5tasks in this job
28skills it uses
8tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 3 of 5 tasks
- 2Resolving and escalating pay, incentive and payment exceptions within authorityNeeded in 3 of 5 tasks
- 3Verifying payment and bank-detail changes against fraud controlsNeeded in 3 of 5 tasks
- 4Reconciling finance subledgers and close recordsNeeded in 1 of 5 tasks
ToolsSAP S/4HANACoupaMicrosoft ExcelBatchflowFortnoxMicrosoft Dynamics 365 Finance and OperationsOracle Fusion ERP and E-Business SuiteSage (50, 200, X3)
All 5 tasks, where people get stuck and all 28 skills
Tasks
- Investigate invoice matching exceptions
- Investigate procurement spend leakage and duplicate commitments
- Verify a supplier payment run before release
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Verify supplier master data and bank detail changes against fraud controls
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Diagnosing duplicate records
- Investigating procurement spend leakage and duplicate commitments
All skills
- Reconciling finance subledgers and close records
- Resolving and escalating pay, incentive and payment exceptions within authority
- Validating records by matching, recomputation and end-to-end tracing
- Verifying payment and bank-detail changes against fraud controls
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing periods consistently
- Designing and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing operational handoffs and flow
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Explaining how business events flow into accounting records
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
54skills it uses
9tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 5 of 8 tasks
- 2Diagnosing duplicate recordsNeeded in 4 of 8 tasks
- 3Investigating procurement spend leakage and duplicate commitmentsNeeded in 4 of 8 tasks
- 4Reconciling finance subledgers and close recordsNeeded in 2 of 8 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsMicrosoft ExcelSAP S/4HANACoupaOracle Fusion ERP and E-Business SuiteFortnoxVisma ERP and accounting+ 3 more
All 8 tasks, where people get stuck and all 54 skills
Tasks
- Reconcile and substantiate balance sheet accounts at period close
- Investigate invoice matching exceptions
- Investigate procurement spend leakage and duplicate commitments
- Automate a recurring reporting or data task with controls and a named owner
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Verify a supplier payment run before release
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Verify supplier master data and bank detail changes against fraud controls
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Diagnosing duplicate records
- Investigating procurement spend leakage and duplicate commitments
- Reconciling finance subledgers and close records
- Validating records by matching, recomputation and end-to-end tracing
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing operational handoffs and flow
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving and escalating pay, incentive and payment exceptions within authority
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying payment and bank-detail changes against fraud controls
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- SAP S/4HANA
- Coupa
- Oracle Fusion ERP and E-Business Suite
- Fortnox
- Visma ERP and accounting
- Batchflow
- Microsoft Dynamics 365 Finance and Operations
- Sage (50, 200, X3)
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
8tasks in this job
65skills it uses
9tools named
The skills this job leans on most
- 1Diagnosing duplicate recordsNeeded in 4 of 8 tasks
- 2Validating records by matching, recomputation and end-to-end tracingNeeded in 4 of 8 tasks
- 3Investigating procurement spend leakage and duplicate commitmentsNeeded in 3 of 8 tasks
- 4Reconciling finance subledgers and close recordsNeeded in 1 of 8 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsSAP S/4HANAMicrosoft Dynamics 365 Finance and OperationsMicrosoft ExcelMicrosoft Power BIFortnoxSage (50, 200, X3)+ 3 more
All 8 tasks, where people get stuck and all 65 skills
Tasks
- Investigate invoice matching exceptions
- Investigate procurement spend leakage and duplicate commitments
- Test the design and operating effectiveness of a data-dependent control
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Verify a supplier payment run before release
- Design data-dependent controls for a new or changed finance or people process, system or automation
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Verify supplier master data and bank detail changes against fraud controls
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Diagnosing duplicate records
- Investigating procurement spend leakage and duplicate commitments
- Reconciling finance subledgers and close records
- Validating records by matching, recomputation and end-to-end tracing
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Making accountable decisions under uncertainty
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving and escalating pay, incentive and payment exceptions within authority
- Sampling and reviewing a team's work against standards
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying AI-assisted outputs against sources
- Verifying payment and bank-detail changes against fraud controls
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
All tools
- SAP S/4HANA
- Microsoft Dynamics 365 Finance and Operations
- Microsoft Excel
- Microsoft Power BI
- Fortnox
- Sage (50, 200, X3)
- Oracle Fusion ERP and E-Business Suite
- Coupa
- Batchflow
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Accountable for matching supplier invoices to orders and receipts and for resolving the exceptions that fall out. Decides whether an invoice can be paid and stops duplicate or leaked payments. Data and AI judgement sits in analysing exceptions and checking automated matching and AI invoice extraction.
Also called Accounts Payable Clerk, Purchase Ledger Clerk