DEPOSITS · RETENTION

SpreadShieldv2

Deposit retention engine

Reference implementation for SACCO and bank deposit desks. Models churn with logistic features (rate gap, ln(balance), activity, tenure), optimises offer rates on a policy-bounded grid, and supports holdout assignment so retention savings are measured. Default weights are flagged uncalibrated until fit_logistic() trains on labelled history.

Architecture previewBanks · SACCOs · treasury / liability desksPythonNumPy

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THE PROBLEM IT ADDRESSES

When market rates rise, depositors leave. Flat retention offers waste margin; no offer costs replacement funding. Teams need a policy-bounded way to decide who gets an offer and at what rate.

How it works

01Score each account’s churn probability from rate gap, ln(balance), transactions, and tenure.
02Estimate cost of doing nothing vs cost of an offer (extra interest vs replacement funding).
03Search a policy-capped rate grid and pick the offer with the best expected saving.
04Optionally assign holdouts so uplift is measured, not asserted.

Inputs

·Account balance and current deposit rate
·Market / T-bill benchmark rate (live input)
·Replacement funding rate
·Transaction count and tenure (months)
·Policy caps: max offer, trigger probability, budget

Outputs

·Churn probability before / after offer
·Recommended offer rate (or keep current)
·Expected annual saving vs no-action
·Portfolio elasticity diagnostic

Capabilities in a scoped build

—Expected-value retention vs deterministic flight assumptions

—Policy-capped offer-rate grid optimisation

—Holdout uplift measurement + portfolio elasticity diagnostic

—Currency-neutral (KES in, KES out) — no hard-coded FX

LIMITATIONS · READ BEFORE ENGAGING

·Default logistic weights are UNCALIBRATED until you train on labelled history.
·Reference pack only — not a production-certified deployment.
·Does not connect to your core banking system out of the box.

VIDEO PREVIEW

See SpreadShield in action

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