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case study — process automation & decision logic

From Manual Reviews to
Rules-Based Auto-Approval

I identified, designed, and led the implementation of an internal application that automated the approval process behind billing credits for customer disputes. It replaced a fully manual, spreadsheet-based workflow with a centralized application connected directly to the company's data systems — cutting approval turnaround from ~2 business days to 2–3 minutes on hundreds of requests per month.

Rules-based decision engine Direct data integration Process automation UAT · change management
 Internal enterprise project. Details have been generalized to protect confidential information — no proprietary data, code, screenshots, or documentation are shared. Process figures are approximate.
 My role: business owner and solution designer — I conceived the idea, analyzed the data, and designed the full decision logic, validation rules and workflow. The internal IT team developed the technical build from my functional design.
2–3 min
approval turnaround, down from
~2 business days (~99% reduction)
100s
requests per month
0
manual reviews when all
validation checks pass
Full
searchable, auditable
approval history

01 — The problem

Two days of process around a two-minute decision

Before a billing credit could be issued, every customer dispute had to be validated. The ~2-day turnaround wasn't caused by the decision itself — it was everything around it: manual data retrieval, manual recalculation, and email-based handoffs accounted for nearly all of the elapsed time.

Where the time went

Every step was manual

Every request was checked by hand, retrieving the relevant records one at a time. The correct values had to be recalculated manually for each case. Data, calculations and approvals lived in scattered spreadsheets and email handoffs — with no centralized record or audit trail.

What it cost

Slow resolution, inconsistent decisions, revenue risk

End-to-end approval took around 2 business days — delaying dispute resolution, degrading customer experience, and tying up team capacity. Manual recalculation increased the risk of inconsistent decisions and revenue leakage, and the lack of an audit trail made decisions hard to defend after the fact.

The insight. All three time drivers — manual data retrieval, manual recalculation, and email-based handoffs — were candidates for elimination through direct data integration and rules-based decisioning. The decision could be automated because the data to make it already existed in the company's systems.

02 — The solution I designed

Direct data integration + a rule engine that decides

An internal application that centralizes the entire approval workflow and automates the decision itself. When all validation checks pass, the request is approved automatically — agents simply process the credit using the approved values.

01SubmitA single request, or a bulk template upload to process many in one run.
02RetrieveOfficial records pulled automatically from the company's data systems — no manual lookups.
03ValidateCustomer-submitted values compared against official system data using configurable thresholds.
04CalculateThe rule engine determines the correct billable values for the request.
05Auto-approveAll checks pass → approved automatically, with no manual review. Exceptions are routed for handling.
06RecordEvery decision stored in a searchable, auditable central history.

Bulk processing

From one-at-a-time to one run

A template-based upload lets agents validate multiple requests in a single run instead of one at a time — the throughput multiplier on top of the per-request speedup.

Searchable approval history

An audit trail that didn't exist before

Every decision is stored centrally and fully retrievable — a complete, auditable record replacing local files and email threads.

03 — My role

From bottleneck analysis to functional design to rollout

I owned the solution end to end on the business side — the IT team built from my functional design, with me acting as business owner throughout development, testing, and rollout.

Analysis & design

Turning process knowledge into decision rules

Identified the automation opportunity by analyzing bottlenecks and cycle-time drivers in the existing workflow. Analyzed historical data to define validation thresholds, tolerance ranges and expected-value logic. Designed the end-to-end process flow, business logic, calculation rules, auto-approval criteria and exception-handling paths.

Delivery & adoption

Requirements, testing, training

Specified the data requirements and matching conditions for the integration, translating business rules into functional requirements for the development team. Led user acceptance testing, validating application output against independently calculated expected results. Wrote the user documentation and trained the operational teams on the new tool.

04 — Impact

~99% faster, standardized, and fully auditable

Applied to hundreds of requests per month, the application removed the manual review entirely for passing requests and left an auditable record of every decision.

Before / after

The same decision, without the process around it

MetricBeforeAfter
Approval turnaround~2 business days2–3 minutes (~99% reduction)
Approval decisionManual review by agentsAutomated, rules-based
Data retrievalManual lookups, one by oneDirect data integration
ProcessSpreadsheets, email handoffsCentralized application
ThroughputOne request at a timeBulk validation in a single run
Audit trailNone (local files)Full searchable history
Beyond speed. The project standardized approval decisions across the team, reduced the risk of incorrect credits and revenue leakage, and freed agent capacity for higher-value work.

05 — Skills demonstrated

Business analysis meets decision automation

Process analysis & redesign Data analysis for decision-rule design Requirements-to-logic translation Data validation & exception handling Decision automation Billing & revenue assurance Data integration Cross-functional collaboration with IT UAT · documentation · training
Related project. A reproducible, open-source exploration of similar reconciliation and anomaly-detection logic — built on synthetic data with full code available — is documented in the Billing Reconciliation & Anomaly Detection Engine →