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I swear I’m working on it… right after I finish this project, feed the kid, answer 27 emails, and maybe sleep a bit.
Until then, try visiting from a bigger screen. It’ll be worth it—I promise!
Automated settlement
Designing trust in automated financial decisions
Problem
High-risk manual settlement with frequent inconsistencies and low trust in automation.
My role
Defined the system design and automation strategy for settlement workflows.
Solution
Human-centered automation with clear system states and reviewable decisions.
Impact
Fewer errors, faster operations, and higher confidence in automated outcomes.
What "Settlement" means in two worlds
In Financial Systems
Clearing financial obligations between parties.
It's about:
Confirming and reconciling transaction data.
Managing risk, timing, and compliance.
Guaranteeing transparency, accuracy, and auditability.
In Sports Trading
Resolving financial outcomes of an event.
It's about:
Validating event results across multiple sources (e.g. half-time and full-time scores)
Calculating winnings, losses, refunds, and cashout scenarios
Updating traders’ balances and system ledgers with accuracy and auditability
Why this mattered
In high-risk financial systems, automation only works when people trust it.
In sports betting settlement, traders need to feel in control of automated decisions. As Nielsen’s principle of user control and freedom suggests, trust is built when people understand what the system is doing and why.
Automated betting settlement is a high-risk operational workflow. Decisions are irreversible, time-sensitive, and directly tied to real money.
When settlement processes fail, the impact goes beyond internal operations. Delays, unclear outcomes, or missing payouts can directly affect how end users perceive the product and the brand behind it.
The goal was not to add more automation.
It was to support better decision-making by making automated outcomes clear, reviewable, and trustworthy for the traders responsible for them.
The problem
Settlement relied heavily on manual work in a system designed to operate at scale.
Traders were responsible for reviewing multiple markets per event, cross-checking half-time and full-time results, and resolving inconsistencies by hand. When errors occurred, recovery often happened after settlement, increasing operational risk and pressure.
Automation existed, but it behaved like a black box.
Outcomes were fast, but difficult to understand, review, or challenge.
The result was predictable:
What we learned from users
Through interviews and meaningfully shadowing traders and clearing officers, one pattern kept emerging:
Users wanted automation
But only if they could understand what the system was doing
And intervene when something didn’t feel right
Around 70% of errors came from inconsistencies between half-time and full-time logic.
This wasn’t a UI problem. It was a clarity, control, and trust problem.
Designing the system
The focus wasn’t on screens, but on how the system behaves.
We designed:
Explicit settlement states (Processed, In queue, Needs review)
Clear separation between half-time and full-time logic
Visible exceptions and conflicts
Contextual review without breaking the flow
Safe retry and recovery paths
Every status, label, and interaction existed to reduce uncertainty at the moment decisions were made.
STEP 1
Initiate Settlement with Intent
Automation begins only when the trader decides the event is ready.
STEP 2
Establish the Ground Truth
Clear, guided inputs define the final result before automation runs.
STEP 3
Validate Before Committing
The system proposes outcomes, highlighting what’s ready and what needs review.
STEP 4
Execute with Full Visibility
Progress is transparent, reducing uncertainty during a high-risk operation.
STEP 5
Monitor Live Resolution
Each market reflects its status in real time, reinforcing trust in the process.
STEP 6
Design for Recovery
Failures are surfaced clearly and can be retried without losing context.
STEP 1
Initiate Settlement with Intent
Automation begins only when the trader decides the event is ready.
STEP 2
Establish the Ground Truth
Clear, guided inputs define the final result before automation runs.
STEP 3
Validate Before Committing
The system proposes outcomes, highlighting what’s ready and what needs review.
STEP 4
Execute with Full Visibility
Progress is transparent, reducing uncertainty during a high-risk operation.
STEP 5
Monitor Live Resolution
Each market reflects its status in real time, reinforcing trust in the process.
STEP 6
Design for Recovery
Failures are surfaced clearly and can be retried without losing context.
STEP 1
Initiate Settlement with Intent
Automation begins only when the trader decides the event is ready.
STEP 2
Establish the Ground Truth
Clear, guided inputs define the final result before automation runs.
STEP 3
Validate Before Committing
The system proposes outcomes, highlighting what’s ready and what needs review.
STEP 4
Execute with Full Visibility
Progress is transparent, reducing uncertainty during a high-risk operation.
STEP 5
Monitor Live Resolution
Each market reflects its status in real time, reinforcing trust in the process.
STEP 6
Design for Recovery
Failures are surfaced clearly and can be retried without losing context.
STEP 1
Initiate Settlement with Intent
Automation begins only when the trader decides the event is ready.
STEP 2
Establish the Ground Truth
Clear, guided inputs define the final result before automation runs.
STEP 3
Validate Before Committing
The system proposes outcomes, highlighting what’s ready and what needs review.
STEP 4
Execute with Full Visibility
Progress is transparent, reducing uncertainty during a high-risk operation.
STEP 5
Monitor Live Resolution
Each market reflects its status in real time, reinforcing trust in the process.
STEP 6
Design for Recovery
Failures are surfaced clearly and can be retried without losing context.
Trade-offs we made
We avoided full automation of edge cases → Risk outweighed the efficiency gain
We didn’t hide complexity behind oversimplified UI → Transparency mattered more than surface simplicity
We didn’t optimize for speed alone → Control and confidence came first
Impact
+52%
task completion rate in settlement flow
−38%
reduction in average time spent per event detail view
↓44%
Error recovery time
>50%
reduction in third-party settlement costs
What this reinforced for me
People trust automation only if they understand what is happening.
Designing for automation requires systematic thinking, not just an interface designer.
Good design doesn’t remove complexity.
It makes complexity safe to operate.
Let’s connect
Thanks for reading! If you’re hiring, collaborating, or just curious about my work — feel free to drop me a line. I’d love to hear from you.
