Every control. Every transaction. Every day.
Controls tested once a year can fail quietly for the other eleven months. CompliBear monitors your key controls continuously, across 100% of transactions, and sends each exception to its owner with a value and the evidence, so problems are fixed in days, not discovered at year end.
Periodic testing leaves long blind spots
Sampling misses the pattern
Twenty-five items can pass while the twenty-sixth, and the thousand after it, fail.
Year-end surprises
A control that failed in March is found in December, after the money has gone.
Alert fatigue
Rules that flag everything train people to ignore the alerts that matter.
Evidence is scattered
Workpapers, emails and spreadsheets. Nobody can trace a finding to its source.
Three frauds, 10,000 invoices, a sample of 25
Three frauds hide among 10,000 invoices. You test a random 25. What are the odds you find none?
Draw a sample and see what it finds.
(9,997 ÷ 10,000)²⁵ ≈ 0.993. Testing all 10,000, every day, finds all three.
From a policy clause to a daily test
- THE CLAUSE“Requirements shall not be split to avoid the approval of the competent authority.”
A line from a purchase manual.
- THE RULE, EXTRACTED BY AIFlag orders to the same vendor, for the same material, within a short window, that together exceed the approval limit while each stays below it.
The auditor reviews and approves the rule.
- THE TESTRuns every day, on every order
The window, the limit and the tolerance stay editable.
Private generative AI reads your documents inside your environment. 750+ ready-made tests are there from day one.
Where we look, and what warns early
| Area | What we test | Early-warning signal |
|---|---|---|
| Procurement and tendering | Split orders, single or limited tenders, bid patterns, repeat orders beyond validity | Spend concentrating on a few vendors |
| Vendor master and payments | Duplicates, bank changes, dormant vendors reactivated, payments without receipt | Vendor risk score rising before payment |
| Contracts and capex projects | Billing against the awarded value, extra items, advances not recovered, damages not levied | Contracts trending to overrun |
| Inventory and product | Stock variance against loss norms, write-offs, slow-moving stores and spares | Variance drifting towards the norm |
| Logistics and freight | Claims against distance, duplicate trips, short deliveries, rate overrides | Transporter outlier score |
| Dealers and distributors | Credit-limit breaches, discounts and incentives beyond the scheme, claims, commissions | Dealer health score falling |
| Revenue and receivables | Price applied against the price list, credit notes, rebates, ageing | Collection slippage forecast |
| Payroll and expenses | Ghost employees, duplicate claims, overtime and allowance anomalies | Unusual pay changes |
| Tax and regulatory | GST input credit against GSTR-2B, TDS, the MSME 45-day rule, e-invoice gaps | Due-date breach forecast |
| IT and SAP controls | SoD conflicts, sensitive transactions, direct table changes, privileged access | Control drift alerts |
Six techniques, one engine
| Technique | What it catches | Example |
|---|---|---|
| Rules and thresholds | Known policy breaches | A payment above the delegated limit |
| Statistical tests | Outliers | Freight claims per kilometre far above peers |
| Machine learning | Unknown patterns | A dealer that behaves unlike similar dealers |
| Link analysis | Hidden relationships | An employee and a vendor sharing a bank account or address |
| Text analytics | What narrations reveal | Vague narrations on large manual entries |
| Predictive models | Exceptions likely to occur | Invoices on course to breach the MSME 45-day limit |
Signals that come before the loss
| Leading signal | What it predicts | Action before the loss |
|---|---|---|
| MSME invoices nearing 45 days | Interest and a disallowed tax deduction | Prioritise them in the next payment run |
| Contract billing nearing the awarded value | A cost overrun | Review variations before approval |
| Dealer credit use rising while offtake falls | A credit breach or bad debt | Review the credit limit |
| Stock variance drifting towards the loss norm | Product loss | Physical verification |
| Vendor risk score climbing | Payment to a risky vendor | Hold before the payment run |
| Manual overrides rising | A control breakdown | A targeted review |
Every vendor, dealer, contract and user, scored daily
| Entity | Type | Risk score | Trend | Top reason |
|---|---|---|---|---|
| Vendor V-20931 | Vendor | 92HIGH | Rising | Bank details changed; tax registration inactive |
| Dealer D-4471 | Dealer | 86HIGH | Rising | Credit use up, offtake down for three months |
| Depot DP-07 | Location | 78MED | Rising | Stock variance close to the loss norm |
| Contract C-1187 | Contract | 74MED | Stable | Billing near the awarded value |
| ERP user U-0332 | User | 69MED | New | Created and paid the same vendor |
Illustrative scores. Each score runs from 0 to 100 and shows the reasons and source transactions behind it.
What the explainer shows.
- Controls tested once a year can fail quietly for the other eleven months.
- Test a random 25 of 10,000 invoices and there is a 99.3% chance you miss all three frauds. Test all 10,000, every day, and you find all three.
- Private generative AI, inside your environment, turns a policy clause into a daily test. The auditor approves the rule.
- Each exception reaches its owner with a value and the evidence, so problems are fixed in days, not found at year end.
Not data ingestion. Not reporting. An early-warning system for internal audit.
07How it works
- 01Connect
Read-only to your ERP
- 02Monitor
Every control, every day
- 03Alert
Exceptions ranked by business impact
- 04Resolve
Owners decide in OATS, with a time-stamped trail
- 05Explain
Root Cause Analyzer shows why, and the cost
08Proof
A listed manufacturer runs CompliBear across 6 plants: 4,200 exceptions found in the first quarter, worth USD 1.1 Mn, and 2,600 audit hours saved a year. · 1,690+ hours of manual testing removed each year across implementations
Start with a two-week LaunchPad
We connect to your data, switch on five insights and two machine-learning models chosen with you, and support you through two weeks of hyper-care. You see live exceptions, valued, on your own data.
Two weeks. Your data, read-only. Live exceptions, valued.
10FAQ
How is this different from our ERP’s GRC module?
GRC tools manage control documentation and access rules. CompliBear tests what actually happened in the transactions and puts a value on each exception. It works alongside your GRC tool.
Who owns the controls?
You do. We configure the insights to your control framework; your control owners receive the exceptions and make the decisions.
What will our external auditors think?
Continuous evidence with a full audit trail usually makes their work easier. We can walk them through the method.
How quickly can we add controls?
New insights come from the library of 750+. Audit Flex adds 5 a quarter and Audit Pro adds 25. See Pricing & licences.
How is this different from the audit analytics we already run?
Most analytics report what has already happened. Here the tests come from your own policies and run on every transaction, every day, and leading signals warn the owner before the loss. The observation is drafted for you.
Does our data leave our environment?
No. It runs on premises or in your chosen cloud, with read-only access. The generative AI that reads your policies is private and runs inside your environment.
What would you find if you tested everything?
See live exceptions on your own data in two weeks.