borrower said yesterday. Right-party-contact stays below 30%. Cost-per-recovery climbs every quarter. The team is busier than ever. The book ages quietly into provisioning.

85% less effort
Every customer-touching signal - voice, written, behavioural, stated, inferred, operational, captured in real time, not sampled at quarter-end.
3× faster recovery
Recovery timelines compress 3 times. Promise-to-pay rate climbs 35%. Cost-per-recovery halves.
60% earlier
Delinquency detection improves 60%. The risky account is flagged in the early bucket. NPL provisioning falls. The CRO sleeps better.
Your collection stack treats the willing, the struggling and the disputing the same way. They are not the same customers.

Most collection platforms still run static risk models, template-based outreach and rigid waterfall strategies. The borrower evolved. The recovery model did not.

Collection was once a manual follow-up team with a list and a script. Then it became a list, a script and a few channels. Today it is a behavioural decisioning problem across the full delinquency lifecycle, or it is a missed recovery. The lender that wins on collections is the one that reads each borrower, picks the right channel and the right tone for each one, and stops chasing the accounts that would have paid anyway.

The Dialler Tax

Your dialler called him 7 times. Your competitor sent him 1 WhatsApp. He paid the other lender first.

The same script. The same cadence. The same 7 calls. Right-party-contact stays below 30%. The customer who would have paid anyway is interrupted at lunch. The customer who is actually struggling gets pushed into dispute. The customer who has already disputed gets another call from the same agent. Dialler-led recovery is busy work that looks like work. The book does not move. The cost line does. The regulator notices. The competitor sends one WhatsApp and gets paid first.

Batch Bucket Trap

The strategy was set on Monday. The customer changed Tuesday. The cycle runs till Friday anyway.

Waterfall cycles. Static buckets. Strategy locked in at 9am, applied to every customer in the segment regardless of what happened in between. By the time the cycle refreshes, the high-risk account is in deeper trouble and the regular payer has been over-contacted. The collection model was designed for a slower world.

Template Treadmill

Same SMS at month-end. Same script at week-three. Same email when nothing else worked.

Audit Trail Crisis

The regulator asks. The team rebuilds the trail for 6 weeks. The DPO never sleeps.

Reaching out more is not a recovery strategy. Deciding better is.

Behavioural Recovery Engine · Where the right channel, the right tone and the right moment finally meet.

Score every account. Personalise your outreach. Recover every rupee.

UNFYD.KOLLECT is the AI-powered, end-to-end collection automation platform that orchestrates the full delinquency lifecycle, from the first early-warning signal on an account through to final recovery and reconciliation. It scores every account on real behaviour, picks the right channel, time and tone per borrower, drafts personalised communications with GenAI, captures promise-to-pay events in the same conversation, and reconciles payments back into the core banking system. Where the legacy collection stacks treat the willing and the perilous the same way, and template-based tools send the same reminder to every bucket, this is the layer that decides, account by account, what should happen next.

He had stretched, not defaulted. The reminders treated him like both. When a leading NBFC adopted UNFYD, the objective was not faster outreach. It was a recovery model that read each borrower before reaching for them.

Behavioural Risk Scoring

Each account carries a live propensity-to-pay score and a propensity-to-default score. Refreshed against every new payment, conversation and channel interaction, with next-best action surfaced per account. Early defaulter detection improves 60%.

Channel & Time Optimisation

A channel attribution engine learns which surface each borrower actually responds to. The night-shift driver gets WhatsApp at 9pm, the HNI gets email at 11am, the senior citizen gets voice IVR after lunch. Promise-to-pay rate climbs 35%.

Agentic GenAI Communications

Agentic AI runs the conversation end-to-end. GenAI drafts every reminder, settlement offer, dispute response and partial-payment plan in brand voice and regulator language, across personalised video, dynamic PDF, conversational WhatsApp and voicebot. The agent steps in only where a human is needed.

Promise-to-Pay & Payment Orchestration

UPI, cards, net-banking, wallets and payment-link surfaces integrated end-to-end. Promise-to-pay detected in conversation, payment link triggered in the same moment, reconciliation with core banking automatic. The recovery closes itself, end-to-end.

Compliance, Consent & Audit

DPDP, RBI, IRDAI and SEBI guardrails baked into the platform layer. Consent management as a first-class versioned workflow, with PII masked at ingest at field-level granularity. Every contact, override and approval carries a timestamp and an attribution.

Live Recovery Dashboard

Bucket-wise recovery, agent performance, channel ROI and predictive NPL forecasting on one screen. Refreshed in real time, drillable by region, product and agent. The Head of Collections walks in with the answer, not the rebuild project.

At A Glance

How Digital Leaders Can Cut Through The Noise.

UNFYD® orchestrates collections across the full BFSI delinquency lifecycle. Personal loans, credit cards, auto loans, home loans, MSME credit. ML risk-scores every account in real time, picks the right channel, time and tone per borrower, drafts the personalised reminder with GenAI in the borrower’s language, captures the promise-to-pay event, sends the payment link in the same conversation, reconciles with the core banking system, closes the case. Early-bucket recovery lifts 35%. Manual collection effort drops 85%. Cost-per-recovery halves. NPL provisioning eases on the next regulator filing.

