Director of Credit Risk Modelling and Analytics

As the Director of Credit Risk Modelling and Analytics at Amplifi, your responsibilities will include leading the development of credit frameworks and lending strategies, directing the analysis and design of advanced credit risk models, and fostering a culture of collaboration among teams to ensure data-driven solutions that align with regulatory compliance. You will also be tasked with mentoring a high-calibre analytical team and delivering insights to the Executive Committee and Board members to guide strategic decisions.

To be considered for the Director of Credit Risk Modelling and Analytics role at Amplifi, you should possess over 10 years of experience in credit risk modelling and analytics within the financial services sector, preferably in consumer lending. Strong leadership capabilities, analytical problem-solving skills, and a solid understanding of regulatory landscapes are essential, as is proficiency in SQL and familiarity with tools such as Python and R.

At Amplifi, data-driven growth in credit risk management is ensured through the strategic development of advanced credit models and analytics frameworks. The Director of Credit Risk Modelling and Analytics plays a key role in monitoring portfolio performance, providing actionable insights, and leading initiatives that balance risk management with sound lending practices. This approach is further enhanced through collaboration with various functional teams to innovate solutions that align with customer needs and regulatory standards.

The Director of Credit Risk Modelling and Analytics at Amplifi will lead a high-performing team of analytical talent, focusing on mentoring and skill development. The role requires close collaboration with cross-functional teams including product, engineering, and commercial functions, to drive excellence in credit risk strategy and governance while also engaging with senior stakeholders like the Executive Committee and Board members.

As the Director of Credit Risk Modelling and Analytics at Amplifi, you can expect a competitive salary along with 25 days of annual leave. Additional perks include private health cover, a cycle-to-work scheme, discounted shopping, and subsidised gym membership. The role also offers hybrid working options, allowing you to maintain a healthy work-life balance while making a meaningful impact in the FinTech space.

When leading a team in credit risk modelling, it’s essential to combine a clear vision with strong mentorship. I would prioritize open communication, foster an environment for innovation, and set regular check-ins to ensure alignment with the organization’s goals. Using my experience, I would guide my team through the complexities of model development while encouraging their professional growth.

Certainly! At my previous position, I developed a machine learning-based credit risk model that integrated traditional data sources with emerging ones, like Open Banking data. This model enhanced underwriting accuracy and reduced default rates by maximizing predictive capabilities. I also ensured rigorous testing and validation phases were included to fine-tune the model effectively.

For compliance in credit risk governance, I would implement a robust governance framework that includes regular audits, comprehensive reporting structures, and constant updates on regulatory changes. Training sessions for the team on compliance requirements would also be essential, ensuring everyone understands the implications and is aligned with best practices.

To ensure that the credit risk strategy aligns with business growth objectives, I would conduct thorough market analysis to identify growth opportunities while continuously engaging with stakeholders to gather insights. By leveraging data-driven analytics and adapting strategies to evolving consumer needs, we can create frameworks that responsibly facilitate growth without compromising risk management.

I have substantial experience with machine learning techniques, utilizing them in the development of predictive models for credit risk analysis. By employing algorithms like logistic regression, decision trees, and neural networks, I’ve been able to enhance risk assessment processes. I believe integrating these techniques will lead to more effective strategies that adapt to changing market conditions.

Engaging stakeholders for credit risk initiatives requires clarity and strategy. I would prepare in-depth presentations, ensuring to tailor the information to the audience's expertise. Regular updates and feedback sessions would be organized to create an ongoing dialogue, fostering trust and alignment on key initiatives.

One of the biggest challenges in credit risk modelling today is the rapid evolution of data sources and regulatory requirements. Adapting to these changes while maintaining robust compliance and accurate risk assessments can be complex. Continuous learning, agility, and an investment in technology are crucial in overcoming these hurdles.

Absolutely! In a previous role, my analysis of portfolio performance revealed a growing trend of late payments within a specific demographic. I presented this data to the executive team, recommending a tailored outreach program to educate this demographic on responsible borrowing. This initiative not only improved repayment rates but also enhanced customer satisfaction.

To monitor portfolio performance effectively, I would employ a combination of key performance indicators (KPIs) and predictive analytics. Regularly analyzing metrics such as default rates, payment patterns, and emerging trends would provide a comprehensive view. Implementing dashboards for real-time insights would also be pivotal, allowing for timely decision-making.

Staying current with regulatory changes in consumer lending involves continuous education and networking. I regularly attend industry seminars, subscribe to financial regulatory newsletters, and participate in forums. Furthermore, I maintain relationships with compliance experts to ensure I remain informed on upcoming legislation and its implications for credit risk management.