Where Robo Claw Fits at Large Banks and Financial Institutions: Choosing Your Rollout Candidates
Evaluate candidate workflows across eight dimensions — impact on customer funds, impact on customer rights and interests, whether a final decision is involved, handling of personal and credit information, external transmission, system writes, and whether human approval and rollback are possible — and prioritize low-risk, read-heavy workflows where a human makes the final call.
When choosing target workflows for a Robo Claw rollout, large banks and financial institutions should prioritize workflows that: (1) don't affect customer funds or transaction execution, (2) don't have AI making the final decision, (3) have clearly defined rules for handling personal and credit information, (4) keep external transmission and writes limited or approval-gated, and (5) preserve human approval and rollback. Workflows meeting these criteria are easier to validate in the Pilot and carry less risk.
Who This Is For
Who This Is For
For operations planning leads, risk management officers, compliance officers, and DX leads deciding which workflow to start a Robo Claw rollout with.
What You'll Decide
What You'll Decide in This Step
In this Discover step, you list candidate workflows for Robo Claw, prioritize them by risk, and decide on the single workflow to tackle first. Detailed requirements, permissions, and approval design happen in the next step, Refine.
Fit Check
Good Fit / Not a Good Fit
Good fit
- High-frequency, well-standardized workflows like internal policy/procedure search or inquiry classification
- Workflows like initial triage of contact-center inquiries, where a human can easily review and correct a first-pass suggestion
- Support workflows — like preparing underwriting or approval-request documents — that assume a human makes the final call
- Workflows that don't affect customer funds or transaction execution, even if they involve writes, as long as approval is required
- Workflows where the input data is internal documents or policies, making confidentiality easy to control
Not a good fit
- The final decision itself on loan approval, credit assessment, identity verification, or AML/fraud determination
- Workflows involving moving customer funds or executing transactions
- Workflows involving account-status changes or important customer notices sent without approval
- Workflows where the rules for handling personal and credit information aren't yet defined
Industry Challenges
Challenges Specific to Large Banks and Financial Institutions
Many institutions run into the following challenges at the stage of choosing target workflows.
Too many candidates to narrow down
Candidates range across policy search, inquiry handling, underwriting-document prep, alert triage, and more, making it hard to decide where to start.
No consistent way to gauge risk
Without a consistent framework for evaluating each workflow's risk level, selection tends to depend on who's making the call.
Unclear whether personal/credit data can be used
Only legal and compliance can determine the classification and permitted use of the data a workflow touches.
Uncertainty over workflows involving writes
There's no criteria for deciding whether workflows that update customer information or core data are fair game.
Method
Implementation Steps
1. List candidate workflows
Inventory internal workflows such as policy search, inquiry classification, underwriting-document review support, and alert triage.
2. Assess impact on customer funds and rights
Check whether a candidate workflow affects the movement of customer funds or customer rights and interests.
3. Confirm who makes the final decision
Confirm the design doesn't have AI making final decisions like loan approval, credit assessment, identity verification, or AML determination.
4. Confirm data classification
Check with legal and compliance whether the workflow touches personal, credit, or transaction data, and whether external transmission occurs.
5. Check for writes and human-approval needs
Work out whether the workflow updates core data and how much human approval needs to remain.
6. Decide on priority
Tabulate the evaluation results and decide on the single workflow to tackle first.
Evaluation Table
Candidate Workflow Evaluation Table
Below is an example evaluation table. Swap in your own candidate workflows. Workflows with less impact on customer funds and where AI doesn't make the final decision score higher priority in Discover.
| Candidate workflow | Impact on customer funds | Final decision | Personal/credit data | Writes | Human approval |
|---|---|---|---|---|---|
| Internal policy/procedure search | None | N/A | Not involved | None | Not required |
| Contact-center inquiry classification | None | Human | Limited | Internal only | Recommended |
| Flagging missing underwriting items | Indirect | Human | Involved | None | Required |
| AML alert initial triage | Indirect | Human | Involved | None | Required |
Data & Systems
Data and Systems Used
In the Discover step, check the state of data and systems like the following to evaluate candidate workflows.
Human-in-the-loop
Where Human Approval Is Required
No implementation happens in Discover, but when evaluating candidates, always assume a human makes the following decisions.
- The final call on whether a workflow may be selected as an automation candidate
- Whether to include workflows touching personal, credit, or transaction data as candidates
- Confirming the final decision hasn't been delegated to AI
- Upfront review by legal, compliance, and risk management
Measurement
Candidate KPIs
In Discover, record the following as hypotheses for each candidate workflow, to validate in later steps.
Current effort
Estimated human effort currently spent on the candidate workflow
Impact hypothesis
A hypothesis for the effort/time savings expected from automation
Risk score
A risk rating derived from impact on customer funds, final-decision ownership, data classification, and similar factors
Pitfalls
Common Pitfalls
Picking only workflows with obvious payoff
Skipping risk evaluation and picking only workflows with visible impact leads to redoing the governance design later.
Deferring data-classification review
Confirming how personal and credit information is handled only after picking a candidate workflow causes rework in later steps.
Starting multiple workflows at once
Running several workflows in parallel from the start complicates permission design and Pilot evaluation, dragging out validation.
FAQ
Frequently Asked Questions
How many workflows should we start with?
In most cases, we recommend starting with just one. Running several in parallel complicates permission design and Pilot evaluation.
Should workflows touching personal or credit information be excluded?
Not necessarily. But you should confirm data classification, purpose of use, and permissions with legal and compliance, and design the workflow so a human always makes the final decision.
Can loan-underwriting workflows be a candidate?
Support tasks like flagging missing underwriting items or drafting approval-request documents can be candidates, but we don't recommend delegating the loan-approval decision itself to AI.
Who should evaluate candidate workflows?
We recommend joint evaluation by the frontline operations-planning lead and risk management/compliance. The evaluation needs to account for both operational reality and risk/regulatory factors.
Continue
Next Step
Once you've chosen a workflow, the next step is to define its requirements, permissions, and approval design.
Let's map out your workflow selection together.
We can review current workload, risk, and data classification to identify Robo Claw candidates and priorities through a consultation on our official landing page.