Case study · Agriculture
From fields to qualified opportunities.
How a bank uses FinSat to grow its agricultural lending. An illustrative scenario.
Why agriculture
Why we started here
- Essential to every economy
- Active in plain sight, yet hard for banks to reach
- Under-served by financial data
01 · The brief
A bank wants to grow its agricultural lending
The bank selects
- Market
- One region, three districts
- Segment
- Mid-size agricultural businesses
- Product
- Working capital
3,200active businesses brought into view
1,450match the bank's customer profile
1,100qualified opportunities, each with a unique reference
The bank receives 1,100 ranked opportunities with a profile and potential score for each, then chooses who to approach first.
Illustrative scenario.
02 · Qualify
How is each farm really performing?
Water status in the region
Water surfaces
Water available around the farm
Land moisture
How wet or dry each field is
Health of the field
Crop health
Condition over the season
Crop rotation
What was grown, year after year
Approximate yield
Estimated output per hectare, to size the opportunity
03 · Deliver
Every farm becomes a qualified opportunity
| ID | Size | Trend | Risk | Potential | Reference |
|---|---|---|---|---|---|
| #0129 | Large | Improving | Low | 90 | XXXXXXX.XXX |
| #0455 | Mid-size | Improving | Low | 88 | XXXXXXX.XXX |
| #0417 | Mid-size | Improving | Low | 86 | XXXXXXX.XXX |
| #0144 | Large | Improving | Low | 84 | XXXXXXX.XXX |
| #0277 | Mid-size | Stable | Medium | 74 | XXXXXXX.XXX |
Illustrative output. References masked.
Ready for your team to act on
Ranked by potential, each with a unique reference your team uses to identify the business.
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