Case Study • Financial Services
AI-Assisted Operational Reporting
An operations team transformed recurring reporting from manual assembly into an AI-assisted review workflow with anomaly detection and auditability.
By Phil Maher • Published
Reporting Effort
15–20h to 2–3h
Cycle Consistency
+42%
Issue Detection
High-severity flags surfaced early
Business Context
The client produced weekly and monthly performance reports by manually aggregating data from multiple systems, writing narrative summaries, and cross-checking inconsistencies in spreadsheets.
Pain Points
- Analysts spent most of their time assembling reports, not interpreting them.
- Narrative quality varied by author and deadline pressure.
- Outlier detection depended on manual spot checks and was inconsistent.
Implementation Approach
I built a reporting pipeline that joined source data, computed standardized KPIs, drafted narrative summaries, and highlighted anomalies for human sign-off. The system produced a draft package that analysts reviewed and approved instead of building from scratch.
Control and Compliance Design
- Deterministic metric calculations before language generation.
- Prompt templates constrained to approved business vocabulary.
- Approval checkpoints with revision history for each reporting cycle.
- Anomaly thresholds tunable per business unit and report class.
Timeline
- Week 1: KPI catalog and baseline reporting map.
- Weeks 2–3: data integration and anomaly-scoring module.
- Week 4: narrative-generation and QA workflow implementation.
- Weeks 5–6: parallel runs, acceptance testing, and cutover.
Outcome
Reporting effort dropped from 15–20 hours per cycle to roughly 2–3 hours of high-value review. Decision makers received more consistent reporting packs, and analysts shifted toward interpretation and action planning instead of manual document production.
Want Better Reporting Throughput?
If your team still rebuilds reports by hand every week or month, you likely have a strong automation and augmentation opportunity.