Effective Work Samples for Evaluating AI Expense Anomaly Detection Skills

Expense anomaly detection using artificial intelligence represents a critical capability for modern finance departments and audit teams. Organizations implementing AI-powered expense monitoring can identify fraudulent transactions, policy violations, and unusual spending patterns that might otherwise go undetected. However, finding professionals who truly understand how to design, implement, and maintain these systems requires more than reviewing resumes or conducting standard interviews.

The technical complexity of AI expense anomaly detection demands hands-on evaluation. Candidates may claim proficiency with machine learning algorithms, data preprocessing techniques, and financial systems integration, but without practical assessment, it's difficult to verify these skills. Work samples provide tangible evidence of a candidate's ability to solve real-world problems in this domain.

Effective work samples for AI expense anomaly detection should evaluate multiple dimensions: technical AI knowledge, understanding of financial data, anomaly detection methodology, and the ability to communicate findings to stakeholders. The exercises should mirror actual job responsibilities while remaining manageable within an interview context.

By implementing the following work samples, organizations can significantly improve their ability to identify candidates who can successfully implement AI-powered expense monitoring systems. These exercises evaluate not just theoretical knowledge but practical application skills, problem-solving approaches, and the ability to translate technical capabilities into business value.

Activity #1: Anomaly Detection System Design

This exercise evaluates a candidate's ability to architect an AI-based expense anomaly detection system. It tests their understanding of the overall workflow, from data ingestion to alerting, and their knowledge of appropriate algorithms and techniques for different types of expense anomalies.

Directions for the Company:

• Provide the candidate with a written brief describing a fictional company's expense management challenges. Include details about company size, expense volume, existing systems, and specific concerns (e.g., T&E fraud, duplicate payments, policy violations).
• Allow the candidate 30-45 minutes to design a solution architecture.
• Prepare a whiteboard or digital drawing tool for the candidate to sketch their design.
• Have a technical evaluator and a finance/business stakeholder present to ask questions.

Directions for the Candidate:

• Review the company brief and design an end-to-end AI system for detecting expense anomalies.
• Create a diagram showing the system architecture, including data sources, preprocessing steps, model components, and output mechanisms.
• Specify which algorithms or techniques you would use for different types of anomalies.
• Explain how your system would handle both known fraud patterns and novel anomalies.
• Be prepared to discuss implementation considerations, including technical requirements and potential challenges.

Feedback Mechanism:

• Provide feedback on the strengths of the candidate's design, particularly noting any innovative approaches or thorough consideration of the business context.
• Offer one specific improvement suggestion, such as an overlooked data source, a more appropriate algorithm for a particular anomaly type, or a refinement to the alerting mechanism.
• Ask the candidate to revise their design based on the feedback, focusing specifically on the improvement area identified.

Activity #2: Anomaly Detection Algorithm Implementation

This exercise tests the candidate's hands-on technical skills in implementing AI algorithms for expense anomaly detection. It evaluates coding ability, data manipulation skills, and understanding of appropriate machine learning techniques for this specific domain.

Directions for the Company:

• Prepare a dataset of anonymized expense transactions (300-500 records) with deliberately inserted anomalies of different types.
• Provide a Jupyter notebook or similar environment with basic data loading code already set up.
• Include a clear description of the dataset schema and the task requirements.
• Allow 60-90 minutes for completion.
• Ensure the candidate has access to necessary libraries (pandas, scikit-learn, etc.).

Directions for the Candidate:

• Analyze the provided expense dataset to identify potential anomalies.
• Implement at least two different anomaly detection techniques:

1. A rule-based approach for detecting specific policy violations
2. A machine learning approach for detecting unusual patterns

• Document your code with comments explaining your approach and reasoning.
• Create visualizations that would help stakeholders understand the detected anomalies.
• Prepare a brief summary of your findings, including the types of anomalies detected and any patterns observed.

Feedback Mechanism:

• Provide positive feedback on the candidate's technical implementation, highlighting effective code, appropriate algorithm selection, or insightful analysis.
• Suggest one area for improvement, such as a more efficient implementation, better feature engineering, or an alternative algorithm that might yield better results.
• Give the candidate 15 minutes to refine their approach based on the feedback.

Activity #3: Anomaly Investigation and Root Cause Analysis

This exercise evaluates the candidate's ability to investigate detected anomalies, determine root causes, and recommend appropriate actions. It tests analytical thinking, domain knowledge of expense processes, and problem-solving skills.

Directions for the Company:

• Create a scenario document describing 3-5 expense anomalies that have been flagged by an AI system.
• For each anomaly, provide transaction details, historical context, and relevant user information.
• Include some red herrings and ambiguous cases to test discernment.
• Prepare a template for the candidate to document their analysis and recommendations.
• Allow 45-60 minutes for completion.

