# AI insights

##### Snapshot (30d)

- Loans scored: 1,420
- Pawn tickets scored: 1,124
- High‑risk accounts: 9.8%

##### Narrative insight

Sample AI‑generated summary — connect this panel to your own model.

Overall exposure is stable, but risk is becoming more concentrated in a small subset of customers and branches. High‑LTV pawn tickets with multiple extensions pose the largest potential loss if forfeited. Cash loans with salary sources are generally stable, except in one branch where late payments have doubled.

Suggested focus

- Run targeted outreach to customers with both high‑LTV pawn tickets and active loans.
- Review underwriting and extension practices in branches with rising early delinquencies.
- Use AI scores to prioritize which overdue tickets to contact first each day.

##### Patterns by segment

- New customers (<6 months): higher pawn usage, but small ticket sizes; watch for rapid repeat usage.
- Repeat borrowers: strong repayment on cash loans, but at risk when combined with large pawn tickets.
- Branches with high approval rates: slightly higher risk scores, especially for high‑value items.

##### Model feedback ideas

###### Data coverage

- Ensure LTV, branch, and extension count are present for all tickets.
- Capture repayment history at the customer level, not just per loan.

###### User feedback

- Let risk officers tag suggestions as helpful or not helpful.
- Collect reasons when high‑risk flags are overridden.

###### Next steps

- Start with rule‑based scoring, then replace or augment with an ML model.
- Expose scores in Customers, Loans, and Pawn tickets pages.