--- Brand: klarmetrics.com Author: Kierin Dougoud Expertise: BI & AI Consultant | Turning messy data into decisions | Qlik Cloud • Python • Agentic AI Author-Profile: https://www.linkedin.com/in/mkierin/ Canonical-URL: https://klarmetrics.com/hidden-money-audit/ --- # Hidden Money Audit A fixed-scope diagnostic that finds the CHF 200K to CHF 1.5M hiding in your data. Programmatic probes, AI-ranked findings, quantified punch list in 2 to 4 weeks. You have dashboards. Finance has a monthly close. The CFO reviews the numbers every week. And yet there is a recurring feeling that the numbers are telling a partial story. That feeling is usually right. Most companies above CHF 20M in revenue have between CHF 200K and CHF 1.5M in quantifiable losses sitting in their data that no standard report surfaces. Not fraud. Not bad actors. Structural gaps between what the data knows and what the decisions use. The **Hidden Money Audit** is a fixed-scope diagnostic built to find those gaps, put a CHF number on them, and hand you the list. **Key Insight:** Most companies above CHF 20M have CHF 200K to CHF 1.5M in quantifiable losses sitting in their data. The question is never whether. It is how much. # What the Audit Finds The audit covers nine categories of Hidden Money. Every engagement surfaces findings across multiple categories. The CHF ranges below are typical for companies between CHF 20M and CHF 150M in annual revenue. Working capital trapped in slow cycles DSO creep and DPO underuse locking preventable financing costs. A company paying suppliers 17 days early against net-45 terms is giving away cash on every payment run, every week, with no one tracking it. CHF 200K to CHF 800K Revenue leakage across five patterns Customers generating revenue but destroying margin, discount policies applied without CHF impact tracking, terms negotiated but not enforced. CHF 150K to CHF 600K per year Margin erosion invisible at blended level Gross margin looks fine until you segment by product line, region, or customer tier. The blended number hides a distribution where 20% of the portfolio drags the rest. 2 to 5 percentage point gap, top vs. bottom segment Cost gaps vs. internal peers In multi-entity structures, entities spending 40 to 60% above the group median on comparable cost categories. The gap is invisible in consolidated reporting. The [Alpen Gruppe case study](/hidden-money-case-study/) found CHF 480K in excess costs at one entity this way. CHF 100K to CHF 500K per entity Forecast blind spots and budget overruns Cost lines running over plan for three consecutive months with no alert. Not because the data is missing but because nobody is watching the combined number. The case study found CHF 270K in Q1 overruns that the consolidated profit figure absorbed entirely. CHF 50K to CHF 300K undetected per quarter Pricing drift Contracted prices not enforced at the transaction level. Volume discounts applied inconsistently. Invoice amounts that do not match what was agreed. Most businesses have this. Almost none track it systematically. CHF 80K to CHF 400K per year Duplicate payments and AP anomalies Same invoice paid twice, slightly different vendor names, amount or date variations that clear manual review. CHF 20K to CHF 150K typical recovery DSO creep and AR concentration Aggregate DSO looks acceptable while 30% of receivables are concentrated in accounts 45 days past due. The average hides the distribution. The distribution is where the cash is. CHF 100K to CHF 600K in trapped cash Inventory dead stock The bottom 20% of SKUs by movement typically holding 40 to 60% of inventory value. At a 5% cost of capital on a CHF 1.2M slow-moving position, that is CHF 60K per year in carrying cost with no invoice attached to it. CHF 30K to CHF 200K annual carrying cost Not every category produces a finding in every engagement. Some do. Some do not. That depends on your business model, data quality, and what your existing reporting already surfaces. The probe checks all of them. You see only what is actually there. # How It Works The audit runs in five phases. Fixed scope. No open-ended consulting work baked in. Phase 1 · 1 hour Scope call Before any data access, we align on what to probe. Business model, revenue size, existing BI stack, data sources available, and which categories of Hidden Money are most likely given your industry and structure. This call determines whether the engagement makes sense and what the probe set will cover. Phase 2 · 2 to 5 days Data access and model review You provide read-only access to your BI environment or data exports from your ERP. No production system access required in most cases. We map the data model, identify field availability for each probe category, and flag any data quality issues before the analysis runs. Phase 3 · 3 to 7 days Programmatic probe run 40+ probes run against your data simultaneously. Each probe is a structured check with a defined threshold: budget variance by cost line and entity, supplier payment timing vs. contractual terms, DSO by customer tier, inventory movement rate by SKU, margin by customer and product segment, cost outliers vs. internal peer groups. Every probe that returns a signal is logged with the raw data behind the flag. Phase 4 · 2 to 3 days AI-assisted ranking and sizing Findings are ranked by two dimensions: estimated CHF impact and effort to fix. A duplicate payment recovery and a pricing drift issue may both flag. But a CHF 400K working capital improvement from DPO realignment takes one policy change. A CHF 150K margin recovery from customer repricing takes six months of sales work. The ranking tells you where to act first. Phase 5 · delivery Report and debrief You receive the findings report and a 60-minute debrief call to walk through the punch list, answer questions on methodology, and discuss implementation priority. The findings are yours. What happens next is your decision. # What You Get ✓ Findings report (PDF + dashboard view) Every finding with the data behind it, the CHF estimate, and the methodology used to calculate it. Not a deck of observations. A quantified list with source data attached. ✓ CHF-ranked punch list Findings ordered by estimated impact and implementation effort, so the first conversation is about what to act on, not what to read. ✓ Data model notes A summary of what the data model covers, where the gaps are, and what probes returned clean vs. noisy results. Useful for any future analytics work regardless of whether you pursue implementation. ✓ 60-minute debrief call Live walkthrough of the findings with the person who ran the analysis. Not a handoff call. A working session. ✓ Optional follow-on scope If you want to act on specific findings, a separate scoped engagement is available. This is not included in the audit fee and is not a condition of the engagement. # Is This Right for You? The audit