Financial Controller

The Challenge

At scaling SaaS companies, month-end variance analysis often becomes the bottleneck. The numbers are ready, but commentary takes days to finalise and is repeatedly revised as questions come back from the CFO or the board.

Traditional P&L reviews highlight outcomes — "Marketing £76k over budget" — but don't clearly separate expected growth effects from genuine efficiency issues, or surface governance failures early. As a result, finance teams spend time defending explanations instead of supporting decisions.

How I Approach Variance Analysis as an FC

I use a driver-based variance approach that separates each movement into:

• Volume effects — expected changes driven by growth
• Efficiency effects — rate deterioration or improvement requiring action
• Governance issues — spend that falls outside agreed approval or policy

This logic is applied consistently across cost areas and reviewed through a ranked, materiality-led lens so attention stays on what actually matters.

For example, instead of a generic explanation like "Marketing was over budget", the analysis produces:

"Marketing £76k over: £9k expected volume growth, £21k efficiency deterioration (CAC increase), and £35k unapproved TechCrunch spend requiring escalation."

Each element implies a different management response.

Governance and Review Discipline

Unapproved or late-added spend is explicitly flagged during analysis, rather than being discovered weeks later during manual review. These items are treated as control issues, not performance outcomes.

The focus is on clarity and defensibility — explanations that hold up when challenged, not just narratives that sound reasonable.

Role of Automation and AI

The analysis follows fixed, deterministic rules so results are consistent month to month. AI can be used to draft commentary from these validated outputs, but finance always reviews and owns the final narrative.

The process is designed to remain stable as volumes grow or tooling changes — logic evolves deliberately with finance policy, not accidentally through spreadsheet edits.

Where This Works Best

This approach works well in any business where costs are driven by measurable activity — including subscription and SaaS models, project-based organisations, technology-enabled services, and growth-stage companies with expanding teams.

Where operational drivers are clear, variance analysis can distinguish expected scale effects from efficiency deterioration quickly and consistently. This supports faster closes and more focused discussions, allowing finance to spend less time defending numbers and more time supporting decisions.

The Challenge

Month-end accruals are a judgment call wrapped in manual lookups. Finance teams cross-reference proposed accruals against forecasts, hunt down missing invoices, decide which variances need immediate investigation, and which can be capped. The process burns 2-4 hours of senior finance time every month, and critical items can slip through when you're juggling multiple entities or vendors.

My Approach

Built a Python tool that automates the comparison logic and flags exceptions that need human attention. The system cross-checks proposed accruals against forecast data, automatically caps amounts to budget where appropriate, blocks accruals when no forecast exists (protecting against surprise costs), and generates urgency-tagged summaries highlighting missing invoices that require immediate follow-up. Users can set manual overrides for approved exceptions. Available as both a web interface and command-line script, making it accessible to non-technical finance teams. Includes optional AI integration for natural language summaries of accrual decisions.

Key Insights

• Reduces month-end accrual processing from 2-3 hours to 20-30 minutes by automating forecast lookups and variance checks
• Urgency flagging immediately identifies which missing invoices to chase first (forecasted spend with no invoice = urgent)
• Automatic capping prevents over-accruing against budget while allowing controlled overrides for approved exceptions
• Works as both web interface and command-line script, making it accessible to non-technical finance teams
• Includes optional AI integration for natural language summaries of accrual decisions
• Supports CSV uploads for invoices, forecasts, and proposed accruals, making it adaptable to different ERP outputs

Business Impact

Finance teams close faster with fewer errors. The tool doesn't replace judgment—it eliminates the manual lookup work so finance professionals can focus on investigating real variances and making informed accrual decisions. Particularly valuable for lean finance teams managing multiple entities, frequent invoicing, or tight month-end windows.

The Challenge

As companies grow and come under external scrutiny, finance reporting changes. Metrics like ARR, burn multiple, CAC payback, and runway are no longer just internal tools — they are examined by boards and investors, often under tight time pressure.

At this stage, the challenge is rarely the absence of dashboards. It's disagreement over definitions, handling edge cases, and managing multiple versions of the numbers. Finance teams often spend days reconciling spreadsheets, recalculating metrics, and rewriting explanations — precisely when clarity and credibility matter most.

The Approach

Rather than building a full BI platform, this project demonstrates a governed reporting layer designed for moments of heightened scrutiny.

The system enforces:

• fixed metric definitions
• structured commentary prompts
• repeatable monthly outputs

Some inputs are automated, others remain manual — deliberately. The priority is not maximum automation, but consistency, traceability, and trust in the numbers.

