Effective Work Samples to Evaluate AI Skills for Accounts Receivable Optimization

Accounts receivable optimization represents a critical financial function that directly impacts an organization's cash flow, working capital, and overall financial health. As businesses increasingly turn to artificial intelligence to transform their AR processes, finding candidates with the right blend of AI expertise and financial domain knowledge has become essential. The intersection of machine learning capabilities with accounts receivable workflows offers tremendous potential for reducing days sales outstanding (DSO), improving cash application accuracy, predicting payment behaviors, and automating collection strategies.

Evaluating candidates for AI roles in accounts receivable requires more than just reviewing resumes or conducting standard interviews. Traditional evaluation methods often fail to reveal a candidate's practical ability to apply AI concepts to real-world AR challenges. Work samples provide a window into how candidates approach problems, their technical proficiency, and their understanding of the unique constraints and opportunities within accounts receivable processes.

The exercises outlined below are designed to assess candidates' abilities to analyze AR data, develop predictive models for payment behavior, design automation solutions for collections, and communicate complex technical concepts to financial stakeholders. These skills are fundamental to successfully implementing AI solutions that deliver measurable improvements to accounts receivable performance metrics.

By incorporating these work samples into your interview process, you'll gain deeper insights into candidates' capabilities beyond what appears on their resumes. You'll observe firsthand how they approach AR-specific challenges, their technical problem-solving skills, and their ability to translate AI concepts into business value for the finance organization.

Activity #1: AR Payment Prediction Model Design

This exercise evaluates a candidate's ability to design a machine learning solution for predicting customer payment behavior—a critical application of AI in accounts receivable that helps optimize collection strategies and improve cash flow forecasting. Candidates will demonstrate their understanding of relevant data features, appropriate modeling approaches, and how to translate technical concepts into business value.

Directions for the Company:

• Provide the candidate with a sanitized dataset containing historical customer payment information (invoice amounts, payment dates, days to payment, customer segments, etc.).
• Include a brief description of your current AR process and key challenges related to payment unpredictability.
• Allow 45-60 minutes for this exercise.
• Prepare questions about model selection, feature engineering, and implementation considerations.
• Have a technical team member and an AR team member present to evaluate both technical soundness and business applicability.

Directions for the Candidate:

• Review the provided AR dataset and business context.
• Design a machine learning approach to predict which customers are likely to pay late or default.
• Create a one-page diagram or flowchart showing your proposed solution architecture.
• Identify the key data features you would use and explain why they're relevant.
• Describe how you would measure the success of your model and integrate it into existing AR workflows.
• Be prepared to explain your approach in non-technical terms to AR stakeholders.

Feedback Mechanism:

• Provide feedback on the technical soundness of their approach (e.g., model selection, feature engineering, evaluation metrics).
• Offer constructive feedback on one aspect that could be improved (e.g., missing an important feature, overlooking a practical implementation challenge).
• Allow the candidate 10 minutes to revise their approach based on the feedback, focusing specifically on the improvement area identified.

Activity #2: Cash Application Automation Solution

This exercise assesses the candidate's ability to design an AI solution for automating cash application—one of the most time-consuming and error-prone processes in accounts receivable. Candidates will demonstrate their understanding of document processing technologies, pattern recognition, and how to measure ROI for automation initiatives.

Directions for the Company:

• Provide samples of payment remittance documents (with sensitive information redacted) that show various formats from different customers.
• Include a brief description of your current manual cash application process and key pain points.
• Share basic metrics about your cash application process (e.g., average time per transaction, error rates, volume).
• Allow 45-60 minutes for this exercise.
• Have both technical and finance team members present for the evaluation.

Directions for the Candidate:

• Review the sample remittance documents and process description.
• Design an AI-powered solution to automate the matching of incoming payments with open invoices.
• Create a simple presentation (3-5 slides) outlining:
• Technologies you would leverage (e.g., OCR, NLP, pattern matching)
• How your solution would handle exceptions and edge cases
• Implementation approach and timeline
• Expected ROI and how you would measure success
• Be prepared to present your solution and answer questions about technical feasibility and business impact.

Feedback Mechanism:

• Provide positive feedback on one aspect of their solution design (e.g., innovative approach to handling exceptions, clear ROI calculation).
• Offer constructive feedback on one area that could be improved (e.g., overlooking a particular challenge, unrealistic implementation timeline).
• Ask the candidate to revise their approach to address the improvement area, allowing 10 minutes for adjustments.

Activity #3: AR Data Analysis and Insight Generation

This exercise evaluates a candidate's ability to extract meaningful insights from accounts receivable data—a fundamental skill for developing effective AI solutions in this domain. Candidates will demonstrate their data analysis capabilities, business acumen, and ability to identify opportunities for process improvement through AI.

