Collections, servicing, onboarding, and verification handled by governed agents that reason over your policies and act in your core systems — with every interaction logged, auditable, and escalatable to a human.
Communications in banking and insurance are not a service function — they are how credit is recovered, fraud is stopped, and trust is kept. Yet most of it runs on dialer teams, static IVRs, and outsourced scripts.
Regulators now expect consistent treatment, complete records, and clear human accountability. Uncontrolled human conversations at scale are a compliance liability; governed agentic conversations are a compliance asset.
The economics have crossed over: an AI-led service or collections interaction costs a small fraction of a human-assisted one, and the gap widens with every language and every hour of coverage added.
Start where value and feasibility intersect. Tags mark the lighthouse, the fastest path to production, and the plays partners lead.
| Use case | Value | Feasibility | Speed | Note |
|---|---|---|---|---|
Early-bucket collections & payment reminders Lighthouse | Measurable in weeks: kept-promise rate and roll rates | |||
Loan & credit-card servicing (balance, statements, disputes intake) Highest volume | Highest volume; needs core banking read APIs | |||
Lead qualification for loans, cards, insurance Fastest to production | Contained scope; CRM-only integration | |||
Customer onboarding & KYC follow-up | Document chase-up is agent-friendly; verification stays human/system-owned | |||
Fraud-related customer verification callbacks Executive value | High trust value; strict scripting and escalation required | |||
Claims first-notice-of-loss and status | Insurance entry point; structured FNOL intake | |||
Policy servicing & renewals outreach | Renewal persistence lift is directly measurable | |||
Relationship-manager assist Partner-led | Wealth desks: meeting prep, product Q&A with advice boundaries | |||
Branch / ATM service coordination | Appointment booking and issue triage | |||
Vulnerable-customer hardship workflows | Design for detection and escalation, not automation |
The loop runs itself: the conventional journey stalls, backtracks, and drags while the agentic one flows straight through — grounding, acting, and resolving with a human approval exactly where policy demands one. Pause anytime and click any node to explore that step.
Gemini Enterprise Agent Platform runs the collections agent: reasoning, policy enforcement, state, tool-calling into core banking, and evaluation.
Where the bank runs a contact centre, escalations land in Customer Engagement Suite (or the incumbent CC stack) with full context; GECX leads any CC-modernization track.
Tilicho Labs provides the communications platform — telephony, channels, conversational execution, session and routing — and builds the core-banking and payment integrations, with usage-based platform pricing.
Hardship, disputes, and legal-stage accounts always route to trained specialists; compliance owns the offer matrix and vulnerability triggers.
Customers: Contacted respectfully in their language on day one, resolved in 90 seconds, never asked to repeat themselves — and routed to a human the moment hardship appears.
Employees: Collectors stop dialing voicemail and typing wrap-ups; they handle the negotiations and hardship cases that need judgment, with full context served up.
Thirteen dimensions where the operating models differ — from experience and speed to governance and measurement. Point solutions improve one channel; the agentic layer changes how the whole enterprise communicates.
| Dimension | Conventional operating model | Point solutions / partial automation | Agentic model on Google Cloud + Tilicho Labs |
|---|---|---|---|
| Customer experience | Queue, IVR maze, repeat yourself at every step | One channel improves; experience breaks at its edges | Recognized, understood, and resolved in the customer's language on any channel |
| Process completion | Conversation ends; a human re-keys the outcome later, if at all | Bot deflects to forms or articles; completion still manual | Agent executes the transaction in systems of record within the conversation |
| Speed of response | Minutes of hold; days for follow-up | Instant answers for scripted intents only | Instant engagement and resolution for the majority of intents, 24/7 |
| Cost to serve | $13.50 median per assisted contact (Gartner 2024) | Savings on deflected volume; escalations still full cost | Self-service economics ($1.84 median, Gartner 2024) on contained volume; cheaper escalations via triage |
| Revenue & collections impact | Coverage-limited: uncontacted leads and accounts decay | Campaign blasts without conversation or negotiation | Every lead and account engaged in minutes, negotiated within policy |
| Context retention | None — each channel and call starts from zero | Session-scoped memory inside one tool | State and memory persist across channels, sessions, and handoffs |
| Personalization | Script-level segments at best | Rule-based greetings and merge fields | Grounded in the customer's actual history, entitlements, and intent |
| Multichannel continuity | Voice, chat, and email owned by different teams and vendors | Continuity only inside the vendor's channel | One agent layer: start on WhatsApp, continue on voice, finish by email |
| Human-agent productivity | Agents toggle 5–10 screens; after-call work eats capacity | Assist widgets bolt onto an unchanged workflow | Humans receive triaged, contextualized escalations with drafted next steps |
| Enterprise-system integration | Swivel-chair: humans are the integration layer | A few hard-wired API calls per bot | Governed tool-calling into CRM, core, ERP, ITSM — permissioned per interaction |
| Governance & compliance | 1–2% call sampling; script discipline is the control | Per-tool logs; no cross-channel policy enforcement | 100% of interactions logged, policy-checked in-line, and auditable |
| Scalability | Hire, train, attrite — capacity lags demand by months | Scales within one channel until intents get messy | Elastic on Google infrastructure; peak marginal cost ≈ flat |
| Measurement & improvement | QA samples and quarterly surveys | Vendor dashboard for its own slice | Every interaction evaluated against golden sets; policies and prompts improve weekly |
Conventional: Queue, IVR maze, repeat yourself at every step
Point solutions: One channel improves; experience breaks at its edges
Agentic: Recognized, understood, and resolved in the customer's language on any channel
Cares about: Credit cost and reputation risk
Wants: Lower roll rates without headlines about harassment
Will object: Will an AI damage customer relationships?
Proof that works: Pilot complaint-rate and CSAT vs human baseline
Cares about: Data residency, core integration risk
Wants: AI inside the bank's cloud tenant with governed APIs
Will object: We can't send customer data to a third-party bot vendor
Proof that works: Reference architecture + security blueprint review
Cares about: Contact rates and kept promises
Wants: 2–3× contact attempts, consistent negotiation
Will object: Customers will just hang up on a bot
Proof that works: A/B pilot on one bucket with answered-call and PTP metrics
Cares about: Fair treatment, auditability, advice boundaries
Wants: 100% logged, policy-bounded conversations
Will object: Who is accountable when the AI says something wrong?
Proof that works: Guardrail demo: offer matrix, blocked topics, escalation triggers, full transcripts
Cares about: Lock-in and unit cost
Wants: Consumption pricing tied to completed interactions
Will object: Another platform subscription?
Proof that works: Unit-economics model with pilot-verified costs
Channels and industry connectors are partner-built; the agent platform and models are Google Cloud; your systems of record and policies remain yours.
Google Cloud provides the intelligence: agents that reason over policies, history, and enterprise data, coordinate specialized agents, and execute governed actions — running in the customer's Google Cloud environment.
Interaction arrives on any channel — or the enterprise initiates outreach
Identity and permissions resolved; consent and policy windows checked
Intent understood in the customer's language, with full context retrieved
Agent reasons over policies, history, and enterprise data
Governed actions executed in systems of record — payments, bookings, tickets, updates
Humans brought in on sentiment, complexity, or policy triggers — with full context
Outcome written back; 100% of the interaction logged and auditable
Every interaction evaluated; policies, prompts, and workflows refined
Defaults reflect typical financial services interaction profiles — replace them with yours. Every formula is shown; the pilot proves the containment rate before any scale decision.
One collections bucket (e.g., 1–30 DPD), one product, two languages, voice + WhatsApp