Fraud & Risk Intelligence

Most finance controls catch fraud after the loss. We help you catch the patterns before they compound.

Fraud and leakage often sit across payments, suppliers, expenses, journals, and contracts. riicu helps finance and control teams map exposure and validate the right monitoring approach on their own data.

riicu.ai/risk-monitoring
Live
Risk Monitoring3 signals
  • Payment riskReview
    Possible duplicate invoice - same vendor, similar amount
    90
  • Expense patternInvestigate
    Anomalous travel claims - small employee cluster
    75
  • Supplier riskReview
    New vendor - bank details overlap with dormant entity
    88
Signals
Mapped
Across flows
Risk surface
Mapped
Across flows
Workflow
Designed
With your team
Validation
Historical
Your data
01
Diagnose
Map your real risk surface across payments, suppliers, payroll, journals and contracts.
02
Design
Define target monitoring workflows: signals, ownership, escalation, governance.
03
Demonstrate
Build analytical demonstrators on your historical data, validated with finance and control.
04
Deliver
Phase implementation around the risk areas where signal is strongest.
How we approach risk

Map the Risk Surface. Then build around it.

Your ERP records transactions — it rarely surfaces patterns. We frame which risk areas matter most, design the analytics that catch them, and validate the logic on your historical data before scaling anything live.

  • Risk surface mapping
    Payments, suppliers, expenses, payroll, journals, contracts.
  • 8 to 15 scenarios per engagement
    Each documented, explainable and validated on your data.
  • Built around your patterns
    Analytics that learn what 'normal' looks like in your business.
Discuss a diagnostic
riicu.ai/risk-surface
Diagnose
Risk Surface5 areas
  • Payment patternsSample
    80%
  • Supplier setupSample
    65%
  • Expense behaviourSample
    50%
  • Payroll anomaliesSample
    70%
  • Journal entriesSample
    35%
Coverage
Designed
With your team
Signal logic
Documented
Per scenario
Validation
On your data
Historical
Workflow
Defined
Action paths
Commercial Leakage & Documentary Risk

Revenue and Margin Quietly Slipping Away.

Pricing inconsistencies, unbilled services, contract drift — commercial leakage is silent and cumulative. It usually only surfaces during a margin review, long after the value has been lost. We help you design analytics that look at contracts, invoicing and pricing together, so the gaps become visible earlier.

In organisations with high transaction volume and multiple pricing tiers, unbilled services and contract drift alone can represent 1–2% of revenue. It rarely surfaces before the annual margin review. Documentary risk — altered invoices, inconsistent approvals — is approached the same way: framed as analytical scenarios validated on your data, not as a black-box engine.

Discuss a diagnostic
riicu.ai/commercial-leakage
Workshop
Commercial Leakage4 scenarios
  • Pricing inconsistencies45%
  • Unbilled services30%
  • Contract non-compliance17%
  • Discount / override misuse8%
Scenarios
Designed
With your team
Validation
Historical
Your data
Workflow
Defined
Action paths
Coverage
Mapped
Pricing & contracts
Use Cases

Risk areas we help you design analytics for

Each area below is a workstream we shape together. The deliverable is a documented set of analytical scenarios, validated on your data, plus a workflow for finance and control to act on the signals.

Risk surface mapping

Manual controls catch issues after the damage is done — usually weeks later, sometimes never.

We map where your real risk sits across financial flows and design which signals are worth monitoring — before building anything.

Payment analytics

A duplicate invoice paid to a vendor that already invoiced last week. Bank details changed by email a few days before payment. Happens more often than CFOs realise.

Design analytical scenarios that compare each payment to historical patterns and vendor master data, validated on your own history.

Supplier risk analytics

A vendor reactivated with new bank details, no purchase in 18 months. It happens. Standard AP controls do not catch it.

We design the signals that flag it before payment goes out — looking at registration data, bank details, and transactional links between vendors, employees and entities.

Expense analytics

Inflated claims, duplicate receipts across team members, small amounts that add up across the year — the patterns are there but no one is looking.

Design scenarios for duplicates, policy outliers and behavioural anomalies, calibrated to your spending norms.

Payroll anomaly review

An active payroll record with no recent timesheet activity. A pay change a few days before the run, by a user who normally does not touch payroll. Hard to catch at scale.

Cross-reference payroll with HR records, time data and access patterns through analytical scenarios you can audit and adjust.

Journal entry analytics

A manual JE posted off-hours, just below the review threshold, by an unusual user. Each one explainable individually. The pattern is what matters.

Design scenarios around timing, amount, account combinations and preparer behaviour, with explainable logic per signal.

Commercial leakage

Pricing drift across contract renewals, services delivered but not invoiced, discounts that became permanent. Often costs more than CFOs expect — and only surfaces at the annual margin review.

Frame analytics that compare contracts, pricing and invoicing together, so gaps surface earlier in the cycle.

Documentary risk

An invoice altered between submission and approval. An approval signed by someone outside the delegation. Standard manual checks miss it.

Design analytical reviews of documents and approval trails, validated on historical cases before scaling.

Connected Workstreams

Looking at tax, compliance and reporting as well?

Many control questions sit close to tax and statutory reporting. We help finance teams shape both - control analytics and tax/compliance design - using the same diagnose → design → demonstrate → deliver approach.

FAQ

Common Questions

No. We are a finance-led design partner. We help you map your real risk surface, design analytical scenarios that fit your business, and build demonstrators on your own data. Implementation is phased around what proves value - not by deploying a generic detection engine.

Not at all. We work alongside finance, internal control and audit. Internal audit typically reviews samples; the analytics we help design are intended to give those teams continuous, explainable signals across financial flows.

Payment patterns (duplicates, bank detail changes), supplier setups (links, dormant reactivation), expense behaviour, payroll anomalies, journal entries (off-hours, threshold-adjacent), commercial leakage (pricing, unbilled services) and documentary risk. The exact priorities depend on where your real exposure sits.

AI is useful for pattern detection at scale — flagging anomalies across thousands of transactions that a human analyst would miss. It does not replace judgement on whether an anomaly is a real risk. Every signal we design has a human review step built in. We are explicit about where the model ends and where the analyst begins.

It usually starts with a diagnostic of your risk surface and current controls. From there we co-design priority analytical scenarios, validate them on historical data, and agree a phased implementation plan with finance and control.

Ready to See Where
Your Real Exposure Sits?

Start with a diagnostic session. We map your risk surface across payments, suppliers, expenses, payroll and journals - and outline which analytical scenarios are worth designing first, with no commitment to a specific tool.