AI & Data Analytics Application · Practice two

Revenue
Assurance

Income leakage hides inside the core banking system: invisible to manual reconciliation and untraceable across pricing, exceptions and channels. A shadow engine calculates what should have been earned, and reconciles it against what was actually booked, every day.

Live reconciliation · Facility FIN-042-7
Core banking system bookedUSD 128,450
Shadow engine expectedUSD 134,690
Shortfall flagged · USD 6,240

Banks lose meaningful revenue every day, because the systems that recognise income were never built to police it.

Banks that price facilities off a benchmark rate, a fixed margin or a floating rate still mostly reconcile what was actually booked against what a facility should have earned by hand: deal by deal, well after the fact. A credit application sets the terms; the core banking system later books a number against that facility; and whether the two agree is usually only checked during a periodic audit, months after the transaction happened. By then, a shortfall is a write-off, not a fix: and an excess booking is a compliance question nobody remembers the context for.

Revenue Assurance came out of exactly that gap on a live mandate, not a product brainstorm. It sits alongside the credit files a bank already produces and turns them into a live, auditable reconciliation: because a bank cannot afford a process that guesses, and it cannot afford one that nobody can explain to an auditor.

How the platform works

Two stages, running continuously against every facility, not a month-end project. Detect establishes the true figure; Resolve puts a person in front of every exception before anything is corrected.

Detect

Reading the facility, then computing what it should have earned
01

Extract

AI reads facility structure, product type, rate, tenor and repayment terms directly from the credit application and availment letter: nobody re-keys a document into a spreadsheet.

02

Calculate

One consistent method, fixed, floating, or benchmark plus margin, works out what should have been booked, applied the same way every time.

03

Compare

That figure is compared, field by field, against what the core banking system actually booked for the same transaction, at account level, daily.

Resolve

Every exception reaches a person, and closes with a reason
01

Flag

Anything outside a configurable tolerance is surfaced as a shortfall or an excess, ranked by value, not buried in a report nobody opens.

02

Investigate

The source document is one click away, so a reviewer confirms in seconds instead of requesting files back from operations.

03

Close

A person closes every flag: the engine never books or reverses anything itself, and every closure keeps a reason attached.

Where the leakage comes from

The problem, the way it’s handled today, and what changes once Revenue Assurance is running.

The problem

  • Human error in data entry: adjustments, reversals and manual entries mis-booked, duplicated, or never captured.
  • Pricing complexity (tiered fees, bundles, dynamic rates) exceeds core system configuration capacity, causing under- or over-accrual.
  • System gaps: incomplete, outdated or mis-configured product processors, covered by manual workarounds.
  • Multi-channel inconsistency: the same product priced differently across branch, digital, RTGS and ATM.
  • Pricing exceptions follow separate approval channels: hard to track or reconcile against the standard schedule.

Current state

  • Sample-based manual reconciliation: large portions of leakage missed by design.
  • Month-end scrubs by Finance and Audit: slow, retrospective, and hostile to deadlines.
  • Hidden discrepancies stay buried until an issue surfaces: rarely an obviously large one.
  • No daily visibility into expected versus actual income at account level.
  • Finance chases last month’s leakage; Internal Audit works from sample data with no holistic transaction view: never preventing the next one.

What ALP delivers

  • A shadow processing engine that calculates expected income at account level daily, and reconciles it against the core banking system.
  • Source-document digitisation: agreements, term sheets, draw-down notices and credit approvals: into an intelligent document repository.
  • Daily variance reports at transaction, currency and operation level, replacing the month-end scrub.
  • A BI module covering borrowing by segment, fixed versus floating, evergreen loans, prepayment and rate-shock forecasting.
  • Ready ETL connectors for most core banking systems: fast integration, no rip-and-replace.
USD 500KMonthly leakage caught, one revenue line
Up to 7×Variation in leakage rates across clients
90 daysTypical implementation cycle
In the field

Two engagements, two very different pricing structures: the same shadow reconciliation underneath.

UAE Bank · Trade Finance Pricing

Plugged leakage across channels on trade finance pricing: improving profitability, audit efficiency and customer experience in parallel.

Saudi Shariah Bank · Murabaha

Profit-rate calculations on Murabaha facilities reconciled against core system bookings for the first time, with every figure traced back to its underlying sale agreement.

Work with us

Bring us the leakage you already suspect is there.

Speak to the team