The Human Touch in a Digital Watchdog: Why Manual Verification is Brolly’s Safety Net

In the world of peer-to-peer lending, Brolly’s mission is clear: make finance fairer, faster, and more accessible. To achieve this at scale, we rely on FrankieOne to automate the vast majority of our identity verification and compliance checks.

However, even the most sophisticated engines hit a "grey zone." While FrankieOne is the shield that deflects thousands of risks in milliseconds, manual verification remains the sword we use to resolve the complex cases.

Here is why the human element is indispensable in our AML (Anti-Money Laundering) and PEP (Politically Exposed Persons) checks, and how we balance automation with insight.

Automated systems are designed to be risk-averse. If a new borrower is named "Sarah Connor," the system might flag her because a "Sarah Connor" appears on a sanctions list in another country.

• The Automation (FrankieOne): Instantly catches the name match to ensure no risk slips through. It pauses the onboarding to prevent a sanctioned individual from entering the ecosystem.

• The Manual Check: A Brolly analyst steps in. Using the FrankieOne portal, they view the flagged profile and quickly verify the date of birth, location, and citizenship. They determine that our Sarah Connor is a legitimate borrower in Brisbane, not the sanctioned individual.

• The Impact: Without this manual review, a legitimate user would be unfairly blocked. Manual verification ensures we don't sacrifice user experience for the sake of compliance.

Identifying a Politically Exposed Person (PEP) is easy for a machine; understanding the risk they pose is harder.

• The Scenario: FrankieOne identifies a potential lender as the family member of a foreign diplomat.

• The Manual Check: The system flags the relationship, but a human must assess the context. Is the diplomat from a high-risk jurisdiction? Is the lender’s source of funds consistent with their employment, or is it unexplained?

• The Nuance: Not all PEPs are "high risk." Manual verification allows Brolly to differentiate between a local town councillor with a modest income (low risk) and a high-level official moving large sums of money (high risk).

Money launderers rarely use their own names. They hide behind layers of shell companies, trusts, and nominees.

• The Automation: FrankieOne peels back the first few layers by automatically checking UBO (Ultimate Beneficial Owner) registries and visualizing the ownership tree.

• The Human Role: When the trail leads to a "dead end," a circular ownership structure, or a trust deed that isn't digitized, a human analyst is needed to dig deeper. They interpret the complex data FrankieOne aggregates to make the final "Go/No-Go" decision.

The goal of using FrankieOne is not to replace the human, but to make the human faster. By automating the obvious 90% (clear passes and clear fails), it frees up the Brolly team to spend their energy on the complex 10%.

FrankieOne assists our manual process by:

• Single Customer View: Presenting identity docs, watchlist hits, and fraud scores in a single dashboard. Our analysts don't need to tab between five different websites to find the truth.

• Audit Trails: When a Brolly analyst manually clears a flag (e.g., "Confirmed False Positive"), FrankieOne automatically records who made the decision and why. This ensures we are always audit-ready for regulators.

• Smart Workflows: If a manual check is required, the system routes the profile to the correct review queue, ensuring high-priority cases are seen first.

In the fight against financial crime, technology is essential, but human judgment is irreplaceable. We use FrankieOne to handle the scale of modern data, but we rely on manual verification to handle the nuance. It is the combination of automated speed and human insight that keeps the Brolly community safe.