AI is optional and downstream. When used, it assists with drafting commentary from validated inputs, while final review and approval always remain with finance.

Commercial Example (Illustrative)

For illustration, the dashboard shows a company with:

• ARR around £1.8 million
• burn multiple roughly 0.8x
• CAC payback just under 12 months
• gross margin near 78%
• approximately 12 months of runway

Together, these metrics show strong growth with strong efficiency, giving leadership real options on how to deploy capital.

The numbers are illustrative; the structure is real.

What This Demonstrates

• How finance can move from version chaos to a single, governed view
• Why locking definitions matters more than visual polish under pressure
• How AI can be introduced carefully, as a drafting aid rather than a decision-maker
• How leadership conversations shift from debating numbers to making decisions

Business Impact

For finance leaders, this approach reduces time spent reconciling and rewriting, and increases time spent on interpretation and decision support. Board materials become more consistent, investor discussions more confident, and internal alignment clearer.

While the example focuses on SaaS metrics, the underlying governance principles apply across business models — wherever finance needs speed without losing control.

The Challenge

Finance leaders face two extremes: paralyzed inaction ("AI is too risky") or reckless adoption ("let's try everything"). Most companies stall at "Should we pilot AI?" without frameworks to assess readiness, structure pilots, or scale with proper governance. Without clear decision protocols, pilots never progress to production, or worse, production lacks the controls auditors expect. Finance needs a complete roadmap from assessment through scaled implementation.

My Approach

Built a three-phase governance framework that provides the complete journey from evaluation to production-ready AI. Phase 1: Scored Readiness Assessment evaluates data quality, infrastructure, semantic layer, and process maturity across 4 dimensions (scored 0-100) to identify gaps before piloting. Phase 2: 90-Day Pilot Framework structures execution with weekly milestones, KPI tracking, cost monitoring, and clear decision gates (Scale, Pivot, or Kill at Week 12). Phase 3: Production Governance implements five enterprise control pillars - access management, validation protocols, audit logging, ownership assignment, and drift monitoring. The framework is illustrated through a detailed example scenario (TechFlow Solutions implementing Marketing Variance Commentary) showing realistic outcomes: 38% cycle time reduction, governance compliance scoring, and ROI analysis.

Key Insights

• Readiness Assessment prevents false starts - example shows scoring of 69.75/100, identifying specific gaps in API access and semantic layer that would block pilot success
• 90-day structured pilots with weekly KPIs prevent endless experimentation - framework demonstrates 38% efficiency potential (6.5hrs → 4.0hrs) with clear ROI evidence for scaling decisions
• Production governance isn't optional - Five control pillars (Access, Validation, Logging, Ownership, Drift) provide audit-ready framework that compliance teams trust
• Detailed examples with realistic metrics - illustrative scenario shows 45% cost savings potential ($450 → $247 per cycle), 93% accuracy targets (up from 85%), 2.3 month break-even on typical $8K investment
• Framework prevents both extremes - Structured enough to scale safely, pragmatic enough to avoid paralysis. Decision gates at every phase (Ready/Not Ready → Scale/Pivot/Kill → Compliant/Non-Compliant)
• Finance-led governance, not IT-imposed - Built by an FC who understands both finance risks and practical automation, controls are relevant without being bureaucratic

Business Impact

For finance teams, this framework removes the guesswork from AI adoption. CFOs get clear decision criteria at every stage: readiness scores show whether to start, pilot KPIs show whether to scale, governance audits show whether you're compliant. The three phases work together - assessment identifies gaps, pilots prove value with metrics, production scales with controls. The illustrative scenario demonstrates the complete path: readiness scoring (69.75/100 with identified fixes), 90-day pilot execution (38% efficiency potential with tracked costs), production governance implementation (90/100 compliance scoring ready for audit).

This isn't theoretical guidance or abstract principles. Each spreadsheet provides complete working examples with realistic numbers - use case scoring templates, weekly milestone trackers, cost calculation models, KPI dashboards, incident logging structures, and governance registries. Shows exactly how the framework functions in practice, what metrics to track, and how decisions get made at each gate. Built by an FC who's scaled finance at startups, these are practical controls designed for real-world application, not consultant frameworks created in isolation.

