Insurance Providers

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. Within 90 days you see which policies were at risk, when intervention was optimal, and whether behavioural signals outperformed your 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

Treatment-versus-control test shows renewal rates, intervention economics, and whether behavioural intelligence outperformed traditional renewal campaigns.

Preserve Portfolio Premiums

90-Day Value Proof

Measurable renewal improvement within first deployment quarter

Retention-Based Pricing

Performance model aligned with premium preservation outcomes

Regulatory Compliance

Built for IRA (Kenya), FSCA (South Africa), NAICOM (Nigeria), and other African insurance regulators

Assess Retention Economics