5tasks in this job
13skills it uses
4tools named
The skills this job leans on most
- 1Interpreting financial statements and ratios for decisionsNeeded in 5 of 5 tasks
- 2Analysing working-capital drivers and cash conversionNeeded in 4 of 5 tasks
- 3Prioritising collection actions from aged debtNeeded in 4 of 5 tasks
ToolsSAP S/4HANAFortnoxMicrosoft ExcelSalesforce
All 5 tasks, where people get stuck and all 13 skills
Tasks
- Reconcile and substantiate balance sheet accounts at period close
- Evaluate receivables exposure and prioritise authorised collection action
- Assess a customer's creditworthiness and set or recommend a credit limit
- Assess expected credit loss and recommend the bad debt provision
- Investigate customer disputes and deductions and resolve them with root cause and routed corrections
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Validating records by matching, recomputation and end-to-end tracing
- Investigating anomalies
All skills
- Analysing working-capital drivers and cash conversion
- Interpreting financial statements and ratios for decisions
- Prioritising collection actions from aged debt
- Assessing customer creditworthiness and setting limits
- Comparing periods consistently
- Escalating unresolved risks with evidence and clear ownership
- Explaining how business events flow into accounting records
- Judging the materiality of defects and deficiencies for the intended use
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Running sensitivity and break-even analysis on model assumptions
- Testing competing explanations with discriminating evidence
- Validating records by matching, recomputation and end-to-end tracing
Paths to start
Coming soonFinancial Modelling and Decision Support
6tasks in this job
45skills it uses
6tools named
The skills this job leans on most
- 1Interpreting financial statements and ratios for decisionsNeeded in 5 of 6 tasks
- 2Setting and reviewing action thresholds for scores and model predictionsNeeded in 4 of 6 tasks
- 3Analysing working-capital drivers and cash conversionNeeded in 4 of 6 tasks
- 4Validating records by matching, recomputation and end-to-end tracingNeeded in 2 of 6 tasks
- 5Investigating anomaliesNeeded in 2 of 6 tasks
ToolsSAP S/4HANAVisma ERP and accountingMicrosoft Dynamics 365 Finance and OperationsMicrosoft ExcelFortnoxSalesforce
All 6 tasks, where people get stuck and all 45 skills
Tasks
- Reconcile and substantiate balance sheet accounts at period close
- Evaluate receivables exposure and prioritise authorised collection action
- Assess a customer's creditworthiness and set or recommend a credit limit
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Assess expected credit loss and recommend the bad debt provision
- Investigate customer disputes and deductions and resolve them with root cause and routed corrections
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Testing competing explanations with discriminating evidence
- Escalating unresolved risks with evidence and clear ownership
All skills
- Analysing working-capital drivers and cash conversion
- Interpreting financial statements and ratios for decisions
- Investigating anomalies
- Validating records by matching, recomputation and end-to-end tracing
- Analysing costs, allocation and margin drivers
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- 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 operational handoffs and flow
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating accruals, provisions and expected credit loss under accounting policy
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Identifying and quantifying decision downside and exposure
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Mapping entity results and eliminating intercompany balances in consolidation
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising collection actions from aged debt
- 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
- Running sensitivity and break-even analysis on model assumptions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
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
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
Coming soonFinancial Modelling and Decision Support
7tasks in this job
53skills it uses
6tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 7 of 7 tasks
- 2Interpreting financial statements and ratios for decisionsNeeded in 6 of 7 tasks
- 3Analysing working-capital drivers and cash conversionNeeded in 5 of 7 tasks
- 4Validating records by matching, recomputation and end-to-end tracingNeeded in 2 of 7 tasks
- 5Investigating anomaliesNeeded in 2 of 7 tasks
ToolsSAP S/4HANAMicrosoft ExcelMicrosoft Power BIFortnoxVisma ERP and accountingSalesforce
All 7 tasks, where people get stuck and all 53 skills
Tasks
- Reconcile and substantiate balance sheet accounts at period close
- Evaluate receivables exposure and prioritise authorised collection action
- Assess a customer's creditworthiness and set or recommend a credit limit
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Build an evidence-based plan for a strategic customer account
- Assess expected credit loss and recommend the bad debt provision
- Investigate customer disputes and deductions and resolve them with root cause and routed corrections
Where people get stuck
- Testing competing explanations with discriminating evidence
- Escalating unresolved risks with evidence and clear ownership
- Interpreting policy scope and delegated authority limits
All skills
- Analysing working-capital drivers and cash conversion
- Interpreting financial statements and ratios for decisions
- Investigating anomalies
- Testing competing explanations with discriminating evidence
- Validating records by matching, recomputation and end-to-end tracing
- Analysing costs, allocation and margin drivers
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- 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 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
- Estimating accruals, provisions and expected credit loss under accounting policy
- Evaluating responsible AI use risks
- 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
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Mapping entity results and eliminating intercompany balances in consolidation
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising collection actions from aged debt
- 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
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- 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 evidence-led findings and auditable recommendations with their limits
Paths to start
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
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
Coming soonFinancial Modelling and Decision Support
About the role
Accountable for collecting customer debt on time and for the accuracy of the receivables ledger it is recorded in. Decides which accounts to chase or hold and recommends credit limits and bad debt provisions within policy. Data judgement sits in reading ageing and payment history correctly, where an Accounts Payable Specialist controls what the business pays out.
Also called Credit Control Analyst, Accounts Receivable Specialist, Collections Analyst