The Challenge

Finance teams field repetitive expense policy questions while employees struggle with vague guidance that leads to incorrect submissions and reimbursement delays. Generic AI chatbots without proper instruction give dangerous advice - approving non-compliant expenses or blocking legitimate claims. Poor AI implementation creates compliance risks: employees book non-refundable flights without required approval, expense both per diem and meal receipts (policy violation), or assume cost savings justify exceptions without checking actual policy rules.

My Approach

Built a side-by-side comparison tool that demonstrates how prompt engineering quality determines AI usefulness in financial operations. Users ask the same expense question twice - once to a poorly-prompted AI (generic instruction), once to an expertly-instructed version (comprehensive system with guardrails) - and see responses compared in real-time. The excellent prompt implements FC thinking: conservative defaults (deny unless policy explicitly permits), explicit approval hierarchies, edge case handling (policy gaps require exception approval), calculations with shown work, and structured responses with policy references. Built with 20+ years of expense policy experience across startups and corporates.

Key Insights

• Prompt engineering is as critical as model selection - same AI produces wildly different results depending on instruction quality
• Conservative defaults prevent compliance failures - "deny unless explicitly permitted" versus dangerous "seems reasonable so yes" approach
• Finance domain knowledge shapes guardrails - prompts must encode approval hierarchies, timing requirements, and common violations
• Structured responses drive compliance - explicit policy references and approval status sections prevent employees from acting on vague guidance
• Edge case handling matters most - poor prompts fail on ambiguous scenarios; excellent prompts ask clarifying questions before answering
• Production implementation requires security - proper API architecture and environment management distinguish proof-of-concept from production-ready solutions

Business Impact

This demonstrates AI as a finance efficiency tool with proper safeguards rather than risky automation. Finance teams reduce repetitive policy questions while maintaining compliance, employees get accurate guidance on complex scenarios, and the company avoids audit issues from poorly-implemented chatbots. Most importantly, it shows the right mindset: AI assists human judgment rather than replacing it, conservative defaults protect compliance, and domain expertise must shape technical implementation.

The Challenge

TechCrunch operated with a flat Chart of Accounts that created five critical failures: no departmental accountability (Heads of Department had no live spend visibility), unknown cost-to-serve (couldn't tag costs to service lines), efficiency blindness (no link between department costs and outcomes), untraceable cost overruns (spikes absorbed into generic GL codes), and audit risk (government contracts require structured project-level reporting). This reactive structure hindered strategic growth and placed grants at compliance risk.

My Approach

Designed a 6-segment multi-dimensional Chart of Accounts delivered over 6 months using an accelerated, data-led methodology. The structure includes: Entity (legal compliance), Natural Account (GL classification), Department (HoD accountability), Project/Contract (NHS grants tracking), Location (site analysis), and Service Line (product margin). Implementation follows a phased approach with scope lock after Month 1, build and configuration, migration rehearsals, and go-live hypercare support. Six non-negotiable de-risking controls ensure delivery: scope lock after M1, weekly governance, parallel run validation, user training completion gates, documented rollback procedures, and hypercare support.

Key Insights

• Multi-dimensional CoA delivers 95% of value on Day 1 - immediate real-time departmental spend visibility, project profitability tracking, and audit-ready reporting without gradual rollout
• Scope lock after Month 1 prevents scope creep - new requirements move to Phase 2 backlog, protecting the 6-month delivery commitment
• 8 weekly migration rehearsals de-risk go-live - parallel runs identify data quality issues, mapping errors, and user training gaps before production cutover
• Every transaction tells complete story - example code "001-60000-MKTG-NHS025-TRA-000" shows entity, cost type, department, contract, location, and service line in single string
• 85% reduction in manual journals - dimensional tagging at source eliminates month-end allocation exercises that consumed finance team hours
• Government contract compliance built-in - project dimension enables auditable cost tracking required for NHS grants without separate shadow systems

Business Impact

For organizations with complex cost structures, this transformation enables strategic decision-making previously impossible with flat CoA. Department heads gain real-time budget accountability, CFOs access unified profitability data for faster decisions, and service line margins inform pricing strategy. Government contracts achieve audit-ready compliance from Day 1, eliminating manual reconciliation exercises and compliance risk. The 6-month delivery methodology balances speed with control - scope lock prevents endless design cycles while weekly governance catches issues early.

This demonstrates financial infrastructure design capability beyond day-to-day FC operations. Shows understanding of both technical ERP configuration (dimensional structures, mapping logic, validation rules) and change management (user training, parallel runs, hypercare support). The de-risking controls reflect real implementation experience - scope creep, data quality issues, and user adoption failures are explicitly prevented through documented gates and governance cadence.