Directions for the Company:

• Provide a sanitized dataset containing AR metrics (e.g., DSO by customer segment, aging reports, collection effectiveness).
• Include basic information about your company's industry, customer segments, and payment terms.
• Prepare a list of current AR challenges your organization is facing.
• Allow 60 minutes for this exercise.
• Provide access to a data analysis tool (Excel, Python notebook, or similar).

Directions for the Candidate:

• Analyze the provided AR dataset to identify patterns, trends, and potential issues.
• Identify 3-5 key insights that could inform AR optimization strategies.
• For each insight, propose an AI-driven solution that could address the underlying issue.
• Create a brief report or dashboard visualizing your findings.
• Prioritize your proposed solutions based on potential business impact and implementation feasibility.
• Be prepared to present your analysis and recommendations.

Feedback Mechanism:

• Highlight one particularly valuable insight or recommendation from their analysis.
• Provide constructive feedback on one aspect that could be improved (e.g., missing an important pattern in the data, proposing a solution that might be difficult to implement).
• Allow the candidate 15 minutes to refine their analysis or recommendations based on the feedback.

Activity #4: AR Process Optimization Planning

This exercise assesses the candidate's ability to develop a comprehensive plan for implementing AI across the accounts receivable function—demonstrating their strategic thinking, project planning skills, and understanding of change management in finance operations.

Directions for the Company:

• Provide an overview of your current AR processes, systems, and key performance metrics.
• Include information about current pain points and business objectives (e.g., reduce DSO, improve cash application accuracy).
• Share any relevant constraints (budget, timeline, technology limitations).
• Allow 60-90 minutes for this exercise.
• Have representatives from finance leadership available to answer questions.

Directions for the Candidate:

• Develop a 12-month roadmap for implementing AI solutions across the accounts receivable function.
• Identify 3-5 high-impact AI use cases for AR optimization.
• For each use case, outline:
• Business objective and expected impact
• Data requirements and potential sources
• Technical approach and required resources
• Implementation timeline and key milestones
• Success metrics and measurement approach
• Create a presentation (5-7 slides) summarizing your plan.
• Be prepared to discuss prioritization decisions, potential challenges, and change management considerations.

Feedback Mechanism:

• Provide positive feedback on one aspect of their plan (e.g., thoughtful prioritization, comprehensive success metrics).
• Offer constructive feedback on one area that could be improved (e.g., overlooking a critical dependency, underestimating change management needs).
• Allow the candidate 15 minutes to revise their plan based on the feedback, focusing specifically on the improvement area identified.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each exercise requires 45-90 minutes to complete, plus time for feedback and discussion. We recommend selecting 1-2 exercises most relevant to your specific needs rather than attempting all four in a single interview. You might consider having candidates complete one exercise remotely before an onsite interview, then conduct a second exercise in person.

Should we provide these exercises to all candidates or only finalists?

These exercises require significant time investment from both candidates and your team. We recommend using them with shortlisted candidates who have already passed initial screening interviews. This ensures you're investing time with candidates who have demonstrated baseline qualifications and serious interest in the role.

How should we adapt these exercises for candidates with different experience levels?

For more junior candidates, consider providing additional structure and guidance. You might simplify the datasets, provide more specific requirements, or focus on technical implementation rather than strategic planning. For senior candidates, emphasize broader strategic thinking and business impact, and potentially increase the complexity of the scenarios.

What if our company doesn't have sample AR data to share with candidates?

If sharing your actual data isn't possible due to confidentiality concerns, you can create synthetic datasets that mirror the patterns and challenges in your AR processes. Alternatively, there are publicly available financial datasets that can be adapted for these exercises. The key is ensuring the data represents realistic AR scenarios relevant to your business.

How should we evaluate candidates who propose solutions different from our current approach?

Different approaches can offer valuable new perspectives. Evaluate candidates on the soundness of their reasoning, not just alignment with your existing methods. Consider whether their approach: 1) demonstrates understanding of AR fundamentals, 2) addresses the core business challenge, 3) is technically feasible, and 4) shows creative problem-solving. Sometimes the most valuable candidates are those who bring fresh perspectives.

Should we expect candidates to have specific AR domain knowledge, or is general AI expertise sufficient?

The ideal candidate will have both AI expertise and AR domain knowledge, but this combination is rare. Focus on evaluating candidates' ability to apply their technical skills to AR problems and their aptitude for learning domain-specific concepts. During the exercises, assess how quickly they grasp AR terminology and processes, and how effectively they translate technical concepts for finance stakeholders.

Finding the right talent to implement AI solutions for accounts receivable optimization requires evaluating both technical expertise and domain understanding. These work samples provide a structured approach to assessing candidates' abilities to apply AI concepts to real-world AR challenges, design practical solutions, and communicate effectively with finance stakeholders. By incorporating these exercises into your interview process, you'll gain deeper insights into candidates' capabilities and make more informed hiring decisions.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI Job Descriptions generator, AI Interview Question Generator, and AI Interview Guide Generator.

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