Directions for the Candidate:

• Review each flagged expense anomaly and conduct a thorough investigation.
• For each case, document:

1. Whether you believe it's a true anomaly or a false positive
2. The evidence supporting your determination
3. The likely root cause (fraud, error, policy misunderstanding, etc.)
4. Recommended actions (approve, reject, escalate, modify system)

• Prioritize the cases based on risk and impact to the organization.
• Suggest any improvements to the detection system based on your findings.

Feedback Mechanism:

• Provide positive feedback on the candidate's analytical approach, particularly noting any insights that demonstrate deep understanding of expense processes or fraud patterns.
• Suggest one area for improvement, such as consideration of additional context, alternative explanations, or more nuanced recommendations.
• Ask the candidate to revisit one specific case and refine their analysis based on the feedback.

Activity #4: Stakeholder Communication and Model Explanation

This exercise assesses the candidate's ability to communicate complex AI concepts to non-technical stakeholders and explain the value and limitations of expense anomaly detection systems. It tests communication skills, business acumen, and the ability to translate technical details into business value.

Directions for the Company:

• Prepare a scenario where the candidate must present findings from an expense anomaly detection system to a mixed audience of finance leaders and executives.
• Provide a summary of detection results, including true positives, false positives, and potential savings.
• Include some challenging questions that stakeholders might ask about the system's reliability, ROI, and implementation requirements.
• Allow 30 minutes for preparation and 15 minutes for presentation/Q&A.
• Have evaluators role-play as different stakeholders with varying levels of technical understanding.

Directions for the Candidate:

• Review the anomaly detection results and prepare a 10-minute presentation for senior stakeholders.
• Your presentation should:

1. Explain how the AI system works in non-technical terms
2. Summarize the key findings and their business impact
3. Address the system's accuracy and reliability
4. Recommend next steps for improving expense controls

• Create 1-2 visualizations that effectively communicate the value of the system.
• Be prepared to answer questions about technical details, implementation considerations, and ROI.

Feedback Mechanism:

• Provide positive feedback on the candidate's communication effectiveness, particularly noting clarity of explanation, business focus, or handling of technical questions.
• Suggest one area for improvement, such as better visualization, more concrete ROI articulation, or clearer explanation of a technical concept.
• Ask the candidate to refine one specific portion of their presentation based on the feedback.

Frequently Asked Questions

How much technical setup is required for these exercises?

For the algorithm implementation exercise, you'll need to prepare a dataset and technical environment. We recommend using anonymized data (or synthetic data) and setting up a cloud-based notebook environment that candidates can access during the interview. For the other exercises, the setup is minimal, requiring only document preparation and possibly whiteboarding tools.

Should we expect candidates to complete all four exercises?

No, these exercises are designed to be used selectively based on the specific role and the stage of the interview process. For most positions, choose 1-2 exercises that best align with the core responsibilities. The system design and algorithm implementation exercises are particularly valuable for technical roles, while the investigation and communication exercises are useful for roles that bridge technical and business functions.

How do we evaluate candidates who use different approaches than we expected?

This is actually a valuable outcome! Different approaches can reveal innovative thinking. Evaluate based on whether the approach is valid, well-reasoned, and effective for the problem at hand, not whether it matches your expected solution. The candidate's ability to explain and justify their approach is often more important than the specific technique used.

What if we don't have expertise in AI to evaluate the technical exercises?

If you lack internal AI expertise, consider having a technical consultant assist with the evaluation. Alternatively, focus on the design and communication exercises, which can be evaluated effectively by those with strong business understanding of expense management. The candidate's ability to explain their approach clearly can also serve as a proxy for technical competence.

How should we handle candidates who struggle with the time constraints?

Time pressure is a reality in most roles, but the primary goal is to assess capability, not speed. If a candidate is making good progress but needs more time, it's reasonable to extend the exercise or to evaluate based on what they accomplished within the timeframe. Focus on their approach and problem-solving process rather than just completion.

Can these exercises be conducted remotely?

Yes, all of these exercises can be adapted for remote interviews. Use collaborative tools like virtual whiteboards for the design exercise, cloud-based notebooks for the implementation exercise, and video conferencing for the communication exercise. Provide clear instructions and test the technical setup beforehand to ensure a smooth experience.

AI in expense anomaly detection represents a specialized skill set that combines technical expertise with domain knowledge. By using these practical work samples, you can identify candidates who not only understand the theory but can apply it effectively to real-world expense management challenges. These exercises help reveal a candidate's technical capabilities, analytical thinking, problem-solving approach, and communication skills—all critical components for success in implementing AI-powered expense monitoring systems.

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