Financial Services · Wealth & advisory firm, UK (85 staff)
Reporting Automation for a Financial Services Firm
92% first-pass accuracy · 95% time saved
A UK wealth and advisory firm spent hundreds of analyst hours each month assembling client reports from six systems. We automated the pipeline end to end — data gathering, reconciliation, narrative drafting and compliance checks — cutting assembly time 95% and achieving 92% first-pass accuracy, with every pack still signed off by a human.
- reduction in report assembly time
- 95%
- first-pass accuracy on generated packs
- 92%
- month-end reporting cycle
- 4 days → 3 hrs
- analyst hours returned per quarter
- 400+
The challenge
Every month, the firm’s analysts assembled client reporting packs: valuations, performance, transactions, fee summaries, and a written commentary — drawn from a portfolio system, a CRM, two custodian feeds, a fee engine and a market-data terminal. None of the six systems agreed with each other by default. The work consumed the back office for the first four days of every month, and quarter-ends were worse.
The hidden cost wasn’t just the hours. Manual assembly meant manual reconciliation, and discrepancies surfaced late — occasionally after a pack reached a client. Analysts hired for their judgement spent their time on copy-paste, and the firm’s growth plan implied hiring more of them to do more of it. The COO’s brief to us was precise: same packs, same sign-off discipline, a fraction of the elapsed time.
The solution
We built a reporting automation pipeline in three layers. The data layer pulls from all six systems on schedule, normalises entities, and runs a reconciliation engine that flags disagreements between sources before anything is assembled — inverting the old process, where discrepancies were found by whoever happened to notice. The assembly layer populates the firm’s own report templates and drafts the written commentary with a language model constrained to reference only reconciled figures; every generated sentence links to the data behind it. The control layer runs compliance checks (mandatory disclosures, tone rules, client-suitability language) and queues each pack in a review workbench where an analyst approves, edits or rejects with full visibility of flags.
The architecture
The pipeline runs in the firm’s cloud environment with UK data residency. Deterministic code owns every number — extraction, reconciliation, calculations — while the language model only ever writes narrative around figures it is handed, a division of labour that eliminates hallucinated numbers by construction. Model outputs are validated against a rule set (no unsupported claims, no advice language outside permitted scopes) and the whole run is logged: source snapshots, model version, checks passed, reviewer decisions. That audit trail satisfied both the compliance function and the firm’s external auditors at the first annual review.
The results
Month-end assembly fell from four days to roughly three hours of elapsed pipeline time plus review. First-pass accuracy — packs approved with zero corrections — reached 92%, and the reconciliation engine now catches source discrepancies the manual process historically missed. The firm recovered more than 400 analyst hours a quarter, redeployed to client-facing analysis, and absorbed a 20% increase in client count the same year with no additional reporting head count. Delivery took 12 weeks inside our standard 90-day engagement structure.
What this means for your firm
Every regulated business has a version of this: recurring packs assembled from systems that don’t talk, checked by people who could be doing better work. The pattern here — deterministic data handling, AI-drafted narrative, human sign-off, audit trail throughout — transfers to management reporting, client valuations, fund fact sheets and board packs. Book a strategy call and bring last month’s pack; we’ll walk through exactly which layers automate and what the cycle time becomes. Or start with our cost guide for AI automation.
Questions clients ask about this project
How do you get to 92% accuracy — and what about the other 8%?
The 92% is first-pass: packs that sail through human review with no corrections. The rest are flagged by the system’s own reconciliation and confidence checks before a reviewer sees them — mostly data-source discrepancies the old process would have missed entirely. Nothing reaches a client without an analyst’s sign-off, so the published error rate is effectively unchanged from zero.
Was the FCA compliance angle a problem?
The opposite — it shaped the design and became a selling point internally. Every figure in a generated pack traces to its source system and timestamp, every narrative sentence is grounded in a data point, and the review-and-sign-off workflow produces exactly the audit trail the compliance team wanted and the old copy-paste process lacked.