AI Customer Retention · RM Copilot

Reach the right customer before they decide to leave.

RetentionCopilot AI reads the everyday signals in a customer's banking activity — a stopped salary, a shrinking balance, money moving to another bank — and ranks the customers a relationship manager should attend to first. For each one, it explains why the risk is rising and recommends a specific retention action to review.

What the copilot does

Three questions a relationship manager has to answer every week.

01 · Identify

Who is most at risk

Every customer is scored on churn risk, relationship value, and urgency, then ranked by a single retention-priority score. The book is ordered so the highest-value customers with the clearest warning signs rise to the top of the list.

02 · Explain

Why the risk is rising

Each score is broken down into the signals behind it — the salary that stopped, the balance that fell, the card that has gone quiet. The reasoning is visible, so a manager can judge it rather than trust a number on faith.

03 · Recommend

What to do next

For each customer the copilot drafts a retention action matched to the cause — a call, an offer, a service fix — with talking points the manager can adapt. Every draft is a starting point for a person to review, not a decision to send.

The retention loop

Better signals, acted on earlier, compound into value.

Retention is not a single moment. It is a loop a bank runs continuously — and small gains at each step carry forward to the next.

01

Better signal detection

Quiet changes in behaviour are caught while there is still time to act on them.

02

Earlier RM intervention

The right manager reaches the right customer with context, before the relationship cools.

03

Stronger retention

More at-risk customers stay, and stay engaged, because the outreach fits the cause.

04

Higher lifetime value

Retained relationships deepen over time, lifting the value of the book the loop feeds back into.

What this MVP demonstrates

Four capabilities, shown end to end on synthetic data.

Early warning

Risk surfaced while it can still be changed

A deterministic scoring engine reads 120 synthetic customer profiles and flags rising churn risk from real behavioural signals — not after the customer has already gone.

Explainability

Every score traces back to its reasons

Scores are built from named drivers a manager can read and challenge. The model supports the decision; it does not hide it.

Actionability

From insight to a next step

Each customer comes with a recommended retention action and talking points, so the work moves from knowing to doing within the same screen.

RM productivity

Attention spent where it matters

A ranked, explained book lets a relationship manager start the week with the few conversations most likely to protect the most value.

Start here

See the book ranked by retention priority.

The dashboard opens on Sara Al-Rashid's Priority Banking book, ordered so the most important conversation sits at the top.