---
title: Manual review optimization: How redirecting manual review increased annual profit
source_page: https://www.sardine.ai/media/fraudology/episodes/manual-review-optimization
canonical: https://www.sardine.ai/media/fraudology/episodes/manual-review-optimization
format: text/markdown
date: 2023-07-20T00:00:00Z
description: Manual review optimization can improve fraud speed, forecasting, and profit, exposing the workflow shifts teams should take much more seriously right now
---

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# Manual review optimization: How redirecting manual review increased annual profit

**Published:** 2023-07-20T00:00:00Z

Manual review optimization can improve fraud speed, forecasting, and profit, exposing the workflow shifts teams should take much more seriously right now

Today we are talking about manual review optimization and what can happen when a fraud team stops treating manual review like a fixed function and starts using it as a strategic advantage.
I sat down with Shawn Kelley, former Director of Payments and Risk at SeatGeek, to talk about a problem a lot of ecommerce companies know all too well. The fraud system was missing orders, the feedback loop was taking far too long, and by the time fraud chargebacks showed up, the damage was already done. For teams relying on rules engines, supervised machine learning fraud models, or tools that need to learn from past mistakes, that lag can be incredibly expensive.
What I really like about this conversation is that Shawn and his team did not solve the problem by adding more complexity just for the sake of it. They rethought the manual review process itself. They redirected where fraud analysts were spending their time, changed the role manual review played in their fraud operations, and ended up seeing a whole lot more value than they expected.
And that matters.
Because manual review optimization is not just about reviewing transactions more efficiently. It is about creating a faster fraud feedback loop, improving fraud analyst productivity, increasing fraud operations efficiency, and giving the business better visibility into what is really happening. In this case, it also led to significant annual profit growth, better forecasting, and a stronger team culture.
What you’ll hear in this episode:
How manual review optimization helped shorten the fraud feedback loop from weeks to hours
Why redirecting the manual review process improved supervised machine learning fraud performance
How faster fraud detection supported stronger chargeback detection improvements
Why fraud analyst workflow changes can increase both fraud operations efficiency and team satisfaction
How fraud forecasting accuracy improved when the team had better visibility into emerging fraud attacks
You should listen to this episode if you:
Lead a fraud team and want to improve manual review optimization without adding unnecessary complexity
Rely on supervised machine learning fraud tools or rules engines that need faster feedback
Want to improve fraud analyst productivity and fraud operations leadership
Care about fraud loss prevention, chargeback detection improvements, and annual profit growth
Are open to rethinking the way your fraud team has always operated in order to test better outcomes
If you liked this episode, be sure to subscribe and review the podcast on iTunes, Spotify, YouTube, or wherever you listen to podcasts. It really helps with getting the word out.
### Episode notes & key takeaways
Why manual review optimization can improve more than just fraud decisions
Let’s break this down.
One of the biggest takeaways from this episode is that manual review optimization is not just about catching more fraud. It is about rethinking the role of manual review inside the broader fraud operation. Shawn shares how the original challenge was not just missed fraud. It was the slow feedback loop on orders the fraud system failed to identify. By the time those missed attacks showed up as fraud chargebacks, it was often weeks later.
That kind of lag creates real problems for teams using tools that need current feedback to improve. Rules engines stay stale. Supervised machine learning fraud systems learn too slowly. Fraud attacks continue longer than they should. By redirecting manual review to focus on identifying emerging fraud patterns faster, the team created a more responsive system overall.
Here is what is actually changing:
Manual review optimization can shorten the fraud feedback loop dramatically
Faster learning improves the effectiveness of supervised machine learning fraud systems
Chargeback detection improvements become easier when signals are found earlier
Fraud loss prevention gets stronger when teams reduce the time between fraud activity and response
Why fraud analyst workflow matters more than most teams realize
Here’s what’s actually happening.
A lot of companies treat fraud analyst workflow like a fixed operational process. Orders come in, analysts review them, and the team moves on. But this conversation makes a strong case for challenging that assumption. When Shawn and his leadership team redirected the focus and placement of their analysts, they found that the team could create value in ways that went well beyond transaction review.
That shift improved fraud analyst productivity because the team was not just processing cases. They were helping identify fraud attacks faster, contributing to better system learning, and creating insights the wider business could actually use. It also improved morale. And honestly, that part matters more than many leaders admit. Happier analysts usually mean stronger retention, better judgment, and a healthier fraud team culture.
Fraud analyst workflow should be designed around impact, not habit
Fraud team restructuring can uncover value beyond individual case review
Fraud analyst productivity improves when work is connected to bigger outcomes
Fraud operations leadership includes building a team structure people can thrive in
How faster fraud detection supports profit and forecasting
This is the part that really stands out.
We talk a lot in fraud about saving losses, but Shawn explains how this approach did much more than that. By creating faster fraud detection and a tighter feedback loop, the company was able to increase annual profit by a massive amount, not just improve revenue on paper. That distinction matters. Because stronger fraud operations are not only about reducing loss. They are also about helping the business make smarter, more profitable decisions.
Another important outcome was improved fraud forecasting accuracy. Once the team had better visibility into emerging attacks and better data flowing through the system, finance could build more accurate forecasts. That is a huge operational benefit. And it is a great reminder that ecommerce fraud strategy does not live in a vacuum. Better fraud decisions can strengthen planning across the company.
Faster fraud detection can drive meaningful annual profit growth
Fraud forecasting accuracy improves when risk signals are identified earlier
Fraud operations efficiency has real value beyond the fraud team
Ecommerce fraud strategy is stronger when fraud and finance are better aligned
Why testing a better process can unlock surprising results
One of the reasons I wanted to share this episode is because the lesson applies far beyond this exact use case. Not every company has the same fraud stack, review queue, or team structure. But almost every company has some version of a process that exists mostly because that is how it has always been done.
What Shawn and his team did was question that. They tested a new way of working, paid attention to the outcomes, and uncovered benefits they had not even fully expected at the start. That is the kind of thinking great fraud leaders bring to the table. Manual review optimization is not always about automation alone. Sometimes it is about redirecting talent, changing incentives, and giving the team a more strategic role in the fraud operation.
Fraud review automation is not the only path to better outcomes
Testing process changes can reveal opportunities hidden in plain sight
Fraud team restructuring can improve both business results and team experience
Strong leaders challenge default workflows when they are no longer serving the goal
The big takeaway from this episode is pretty simple. Manual review optimization can do a lot more than speed up case handling. When done thoughtfully, it can improve fraud detection, strengthen machine learning feedback loops, support better forecasting, increase profitability, and create a stronger team in the process. That is exactly why this conversation with Shawn is worth paying attention to.