For banks, credit unions and insurers
Know why your AI agent decided.
Show your auditors and examiners.
Your agents can approve. People still own every decline, filing and final call, because no one can show why the agent decided. Wander traces each decision to the reasons that drove it and tests them against your written policy, so you can hand agents more of the work.
Built for lending, collections, BSA and underwriting decisions.
dec_7q2f9c
Decline auto loan application LN-4471
- agent
- lending-agent
- loan
- Used auto · $24,500 · 72 mo
- reason
- Debt-to-income too high
Tested against policy
- Credit score above 640 minimum
- Term within used-auto limit
- DTI is 38%, under the 45% max
- In bounds
lending-agent
Approve auto loan LN-4472 for $18,000
- In bounds
collections-agent
Set payment plan for member #20817
- Out of bounds
bsa-agent
File SAR on account #5530
The problem
Your agents can approve.
People still own every decline.
Lenders already let automation approve and fund loans. But anything that could be an adverse action, a regulatory filing or a final call stays with a person, because no one can show why the agent decided, or that its reasons hold up.
The moment an agent’s output is final, it stops being a tool and becomes a model. Then someone has to validate it.
01
Adverse action stays with people
Declines, counteroffers and regulatory filings carry legal weight. If you can't show the reasons behind them, a person has to make the call.
02
A tool becomes a model
Once an agent's output is final, it needs validation and ongoing monitoring like any other model, and the evidence for that doesn't exist yet.
03
Spot checks don't scale
Teams audit a sample of agent decisions by hand, in spreadsheets and call reviews. It's slow, it misses things, and it caps what the agent can do.
How it works
Follow one decision through Wander.
Here’s what your team, and your auditors, would see for a single auto loan decision.
For every decision, you get
- The why
- The factors that actually moved the outcome. Not the model narrating itself.
- The test
- Deterministic checks against your written policy: approved factors, limits, and whether the stated reasons hold up.
- The record
- One your auditors and examiners can follow from reasons to outcome to policy, instead of a hand-checked sample.
Observability shows you what an agent did. Wander shows whether it was allowed to.
- 1
Your agent makes a decision
A lending agent declines an auto loan and cites debt-to-income. Today, that decline waits for a person, or gets spot-checked after the fact.
Decision · lending-agent
- case
- LN-4471
- loan
- Used auto · $24,500 · 72 mo
- outcome
- Decline
- reason
- Debt-to-income too high
- 2
Wander traces the reasons to the outcome
Each factor the agent relied on is linked to the result it pushed toward, so you can see what actually drove the decision, not just what the agent said.
Reasons · dec_7q2f9c
- Credit score 702→ Approve
- Debt-to-income 38%→ Approve
- Term within used-auto limit→ Approve
- Time at address: 4 months→ Decline
why: time at address drove the decline, not debt-to-income
- 3
Tested against your policy
Deterministic checks against your written loan policy: approved factors, limits, and whether the reason given is the reason that drove the outcome.
Policy · consumer loan policy v12
- ✓Credit score above 640 minimum
- ✓Debt-to-income within 45% maximum
- ✕Stated reason doesn’t match what drove it
- ✕Time at address isn’t an approved factor
- 4
A record your auditors can follow
In bounds, the decision goes out with its record. If the reasons don't hold up, a loan officer sees why before any adverse action notice goes out. Your auditors and examiners see the same record you do.
Record
In boundsReviewOut- 14:02:11
- held for loan officer review
- 14:09:30
- approved at $24,500 · d.morales
- 14:09:31
- no adverse action notice sent
- sealed
- sha256:9c1e…a04b
How you roll it out
Start in shadow.
Expand when the record says so.
You don’t have to trust an agent on day one, and nothing changes in production to start. Give each agent more authority only when the evidence, and your auditors, support it.
Stage 1
Shadow
Nothing goes out
Run Wander in shadow alongside your agents, with no change in production. You get the why and a policy test for every decision, instead of a hand-checked sample.
Stage 2
Approve
Your team signs off
Decisions go out once someone on your team approves, with the reasons and policy tests in front of them. A real check, not a rubber stamp.
Stage 3
Bounded
Exceptions reviewed
Decisions that pass every test go out on their own, and your team only sees the exceptions. Approvals first. Declines and filings last.
Stage 4
Expanding
Authority grows
As the record builds, your risk team widens what each agent may decide, with the evidence to show your auditors and examiners.
Who it’s for
Built to get through your AI committee.
Everyone who reviews a new agent sees the same record.
Business leaders
Agents take on the work your team double-checks today. You only see the exceptions.
CIO and CTO
One record for every agent, so new use cases get approved faster.
Risk and compliance
The reasons behind every decision, tested against policy and ready for validators.
Security and legal
A clear view of what each agent decided and why, for your vendor review.
Your policy, written as bounds.
Every decline carries the specific reasons that actually drove it, and they have to hold up against policy.
Loan decisions stay inside the board-approved lending policy. Exceptions go to a loan officer.
Agents can draft a SAR and disposition routine alerts. Only an analyst can file.
Underwriting decisions never rest on a name, a ZIP code or any other proxy for a protected class.
For your auditors and examiners
An answer to “why did the agent decide that?”
Every decision becomes a record your auditors, validators and examiners can follow, from reasons to outcome to the policy it was tested against. Agent activity shows up in your next audit or exam as evidence, not as a finding.
Wander produces evidence that supports these requirements. It doesn’t make an organization compliant on its own.
- ECOA / Reg B
- Specific, accurate reasons for every adverse action, traced to what actually drove the decision, and evidence that no prohibited basis did.
- Model risk
- Once an agent's output is final, it's a model. Wander produces the outcome evidence and ongoing monitoring validators ask for. SR 26-2 leaves agentic AI to your own risk management.
- Third-party risk
- A record of how each vendor-built or in-house agent decides, for the review every new agent and use case goes through.
- BSA / AML
- How each alert was dispositioned, on what basis and who filed, so agents can draft while analysts decide.
- NIST AI RMF
- Evidence across Govern, Map, Measure and Manage, per agent and per decision.
Questions
What your risk committee will ask.
Isn't this just the model explaining itself?
No. A model's explanation of its own decision isn't proof. Wander traces the outcome to the factors that actually moved it, then checks those against your written policy deterministically.
How is this different from observability?
Observability shows what an agent did. Wander shows whether it was allowed to: the reasons behind each decision, tested against your policy, with a record your auditors can follow.
How do you show a decision didn't rest on something it shouldn't?
Because reasons are tied to the outcome, you can see which factors actually drove it, including ones the agent didn't mention. Each decision is tested against your approved factors, prohibited ones and their proxies, and the result is recorded with it.
Do our agents have to make final decisions on day one?
No. Most teams start in shadow: agents decide, Wander records why and tests it against policy, and nothing goes out. Approvals get handed over first, adverse actions last.
Who is Wander for?
Banks, credit unions and insurers whose agents could take on lending, collections, BSA or underwriting decisions, but whose people still double-check them by hand.
Does Wander make us compliant?
No tool can. Wander produces the evidence auditors and examiners ask for: why each decision was made, which policy it was tested against and what happened when it failed.
Get started
See it on your own decisions.
Tell us about a decision your team still double-checks by hand. We’ll walk you through how Wander would trace its reasons and test them against your policy.
Design partners
We’re working closely with a small group of banks, credit unions and insurers. Design partners get:
- Your policy encoded with your risk and compliance teams
- Start in shadow against your real agent decisions
- Records shaped for your auditors and examiners
- A direct line to the founders