Rigid premiums. Irregular income.

Insurance products built for formal-economy payrolls do not survive contact with informal-sector income. When earnings vary month to month, a fixed debit order becomes a lapse mechanism. The policyholder does not cancel. The payment fails, the grace period expires, and the coverage disappears. Behavioural intelligence reads income patterns and payment capacity shifts, enabling intervention before a bounced collection becomes a lost policy.Assess Retention Economics

The Lapse Trap

Policyholders with irregular income do not lapse because they chose to leave. They lapse because fixed collection schedules cannot accommodate variable earnings. A bounced debit order triggers a double collection. When that fails, the grace period expires silently and the insurer records a lapse. The policyholder may still want coverage. Behavioural intelligence distinguishes payment capacity from genuine attrition, enabling intervention before a collection failure becomes a lost policy.

Payment Capacity Intelligence

Income patterns shift before payments fail. Seasonal earnings, informal-sector transitions, and employment changes alter payment capacity weeks before a debit order bounces. Behavioural intelligence distinguishes temporary capacity problems from genuine attrition, enabling intervention while the policyholder still intends to stay.

Income pattern prediction

Payment failure early warning

Capacity vs attrition classification

Claims-to-Renewal Pipeline

In African markets, the claims experience is the marketing channel. A denied or delayed first claim does not just lose one policyholder. It loses their community. Behavioural intelligence tracks claims satisfaction as a retention signal, enabling proactive recovery before a negative experience becomes generational distrust.

Claims satisfaction tracking

Post-claims retention intervention

Community trust preservation

Silent Lapse Prevention

Most lapsed policyholders did not decide to cancel. Their payment failed, the grace period expired, and the coverage disappeared without a conscious decision. Behavioural intelligence identifies silent lapse trajectories and triggers intervention during the grace window, when the policyholder is still reachable and still wants coverage.

Grace period intervention triggers

Silent lapse trajectory detection

Flexible collection orchestration

Compounding Intelligence

Lapse signals sharpen life event detection. Claims patterns refine renewal targeting. Retention responses improve coverage timing. One behavioural layer. Each application strengthens the others. We integrate with your policy data, build lapse models against your book, and run a treatment-versus-control test. A 90-day structure can be used where data readiness, sample size, approvals, and renewal timing support it. The agreed analysis measures at-risk policies, intervention timing, and performance against the existing renewal process.

Assess

Lapse patterns, claims dissatisfaction, and life event signals sharpen renewal targeting. Each data source refines the others, building a compounding picture of policyholder risk and opportunity.

Intervene

Retention responses improve coverage expansion timing. Precision campaigns deploy when intervention timing maximises renewal probability, not when the renewal date arrives.

Prove

The treatment-versus-control analysis will measure renewal rates, intervention economics, and whether behavioural intelligence outperformed traditional renewal campaigns.

Preserve Portfolio Premiums

Proposed 90-Day Value Proof

Treatment group renewal rates vs control group, measured by payment cause

Commercial Terms

Pricing and any success-linked terms are agreed for the scoped engagement

Governance Review

Map data, conduct, outsourcing, and model-governance requirements for the selected jurisdiction

Assess Retention Economics