5tasks in this job
31skills it uses
5tools 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 5 of 5 tasks
- 3Assessing customer creditworthiness and setting limitsNeeded in 5 of 5 tasks
- 4Prioritising collection actions from aged debtNeeded in 5 of 5 tasks
- 5Interpreting policy scope and delegated authority limitsNeeded in 5 of 5 tasks
ToolsMicrosoft Power BIMicrosoft ExcelExcel VBA and macrosSAP S/4HANAMicrosoft SQL Server (with SSMS)Visma ERP and accountingSalesforce
All 5 tasks, where people get stuck and all 31 skills
Tasks
- 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
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
- Investigate customer disputes and deductions and resolve them with root cause and routed corrections
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Estimating accruals, provisions and expected credit loss under accounting policy
- Designing and weighting rule-based scoring models
All skills
- Analysing working-capital drivers and cash conversion
- Assessing customer creditworthiness and setting limits
- Interpreting financial statements and ratios for decisions
- Interpreting policy scope and delegated authority limits
- Prioritising collection actions from aged debt
- Analysing costs, allocation and margin drivers
- Assigning and operating data ownership and stewardship
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing operational handoffs and flow
- Escalating unresolved risks with evidence and clear ownership
- Estimating accruals, provisions and expected credit loss under accounting policy
- Facilitating evidence-led retrospectives and corrective actions
- Identifying and quantifying decision downside and exposure
- Influencing change without formal authority
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- 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
- Validating records by matching, recomputation and end-to-end tracing
- 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
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
8tasks in this job
62skills it uses
7tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 6 of 8 tasks
- 2Interpreting financial statements and ratios for decisionsNeeded in 5 of 8 tasks
- 3Analysing working-capital drivers and cash conversionNeeded in 5 of 8 tasks
- 4Assessing customer creditworthiness and setting limitsNeeded in 5 of 8 tasks
- 5Prioritising collection actions from aged debtNeeded in 5 of 8 tasks
ToolsMicrosoft Power BIMicrosoft ExcelExcel VBA and macrosSAP S/4HANAMicrosoft SQL Server (with SSMS)Visma ERP and accounting+ 1 more
All 8 tasks, where people get stuck and all 62 skills
Tasks
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- 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
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
- 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
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Analysing working-capital drivers and cash conversion
- Assessing customer creditworthiness and setting limits
- Interpreting financial statements and ratios for decisions
- Prioritising collection actions from aged debt
- Rating risks against appetite and choosing proportionate treatment
- Analysing costs, allocation and margin drivers
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- 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
- Estimating accruals, provisions and expected credit loss under accounting policy
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- 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
- Selecting charts for evidence and decisions
- 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
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
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
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 SupportMachine Learning: from Data to Validated Models and Decisions
10tasks in this job
76skills it uses
8tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 10 of 10 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 8 of 10 tasks
- 3Setting and reviewing action thresholds for scores and model predictionsNeeded in 6 of 10 tasks
- 4Interpreting financial statements and ratios for decisionsNeeded in 6 of 10 tasks
- 5Analysing working-capital drivers and cash conversionNeeded in 6 of 10 tasks
ToolsMicrosoft Power BIExcel VBA and macrosSAP S/4HANAMicrosoft SQL Server (with SSMS)Visma ERP and accountingSAS+ 2 more
All 10 tasks, where people get stuck and all 76 skills
Tasks
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- Evaluate receivables exposure and prioritise authorised collection action
- Design data-dependent controls for a new or changed finance or people process, system or automation
- 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
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
- 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
- Escalating unresolved risks with evidence and clear ownership
- Rating risks against appetite and choosing proportionate treatment
All skills
- Analysing working-capital drivers and cash conversion
- Escalating unresolved risks with evidence and clear ownership
- Interpreting financial statements and ratios for decisions
- Rating risks against appetite and choosing proportionate treatment
- Setting and reviewing action thresholds for scores and model predictions
- Analysing costs, allocation and margin drivers
- Assessing customer creditworthiness and setting limits
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Designing and appraising samples
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating accruals, provisions and expected credit loss under accounting policy
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping proposed process changes and control impacts
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising collection actions from aged debt
- Reading and tracing unfamiliar code
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- 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
- Selecting charts for evidence and decisions
- 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
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- 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
- Writing maintainable code for analytical and data work
All tools
- Microsoft Power BI
- Excel VBA and macros
- SAP S/4HANA
- Microsoft SQL Server (with SSMS)
- Visma ERP and accounting
- SAS
- Microsoft Excel
- Salesforce
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
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 SupportMachine Learning: from Data to Validated Models and Decisions
About the role
Accountable for credit policy, customer credit risk and collections performance and for the team that runs them. Decides credit limits and holds above the team's authority and recommends bad debt provisions to the Financial Controller. Data judgement sits in reading ageing and payment behaviour to judge exposure, where a Financial Controller owns close integrity and accounting controls.
Also called Credit Control Manager, Head of Credit Control (when the role manages one team)