Enquiry Triage & Eligibility Screening

Q: Do we still need a Compliance Officer to review cases?

Yes. The automation handles eligibility screening, but a Compliance Officer still performs identity verification and AML checks on qualified applicants before they move to underwriting.

Q: Can the automation work with our existing CRM and spreadsheets?

Yes. The automation integrates with HubSpot, Google Sheets, and other mainstream tools your team already uses.

Q: What if borrower data is incomplete or ambiguous?

The automation flags incomplete or unclear data and routes those cases to an operations officer for manual review. Staff can request additional information or make a judgment call on borderline cases.

Q: How quickly will we see results after launching the automation?

Most teams see throughput improvements within the first week as the automation handles routine screening. Staff time freed up can be redirected to complex cases or other priorities.

Gathering enough information to route an enquiry to the right counsellor and service type.

1.1k hrs

Manual time identified

Companies have mapped

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About This Automation

Enquiry triage and eligibility screening is the process of receiving borrower applications, extracting key information, and determining whether they meet lending criteria. Manual screening is slow, error-prone, and consumes significant staff time on repetitive data entry and policy checks.

Automation captures enquiry data automatically, evaluates borrowers against lending rules in real time, and routes qualified cases to underwriting within minutes. Staff focus only on exceptions and complex cases that require human judgment.

Key features:

Capture borrower information automatically from email, web forms, and phone intake

Evaluate eligibility against lending policy rules without manual spreadsheet lookups

Flag missing or ambiguous data that requires human review

Route qualified cases to underwriting and send rejection notices instantly

Create auditable case records with decision reasoning and timestamps

Reduce manual screening time from 62 minutes to under 10 minutes per enquiry

Data insights How it works Apps used What you get FAQ

Top friction points when done manually

The issues teams report most often with this process

#	Friction point	Companies Report This
1	Manual data entry from enquiries Borrower information is copied by hand from email or forms into spreadsheets and CRM, consuming 12 minutes per case and introducing typos.	80%
2	Policy rule lookups and cross-referencing Staff must manually check lending criteria against policy documents and spreadsheets, taking 15 minutes and risking inconsistent decisions.	67%
3	Contact detail validation delays Phone and email verification requires manual calls or test messages, adding 5 minutes and creating bottlenecks when contact is unreachable.	53%
4	Multi-system case creation Staff must log into CRM, create records, and send separate notifications to underwriting, fragmenting the workflow across tools.	40%
5	Inconsistent eligibility decisions Manual screening leads to 22% error rate and inconsistent application of lending rules across cases.	26%

Disclaimer

Automation readiness

How well-suited this process is for automation

Process Pain Score™

9.3/ 10

AI Fit Rating™

8.9/ 10

Automation Lift Index™

8.7/ 10

Hidden Overhead™

7.1/ 10

How The Automation Works

The full workflow, from trigger to completion.

1. Enquiry Receivedtrigger

New enquiry arrives via email, web form, or phone intake system and is captured in the inbox or intake queue.

2. Extract Data from Enquiry

Automation parses enquiry email or form submission and extracts borrower name, contact details, loan amount, purpose, and employment status into structured fields.

3. Verify Contact Details

Automation validates phone number and email format, and checks against known invalid patterns or blacklists.

4. Screen Against Eligibility Rules

The automation compares extracted data against lending policy rules: income thresholds, credit score, age, employment, and loan-to-value. flags any missing or borderline data for human review.

5. Meets Eligibility Criteria?

Decision node routes case based on assessment: qualified cases proceed to underwriting, borderline cases flag for manual review, ineligible cases are rejected.

6. Create Case

Automation creates a new case record with all extracted data, eligibility decision, and audit trail of checks performed.

7. Notify Underwriting

Automation posts qualified case summary underwriting channel with case link and key borrower details.

8. Send Rejection or Info Request Email

For ineligible or incomplete cases, automation sends templated email with rejection reason or request for additional documents, and logs outcome.

Most popular tool stack used

— the complete tool combinations companies use

52% of companies

28% of companies

12% of companies

Disclaimer

What you get when you map this process

Everything you need to understand, plan, and build your automation.

ROI and business case

What this process costs today and what changes once it's automated.

Launch schedule

What gets built, in what order, and what success looks like once it's live.

Process runbook

How the automation runs day to day, including exceptions and human decision points.

Developer handover pack

Full build spec, logic, and configuration — ready to hand off without a briefing call.

Integration and connections guide

Every tool connection, credential, and data mapping the build needs.

Test and QA plan

Every scenario checked and signed off before the automation goes live.

Frequently asked questions

Everything you need to know before mapping this process.

What happens to enquiries that don't meet eligibility criteria?

Ineligible cases are automatically flagged with the reason and a templated rejection or information request email is sent to the borrower. Operations staff can review exceptions and override decisions if needed.

Do we still need a Compliance Officer to review cases?

Can the automation work with our existing CRM and spreadsheets?

What if borrower data is incomplete or ambiguous?

How quickly will we see results after launching the automation?

View more FAQs

Process pain:9.3/10

Mapped by:8 Companies

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Page updated 10 August 2026