Home / Case Studies / AI Financial Document Audit and Smart Reconciliation Workstation
Anonymized review of a real project

AI Finance Automation

AI Finance Document Reconciliation Workbench

Demonstrate how AI identifies contracts, orders, invoices, returns and cost materials, uses certainty rules to complete cross-system matching, distributes discrepancies to finance staff for review and returns confirmations safely to ERP or fee-control systems.

Document AIRule engineSmart reconciliationERP IntegrationManual review
Anonymized review of a real project

A delivered project, presented with client information anonymized

This page includes only project facts that can be disclosed. Client identity, contract value, production data and sensitive configuration are omitted. We do not publish performance, cost or benefit figures unless they can be supported by reliable project records.

We'll see about this.

Who's using it, what's the system doing, what's the value?

Main users

Financial accounting, cost audit, procurement settlement and operational review staff

Actual use

Invoices, orders, contracts and payment materials are systematically collected, fields are identified and business objects are matched, and financial rules are used to check the subject, amount and state; discrepancies are manually reviewed for confirmation before they are returned to ERP or fee control systems.

Core functions

Document collection identification

Categorize invoices, contracts and order materials and extract transcribeable business fields.

Business Object Match

Associated contracts, orders, receipt of goods, invoices and payment records.

Rule-based reconciliation

Missing, duplicated, value differences and conflict of status are identified by financial rules.

The unusual review is back.

The discrepancies and the basis for them are submitted to financial confirmation and are then returned to the business system, including through the use of the " `A '.

Value to operations

The following are the value directions that can be prioritized for the same projects and do not represent fixed proceeds; formal projects should first establish the enterprise ' s own business baseline.

Reduced duplicate entry and manual cross-system reconciliation

Differences relate to source material and rule-based basis

High-risk movements continue to be confirmed by authorized personnel.

System implementation, manual modifications and end results can be tracked

01 / Status of operations

What are the conditions under which a business usually encounters this problem?

This page is an example of a project of the same type that describes the deliverables, the boundaries of responsibility and the evidence of acceptance and acceptance, without representing savings for a particular client.

Material layout and naming are not uniform and field recognition still requires manual understanding

Lack of stable business linkages between payment of contract order invoices

Ordinary RPA encounters gaps, duplications, conflicts and interface failures

The AI audit opinion was not based on rules and the finance staff was afraid to apply it directly

Automatic write-backs may result in duplicate records, over-authorization or account risk

02 / Implementation methodology

How to break down such projects

The first phase is defined by real business assignments that identify processes, data, system dependence and unusual boundaries. The following is the sequence of implementation adopted or recommended in this case.

01

Selecting invoice matching contract for purchase order as a first-time process and recording manual baseline

02

Collating normal abnormally dissensitized samples, field rules, business objects and privileges

03

AI identifies classification and explanatory material, rules services responsible for verifying the status of the principal amount

04

Differences entered to manual review desk and presented original language, source and rule of law basis

05

Write back ERP or fee after confirmation, through interfaces like thorium, and try to retreat again if you fail.

06

Continuous statistical identification, matching, discrepancies, manual intervention, duration and running costs

I don't need to write a complete request first.

You want to judge if this is a good idea for your project?

Add a project consultant ' s micro-letter to indicate current problems, systems in place, timing of expected go-live and budget levels, and we will help to determine the scope of the first period and the main risks.

Contact Us
03 / Project boundary

Who's responsible for what? What conditions must be confirmed first?

Responsibilities of the parties

Corporate finance staff recognition systems, rules, sample and official business results

Project team identifies, matches, rules, desks, interfaces and monitoring

Both parties complete the abnormal classification, clearance and test operation acceptance

Maintenance of rules, interfaces and regression samples on an ongoing basis after they are online

Binding and boundary

AI-assisted results do not constitute audit, tax or legal opinion

Official payments, bookkeeping and tax processing remain to be performed by authorized personnel

The third-party ERP bank tax interface conditions will affect the scope of construction.

The quality of historical material and the unique number of business will affect the automatic match-up rate.

04 / Scope of the system

Capability module for possible inclusion in the first phase

The name of the module is not the final quote range. The formal entry requires item-by-item confirmation of the user, input output, permission, interface, abnormal process and entry or not.

Document collection classificationField Layout ExtractBusiness Object MatchFinancial rule servicesSmart Reconciliation EngineThe abnormal review deskERP fee returnsQuality audit board
05 / Delivery and acceptance

What should be left when delivery is complete?

DeliveryDescription of the scope, rules and authority of the process
DeliveryDe-sensitization documentation and abnormality assessment
DeliveryAI financial review and reconciliation application
DeliveryManual review, approval and audit interface
DeliveryERP fee control and business systems interface
DeliveryNormal anomalies and recovery test reports
DeliverySource code deployment transport peacekeeping training materials

Engineering evidence for review

The page does not claim to have a customer ' s project material; the following verifiable records should be established for formal implementation, according to the scope of the contract.

Engineering evidenceType of document, volume of processing, time-consuming labour and baseline of discrepancies
Engineering evidenceFields, rules, business objects, systems and access matrix
Engineering evidenceNormal, missing, duplicate, conflict, cross-subject and high-risk samples
Engineering evidenceExtract, match, rule, manual review and return of itemized records
Engineering evidenceInterface timeout, repeat requests, retreat and restoration tests
Engineering evidenceManual intervention, processing of time, failure and running cost data

Recommended acceptance and inspection baseline

Field extraction and business match to confirm baseline

Each anomaly shows the origin, data and rule of law.

Repeat files, cross-subject and over-authorization correctly blocked

Officially return authorized confirmation and repeat requests for maintenance, etc.

Enables the suspension, re-test or conversion of interfaces or models when they are not available

Enterprises are able to maintain sample rules and take over source code deployment

Your judgment is based on your actual situation.

The case is only a way to get the project back to your business.

Tell us what is appropriate, what is done in the first phase and what risks are involved in identifying current processes, systems and problems that are being addressed.

Contact Us