Designing data-reliant risk models without data | Data Days 2022

TL;DR

Data engineers discuss their experiences in designing risk models and optimizing data-driven decision-making processes for insurtech and financial services companies.

Transcript

hello hello welcome everyone welcome to the data engineering track uh or welcome to the tech side the the data tech site so yeah i'm jorgos i'm a data engineer here for the day and this is actually my fourth database if i if i count uh right and yeah that hits at least personally my highlights every year so i hope you enjoyed as much so today in th... Read More

Key Insights

• 🖐️ Data engineers play a crucial role in designing risk models and optimizing data-driven decision-making processes in insurtech and financial services companies.
• 🚱 Collaboration between data engineers and non-technical stakeholders, such as underwriters, is essential to ensure accurate and meaningful results.
• 💦 The challenges of working without historical data require innovative approaches, including simulations and expert judgment.
• 🐕‍🦺 Continuous refinement of risk models through real-world data collection is necessary to improve accuracy and optimize decision-making processes in insurtech and financial services.

Questions & Answers

Q: How do data engineers work with non-technical underwriters in designing risk models without historical data?

Data engineers collaborate closely with underwriters and provide them with accessible and understandable metrics derived from complex data analysis. The underwriters then utilize their expertise to adjust and validate the outcomes, creating a feedback loop for continuous refinement.

Q: How does recap collect relevant data for assessing financial health and risk?

Recap collects data from various sources, such as bank accounts and subscription management services, and combines it with accounting data provided by customers. This data is then used to assess financial health and determine the interest rates for providing funds.

Q: What challenges do data engineers face in insurtech and financial services companies?

Data engineers often encounter challenges in obtaining and processing data, as well as interpreting the results in a meaningful and actionable way. Additionally, working with non-technical stakeholders can require effective communication and collaboration to align their expertise with technical solutions.

Q: How do insurtech and financial services companies optimize their risk models and data-driven decision-making?

Companies like Baobab and Recap continuously refine their risk models by collecting real-world data to validate and optimize their assumptions. They also allow for manual adjustments by underwriters, providing a flexible approach to tailor the model's outcomes to real-world scenarios and customer needs.

Summary & Key Takeaways

• The panelists discuss their roles as data engineers in insurtech and financial services companies.

• They highlight the importance of designing risk models without access to historical data and the challenges of working with non-technical underwriters.

• The panelists explain their respective companies' business models and how data analysis plays a crucial role in their operations.

• The role of data-driven decision-making in the insurance and financial industry is emphasized, with a focus on risk assessment and optimization.