6tasks in this job
59skills it uses
8tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 4 of 6 tasks
- 2Resolving and escalating pay, incentive and payment exceptions within authorityNeeded in 3 of 6 tasks
- 3Verifying payment and bank-detail changes against fraud controlsNeeded in 3 of 6 tasks
- 4Reconciling finance subledgers and close recordsNeeded in 1 of 6 tasks
ToolsSAP S/4HANAMicrosoft Dynamics 365 Finance and OperationsMicrosoft ExcelOracle Fusion ERP and E-Business SuiteFortnoxCoupaIbisticSage (50, 200, X3)
All 6 tasks, where people get stuck and all 59 skills
Tasks
- Investigate invoice matching exceptions
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- 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 anomalies
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
- Analysing queue behaviour, capacity and service constraints
- 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
- Balancing territories and quotas from account potential
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting needs through interviews, workshops and observation
- 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
- Giving specific developmental feedback on data and AI work
- 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
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- 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
- Specifying decision-useful reports
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
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
6tasks in this job
67skills it uses
9tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 4 of 6 tasks
- 2Diagnosing duplicate recordsNeeded in 3 of 6 tasks
- 3Investigating anomaliesNeeded in 3 of 6 tasks
- 4Reconciling finance subledgers and close recordsNeeded in 1 of 6 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsSAP S/4HANAOracle Fusion ERP and E-Business SuiteMicrosoft ExcelMicrosoft Power BICoupaIbistic+ 3 more
All 6 tasks, where people get stuck and all 67 skills
Tasks
- Investigate invoice matching exceptions
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- 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
- Testing changes to data services and business systems for business meaning and downstream impact
- Designing testable data controls
- Explaining control types, dependencies and compensating controls
All skills
- Diagnosing duplicate records
- Investigating anomalies
- 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
- Analysing queue behaviour, capacity and service constraints
- 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
- Balancing territories and quotas from account potential
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- 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
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- 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 procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- 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
- Specifying AI transparency and explanation duties to affected people
- Specifying decision-useful reports
- 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
- Oracle Fusion ERP and E-Business Suite
- Microsoft Excel
- Microsoft Power BI
- Coupa
- Ibistic
- Fortnox
- 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
76skills it uses
9tools named
The skills this job leans on most
- 1Validating records by matching, recomputation and end-to-end tracingNeeded in 3 of 8 tasks
- 2Diagnosing duplicate recordsNeeded in 2 of 8 tasks
- 3Designing testable data controlsNeeded in 2 of 8 tasks
- 4Setting and reviewing action thresholds for scores and model predictionsCore to this role
- 5Testing changes to data services and business systems for business meaning and downstream impactCore to this role
ToolsSAP S/4HANAMicrosoft Power BIMicrosoft ExcelMicrosoft Dynamics 365 Finance and OperationsCoupaIbistic+ 3 more
All 8 tasks, where people get stuck and all 76 skills
Tasks
- Investigate invoice matching exceptions
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Investigate procurement spend leakage and duplicate commitments
- Test the design and operating effectiveness of a data-dependent control
- Design data-dependent controls for a new or changed finance or people process, system or automation
- Assess supplier risk and maintain the supplier risk register
- 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
- Designing testable data controls
- Diagnosing duplicate records
- Validating records by matching, recomputation and end-to-end tracing
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- 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
- Balancing territories and quotas from account potential
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- 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
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- 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
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- 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
- Reconciling finance subledgers and close records
- 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
- Specifying decision-useful reports
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- 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 Power BI
- Microsoft Excel
- Microsoft Dynamics 365 Finance and Operations
- Coupa
- Ibistic
- Fortnox
- Oracle Fusion ERP and E-Business Suite
- 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
About the role
Accountable for supervising invoice processing and for resolving exceptions across the payables queue. Allocates and checks others' work and owns exception resolution rather than clearing a single queue. Data and AI judgement sits in reviewing automated matching and AI invoice extraction for errors and leakage.
Also called Accounts Payable Team Leader