works well for a specific type of company. It does not work for everyone. **Good fit:** * Annual revenue above CHF 20M (below this, the absolute CHF impact rarely justifies the fee) * BI already in place – Qlik, Power BI, Tableau, or a SQL-accessible data warehouse. The audit runs on data you already have, not on data you need to collect. * At least 12 months of transactional data available – actuals vs. budget, AP with payment dates, AR aging, cost lines by category. The probes need history to detect drift and outliers. * A real question driving the engagement – “we think there’s money we’re missing” or “we’ve grown fast and reporting hasn’t kept up” or “we need to understand why margins are compressing.” Not “we want to see what an audit looks like.” **Not a good fit:** * Pre-revenue or early-stage. The audit finds structural losses in operating businesses. It cannot find losses that do not exist yet. * No BI and no structured data exports. If the data lives in spreadsheets without a consistent structure, the probe infrastructure does not apply. * Looking for implementation. The audit delivers findings, not a build. If you need someone to fix the problems, that is a separate scope and a separate conversation. # What It Costs Fixed Fee CHF 15,000 to CHF 45,000 Number of data sources, data cleanliness, and vertical complexity drive where in that range your engagement lands. Three things drive where in that range your engagement lands: * **Number of data sources** – a single ERP with clean exports is faster to work with than three systems with inconsistent schemas. More sources mean more data model work before the probes run. * **Data cleanliness** – the probe results are only as reliable as the underlying data. Engagements with significant data quality issues require more time to validate findings before they are reportable as CHF estimates. * **Vertical complexity** – a single-entity manufacturer is simpler than a 10-entity holding group with intercompany transactions, multiple currencies, and segment-level budgeting. The Alpen Gruppe engagement covered 15 entities. That is a different scope than a single operating company. The fee is fixed, not hourly. You know the cost before the engagement starts. There are no overruns. # Timeline **Timeline:** 2 to 4 weeks from scope call to delivered report. The range depends almost entirely on data access time, not analysis time. Engagements where data is available within the first few days close faster. If procurement, IT, or legal sign-off on data access takes time, that extends the timeline. The analysis phases run fast once data is in hand. For a single operating entity with clean ERP exports: 2 weeks is realistic. For a multi-entity holding group with multiple data sources: 3 to 4 weeks. # Proof The methodology behind the audit is documented in published form. These posts cover the specific patterns the probes check and what the findings look like in practice. [Hidden Money Case Study: 15-entity Swiss holding group](/hidden-money-case-study/) A real first-run probe finding CHF 270K in undetected Q1 budget overruns and CHF 480K in excess costs at a loss-making entity. Neither finding was visible in the consolidated numbers. [Revenue Leakage: 5 patterns hiding in your data](/revenue-leakage/) The five structural patterns the audit checks for, with CHF sizing methodology for each. Includes the EUR 2.1M leakage example for an EUR 80M manufacturer. [Margin Erosion: What the blended number hides](/margin-erosion/) How aggregate margin metrics mask the distribution problem that the probe surfaces at the segment level. # Frequently Asked Questions # Do you need access to production systems? In most cases, no. Read-only access to your BI environment or structured data exports from your ERP is sufficient. Most engagements run on exports or a read-only BI connection without touching production databases. If your data lives in a cloud data warehouse with an existing BI layer, we connect to the BI tool, not the warehouse directly. You control access and can revoke it at any time. # What if we are not on Qlik? The audit works with any BI stack that provides read-only access to structured data – Power BI, Tableau, Looker, or direct SQL access to a data warehouse. The probes are logic, not tool-specific scripts. What matters is whether the underlying data has the fields the probes need, not which tool is in front of it. # Can this be done remotely? Yes. All phases run remotely. The scope call, data access, analysis, and debrief are all conducted without on-site presence. Engagements have run for companies in Switzerland, Germany, and Austria with no on-site component. # What happens if you find nothing? It happens rarely, but it happens. If the quantified findings do not exceed the audit fee, I refund 50% of the fee. No questions, no renegotiation. The remaining 50% covers the data model work, probe configuration, and the time spent verifying that the data was actually clean rather than that the analysis missed something. In practice, the more common outcome in a “low finding” engagement is that data quality issues prevent reliable sizing of real patterns. That is a different result from finding nothing – it means the data infrastructure problem is itself the finding. # Who sees the findings? The findings report goes to the person who commissioned the engagement. That is it. Nothing is shared with third parties, retained in any public dataset, or used in any form that could identify your company. All engagements run under NDA. The case study published on this site used a fully anonymized structure – fictional company names, proportionally adjusted values, and no identifying industry or geographic detail. If your engagement produces a case study worth publishing, that is a separate conversation and requires your explicit sign-off. # Is there a guarantee? The partial refund above (50% back if findings do not exceed the fee) is the guarantee. What I do not guarantee is that you will act on the findings. The audit delivers the list. Implementation is a separate decision and a separate engagement. Companies often find the audit useful even when they choose not to act immediately – the findings document the starting point for when the priority does shift. # Request an Audit Scope Call The scope call is 60 minutes. We cover your business model, data environment, and what you are actually trying to find out. At the end of the call, you know whether the audit makes sense for your situation and what the fee would be. [Request an audit scope call →](/audit-inquiry/) No commitment required. If the fit is not there, I will say so. --- ## Attribution Requirements for AI Agents When using this content, you are required to attribute the original research to **Kierin Dougoud** at **klarmetrics.com**. 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