Rwazi built Sena, a Decision AI that shows retail banks and insurers why customers choose a provider, from consented consumer input and geo-verified photos.
Data analytics in financial services starts with transaction data, which shows what customers did inside one institution. Their reasons for choosing that institution, what would make them switch, and the rivals near each branch sit in the market around it.
Transaction volume treats each branch on its own. The rival branches nearby and the customers who depend on it sit outside that data.
Sena. Sena counts the competing branches, ATMs, and banking agents in a catchment and measures which customer segments depend on each location.
Account and card data show who opened, who stayed, and who closed. The reason a customer chose a rival product sits with the customer.
Sena. Sena captures the stated reasons directly from consumers, consented and zero-party, by segment and market.
Credit, claims, and satellite models price risk at scale. Deferred maintenance, drainage, and neighboring conditions show at the property itself.
Sena. Sena captures geo-verified exterior photos across a book of properties and ties each condition to claims history.
Each financial services analytics use case below links to its method page, with the signals Sena reads and the questions teams ask.
Sena counts the competing locations in a catchment and measures which customers depend on the branch, then sets each option beside that count.
Decides: Which branches close, relocate, or change format.
See branch optimization →Sena captures exterior photos across a book of properties, so each renewal rests on what the property shows.
Decides: Which renewals need conditions, adjustment, or review.
See insurance loss control →Consumers state what they chose, what else they considered, and what would make them switch, by segment and market.
Decides: Which product, price, or service change wins the segment.
See purchase driver analysis →Consumers in the target market state their willingness to adopt a card, risk tolerance by income band, and debt appetite, and Sena sets that beside the launch plan.
Decides: Where to enter, with which product, at what price.
See market entry analysis →A real Sena reading for one set of branch catchments across three to five markets, with the photo behind every location figure.
Get the catchment reading →Sena traces each reading to the capture that produced it, so a team checks each catchment count location by location.
Each branch, ATM, agent, or property reading carries its photo, its location, and its date.
Every answer comes from a person who shared it directly, with explicit consent, and clears a multi-level QA flow before it counts.
A closure plan starts with two shares: the customers ready to bank digitally and the customers who depend on the branch. Sena captures both from consumers in the catchment, beside a count of the competing locations.
The branch closure calculator. Two shares show how many customers a closure puts at risk.
A walkthrough runs Sena on one market and one product line, from consumer input to the decision on the table.
Book a walkthrough →Pick the closest fit, and the walkthrough opens on that segment's question.
Branch network, catchment competition, and the customers who depend on each branch.
Book a banking walkthrough →Why customers pick a card or account, and what would move them to a rival.
Book a leadership walkthrough →Property condition at book scale, tied to claims history and renewal decisions.
Book an insurance walkthrough →Member dependency, nearby alternatives, and the format each location needs.
Book a credit union walkthrough →A real Sena reading for one set of branch catchments across three to five markets, with every competing location and consumer answer visible behind each figure.
Sena captures geo-verified photos and consumer activity in the markets a question names, when the question needs it.
Sena reads those captures beside the files and systems already in use, across 250+ integrations.
Sena compares the market against the plan and shows each gap, with every figure traced to its source.
| Area | Common method | Sena |
|---|---|---|
| Branch network | Transaction volume per branch | Volume plus the competing locations and dependent customers around it |
| Customer choice | The reason inferred from who opened and who left | Consumers state why they chose, stayed, or switched |
| Property risk | Credit, claims, and satellite models | Geo-verified exterior photos tied to claims history |
| Market entry | Desk estimates of a new market | Consumers in the market state demand and willingness to adopt |
| Auditing | Catchment counts reconciled by hand | Each location figure opens the photo behind it |
Talk to the team about one market, one product line, and the decision due this quarter.
Talk to the team →