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Insurance · Customer retention prediction

Your retention team calls at 30 days. The decision was made at 90.

Banao builds policyholder churn prediction that identifies at-risk renewals 60–90 days before expiry — segmented by why each policyholder is likely to lapse — so your team has time and context to act, not just a name on a dial list.

The model runs over policy tenure, payment behaviour, claims history, premium change sensitivity, and service contact events, and pushes a prioritised at-risk list with reason codes directly into your CRM or call-centre queue.

A mid-market general insurer— churn model deployed into retention queue, scoring renewals 90 days before expiry.

What a Banao retention prediction deployment includes

A churn model that runs unseen in a data warehouse is not a retention programme. We wire the output into your team's day-to-day workflow so the scored list gets used.

At-risk scoring 60–90 days before renewal

Propensity models over policy, payment, and service data that score each renewing policyholder with enough lead time for outreach to change the outcome — not just to record the lapse after it happens.

Reason-code segmentation

The model tells your team why a policyholder is at risk — price sensitivity, recent claim friction, gap in contact, competitor exposure — so the offer and the call script differ by segment rather than defaulting to a discount for everyone.

CRM and call-centre integration

The scored, prioritised at-risk list is pushed into your existing CRM or call-centre platform on the schedule your team works, with reason codes visible to the agent before the call starts.

Retention offer optimisation

A/B tracking of which interventions — adjusted premium, added benefit, direct account manager contact — actually changed the renewal outcome, so the retention playbook improves on real data month to month.

Portfolio retention dashboard

Book-level and segment-level visibility into at-risk policies, intervention rates, and saves — so retention management is driven by current numbers, not anecdote.

Lapse feedback loop

Every lapsed policy feeds back into the model as a confirmed outcome, closing the training loop and preventing accuracy from drifting as your portfolio mix and market conditions shift.

Deployed on live renewal portfolios

Insurer names are under NDA; deployments are described by line of business. Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.

A mid-market general insurer

Renewal churn model integrated into retention call-centre queue

  • ··%at-risk renewals identified 90 days out
  • ··%improvement in retention call conversion
  • ··%reduction in lapse rate on flagged segment

The insurer's retention team contacted near-expiry policies in the same order regardless of risk, with no way to prioritise those most likely to leave. Banao trained a propensity model over five years of policy, payment, and claims history, surfaced the at-risk segment to the queue 90 days before renewal, and added reason codes so agents opened each call with a relevant angle rather than a generic renewal pitch.

We run our own company on the AI we sell

Banao is a ~300-person engineering company that operates every core pipeline — hiring, demand generation, and client delivery — on its own AI products before shipping them to clients.

InterviewGod screens our own engineering hires every week. Vikaas runs our own demand-gen pipeline. A model that has to perform inside our own operation arrives at your retention team already hardened against the edge cases a sandbox never surfaces.

  • InterviewGodScreens Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When churn prediction is the wrong investment

Not every book has a retention problem that a propensity model solves. We will tell you before you build one:

  • Pure price competition: if policyholders are leaving because your premium is 30% above the market, no prediction model changes that — the problem is pricing strategy, not insufficient early warning.
  • Very small book: below a few thousand active policies renewing each year, a retention manager who knows the book by name will outperform a statistical model at a fraction of the cost.
  • No historical lapse outcomes: a churn model needs labelled examples of who lapsed and who didn't. If past lapses were never recorded against a policy ID, week one is building that record, which shifts both the timeline and sometimes the business case.
  • Patchy CRM coverage: reason-code segmentation needs service contact events. If CRM data is thin, the model's reason codes will be less actionable — we design around this but flag it early so expectations are calibrated.

How we start — two weeks before any commitment to build

You have likely seen a churn model pitched on a slide. We prefer to show you what your book's lapse patterns actually look like before quoting a build.

  1. AI Discovery Sprint2 weeks · fixed price

    We analyse your renewal history, payment behaviour, and CRM contact data and come back with a baseline model accuracy estimate, a lapse-rate reduction projection, and the data gaps to fix before building. You keep the output regardless of whether you proceed. If you do proceed, the Sprint cost is credited against the build.

  2. Build

    Feature engineering over policy, payment, claims, and contact data; model training to your renewal cadence and book composition; and integration with your CRM, policy admin, and call-centre platform — legacy systems included.

  3. Production & continuous learning

    Live scoring into your retention queue, a portfolio dashboard for management visibility, and a monthly lapse feedback loop that keeps the model current as your book and market shift.

Frequently asked questions

Policy start and renewal dates, premium change history, payment behaviour, claims history, and service contact events are the core inputs. More complete CRM coverage improves reason-code quality, but a working propensity model is achievable without it — the Discovery Sprint maps exactly what you have and what baseline it supports.

Yes. Banao connects to legacy policy admin and claims platforms via file drops, APIs, or database-level integration. The model runs in your environment — your cloud tenant or on-prem — so policyholder data never leaves your control.

Typically 60–90 days before the renewal date, which is the window that allows a meaningful intervention. Shorter lead times reduce the options available to your retention team. We calibrate the scoring cadence to match your workflow in the Sprint.

The standard method is a holdout or A/B test: a share of at-risk policies receives standard treatment while a share receives model-prioritised outreach. The difference in lapse rate across the two groups gives direct attribution. We design this measurement protocol during the build phase.

A standard path is a 2-week Sprint, a 6–8 week build, and a 2-week rollout into the queue. Banao's engineering bench means delivery starts within weeks of sign-off, not the months a standing-up-from-scratch hire cycle would take.

See which policies in your book are most at risk of lapsing

Bring your renewal data and your current lapse rate. In 45 minutes we will show you whether a prediction model changes those numbers — and what the maths behind it looks like.

Book a 45-min scoping call