Director of Credit

The Director of Credit at Capchase is responsible for overseeing the entire credit lifecycle, including credit policy and risk appetite, portfolio management, risk modeling and analytics, and automation of key processes. This role involves leveraging data to assess creditworthiness and inform strategic decisions, ensuring robust risk management while maximizing profitability.

Candidates for the Director of Credit position at Capchase are expected to have at least 6 years of experience in B2B risk management, preferably within fintech or a similar high-growth environment. Key qualifications include a strong understanding of B2B financing, credit risk management expertise, proficiency in SQL, and strong analytical skills.

The Director of Credit plays a crucial role in Capchase's growth by defining credit policies, optimizing portfolio performance, and mitigating risks through data-driven strategies. By managing the credit lifecycle effectively, this position ensures that the company can offer flexible financing options, incentivizing software companies and ultimately driving more sales.

Success in the Director of Credit role at Capchase requires a data-driven mindset, technical skills in SQL and Excel, and a passion for continuous improvement. Additionally, collaboration skills are essential, as this position requires working closely with cross-functional teams to enhance credit processes and address challenges effectively.

A typical day for the Director of Credit at Capchase involves analyzing credit data to optimize portfolios, collaborating with technical teams on process automation, and building relationships with vendors and buyers. The role is dynamic, combining strategic planning, risk assessment, and data analysis to drive the company's financing initiatives.

When answering this question, focus on specific examples from your past positions that highlight your experience in B2B credit risk management. Discuss the strategies you've implemented, the data analytics you've used, and any significant outcomes you achieved that contributed to company objectives.

In your response, describe the key components of an effective credit policy, such as risk limits, assessment criteria, and recovery strategies. Emphasize the importance of aligning the policy with business goals and utilizing data analytics to continually refine its effectiveness.

Here, discuss the analytical tools and methodologies you've used to create risk models. Highlight your proficiency in data science and how you’ve leveraged these tools to inform credit decisions and improve portfolio performance.

Demonstrate your commitment to continuous learning by sharing the resources you use to stay informed about credit risk regulations, such as industry publications, networking with peers, and attending relevant conferences or webinars.

In your answer, share a specific example of a credit recovery strategy that was successful. Explain the challenges faced, the strategy employed, and the positive outcomes that resulted from your approach.

Explain your approach to relationship building, emphasizing the importance of communication and trust. Include tactics you've used in the past, such as regular check-ins, transparent negotiations, and proactive problem-solving.

Discuss specific challenges you've encountered, such as economic downturns or regulatory changes, and explain the steps you took to overcome them. This response should highlight your problem-solving skills and adaptability.

Provide a step-by-step overview of your process for assessing creditworthiness, including key metrics and data points you analyze. Be sure to emphasize your analytical skills and any tools you utilize in this evaluation.

Share your strategies for effective prioritization, such as using risk assessment frameworks to identify high-impact accounts versus smaller ones. Discuss how you maintain organization and ensure thorough analysis despite a large workload.

Focus on a specific instance where you automated a credit-related process. Explain the initial challenges, the steps taken to implement automation, and the resulting efficiency gains or error reductions.