Stop reminding the people who would have paid anyway. Start deciding who actually needs an intervention.
SEE THE RECOVERY MODEL
  • Premium reminders timed against pay-day, salary cycle and household cash-flow patterns
  • Lapse-intent language flagged in any inbound, retention nudge triggered before the grace window closes
  • Claims-handler tone and IRDAI conduct adherence scored on every recovery conversation
  • Renewal, top-up and reinstatement journeys delivered as personalised video and dynamic PDF
  • SIP-skip risk predicted from transaction velocity and market-event sentiment
  • Top-up and rebalance offers triggered against the investor’s actual cash-flow window
  • RM nudges sequenced for HNI and mass-affluent cohorts with different cadences
  • SEBI suitability and AMFI conduct guardrails baked into every advisor conversation
  • Small-ticket, high-frequency installment cycles managed without bloated agent overhead
  • WhatsApp-first reminders that respect Gen-Z and millennial communication norms
  • Self-serve dispute resolution, partial-payment and re-schedule options in-app
  • RBI digital-lending guidelines and consent capture built into every contact attempt
  • Festive-season EMI nudges sequenced against household cash-flow and bonus cycles
  • Dealer-finance reconciliation pushed to the dealer and the financier on the same record
  • Down-payment recovery, repossession threshold and warranty status on one decision tree
  • Vernacular reminders timed against installation, service and AMC moments
  • Bill-shock detection from usage spikes, plan-shift offer triggered before the dispute
  • Port-out intent caught in distress language, retention nudge fired before the MNP request
  • Enterprise-account dispute resolution routed to the right CSM without a queue
  • Pay-later, partial-payment and plan-downgrade offers personalised to the subscriber profile
Yesterday’s defaulter. Today’s promise-to-pay. Tomorrow’s repeat customer. Recover the rupee and keep the relationship. That’s collections, reimagined.
Precision at enterprise scale

Behaviour data in. Recovered rupees out. In the same quarter you saw the signal.

Every payment history record, every transaction velocity signal, every channel response, every NLP-flagged distress moment, every life-event hint, feeds one decisioning surface. The right account gets the right channel and the right tone at the right hour. The wrong account stops getting the 11th call.

Payment history and transaction velocity
Bureau scores and credit-history events
Voice and chat distress signals
WhatsApp, SMS and email response patterns
App login, payment-link and self-serve activity
Consent, regulatory and audit logs
U

NPL provisioning eases on the next filing

Early-bucket recovery rises, severe-delinquency slippage falls, provisioning models flex accordingly. The next regulator filing reads cleaner. The auditor moves on faster. The board minutes get shorter.

Cost-per-recovery, halved

Recovery handled where the borrower actually responds, not where the playbook said to call. Agents step in only at the 20% of accounts that actually need a human and the platform handles the rest. Cost-per-recovery drops 50%, capacity opens, the line on the contact-centre P&L shrinks.

The borrower stays a customer

Recovery handled with the right tone preserves the relationship. Today’s defaulter becomes tomorrow’s promise-to-pay, becomes next quarter’s top-up loan. Hard-recovery cohorts shrink. Lifetime value rises. The CMO and the CRO finally agree on something.

The model sharpens itself, every cycle

Channel mix, message tone, time-of-day and frequency self-tune against what actually produced a promise-to-pay last week. No quarterly retraining project. No vendor SOW for the next model. The recovery model the CRO inherits in Q4 is sharper than the one they signed off in Q1.

Built for regulated industries

Borrower data stays where your regulator says it stays.

Payment history, bureau scores, contact transcripts, consent records and recovery actions held inside the residency boundary your regulator specifies. Field-level PII masking at ingestion, AES-256 at rest, TLS 1.3 in transit, consent honoured at every contact attempt. DPDP, RBI, IRDAI, SEBI, ISO 27001 and SOC 2 aligned by default.

Cloud / SaaS

Multi-tenant or dedicated hosting, auto-scaling and a 99.9% SLA. The fast path when time-to-value matters most.

Fastest go-live · lowest ops overhead

On-premise

Full deployment inside your own data centre. Complete data sovereignty, no third-party cloud dependency. The deployment regulators sign off without a redline.

RBI / GDPR / PDPA aligned

Hybrid

Processing and storage split across an on-premise core and cloud edge, for mixed compliance needs across business units.

Per-BU compliance · one platform
Role-based access control

Granular RBAC across users, teams, channels and campaign types, with full audit logging.

SOC 2 Type II / ISO 27001 ready

Architecture aligned to the frameworks regulated enterprises are measured against.

Data residency and sovereignty

Data held within specified geographic boundaries for GDPR, PDPA and RBI requirements.

SSO / LDAP / SAML 2.0

Enterprise identity across every UNFYD module via your existing directory.

Encryption at rest and in transit

AES-256 at rest, TLS 1.3 in transit, end-to-end encrypted campaign payloads.

Disaster recovery and HA

Active-passive DR with automated failover and an RPO under 4 hours across all modes.