#BehindTheScenes – Get to know David Jaimes, Risk data analyst at ID Finance

Did you know that the Risk data analysis department is a key area for any financial company and also for ID Finance? This area is responsible for identifying and accurately quantifying risks of several types. It provides valuable insights about potential vulnerabilities and enables the company to take measures that mitigate risks, thus ensuring its operational efficiency and contributing to profitability. In this regard, the risk data analysis teams assess the probability and potential impact of different risks, such as market volatility, credit defaults, liquidity shortages, operational failures or regulatory compliance issues.

To gain better insights of the activity of our Risk Data Analysis team we would like to invite you to accompany us once more behind the scenes of ID Finance and introduce you to David Jaimes. He has been working for the company for over one and a half year. He has been working for the financial industry for almost seven years, always in data analysis and data science for credit risk departments.

David, could you tell us a bit more about your professional background as data analyst?

I started as a junior analyst at one of the biggest banks in Colombia and then changed to the fintech sector when I joined ID Finance. I’m highly interested in reading about new developments in data science, IA and general investigations related to data analysis.

And what exactly does a risk data analysis department do?

The role of the risk data analysis department is crucial for financial businesses since it has the responsibilities of identifying, assessing and mitigating risks related with the financial activities and the operations.  Specifically for credit risk this means assessing and managing the risk associated to the creditworthiness of borrowers or counterparties.

What is its relevance for a fintech company such as ID Finance?

For any fintech, but specially for one facing an aggressive expansion due to a well-designed product such as our Plazo app there are risks that are present every day. Risks such as fraud, deterioration of repayment behaviour or loss of predictability in models used to assess the quality of the borrowers can result in a loss of income for the company which of course can damage the company itself.

Building and keeping monitors on credit assessment, credit scoring and portfolio monitoring is vital to reduce such risks and maintain a healthy portfolio that permits a healthy grow of the company.

How does technology contribute to risk data analysis?

Technology in credit risk data analysis is vital. To assess and mitigate credit risk the team needs high volumes of data which are viable to process due to technology. Some of the key processes that are highly impacted by technology are the Data Aggregation, advanced analytics, machine learning, automation of data visualization and reporting.

And what do you think makes ID Finance unique in Risk data analysis?

At ID Finance the best-in-class practices are always related to our team: we are unique because these processes are maintained daily by amazingly competent professionals that react to any failure related to data quite fast. The team works really hard to keep data, reporting and automation workflows organized and working.  The use of updated technologies is key, and our team always looks forward to using the latest tools for each of the processes. Further than our team, ID Finance has implemented a robust data management system that permits the aggregation of the information that we gather from different sources. We invest time every week on validating our models and policies to have a great accuracy to minimize the risks of the company and finally we have a reporting system that allow us to identify and avoid manual and repetitive tasks within credit risk analysis workflows and implement automation tools and technologies to reduce human errors and enhance productivity.

What role does AI have in risk data analysis?

The primary way in which AI is related to the risk department is machine learning. Normally we separate these two concepts but in reality, machine learning is a small part of the developments of artificial intelligence. We are currently developing some of our models with the use of these technologies enabling to increase the power of predictability. However, there are many challenges still there since the use of these technologies in the financial industry is not completely accepted as there is a high uncertainty in comparison with traditional models. However, this is changing daily and in the future these types of models will command the decision making for every fintech.

And what opportunities do you see in the use of AI for your department?

Regarding more recent technologies such as generative AI (such as chat GPT), the usage is still low within the industry, but it represents a monumental opportunity of development to get real time insights with a higher level of detail on predictive analytics. Some of the opportunities that are already arising are related with forecasting and reporting since generative AI can help to develop formulas and queries and even suggest inputs for models of forecast.  This can be quite useful for any data analyst since it can help optimize certain functions.  However, this technology keeps developing every day. I’m certain that it will offer many other opportunities the near future to the data analysis departments.

How does a day look like for risk data analysis?

A day for a risk data analyst in a credit department consists in collecting and analyzing data, building, calibrating and monitoring models, creating and maintain reports for the assessment of risk and of course collaborating with team members. All of this to contribute to the risk management strategies that make ID Finance one of the fastest growing companies in Europe.

Sounds like a challenging activity… What do you like to do after an intense working day?

Beyond my passion for reading about work related subjects, what I enjoy most are the daily walks with my dog. I also love to read thrillers and listen to podcasts and music.

What do you like best of working at ID Finance?

What I like the most is the independence we are given to propose solutions, to explore the data and to bring insightful analysis to the management.  While we contribute to the growth of the company, we keep growing ourselves as professionals. At ID Finance we also have great and experienced professionals from whom to learn every day. At the same time spending time off with other colleagues from all over the world on teambuilding activities or afterworks gives us the opportunity to have lots of fun and find the perfect balance between personal and professional life within the company.

What recommendation would you make to someone joining the Risk Data Analysis team?

Always keep reading and learning about new trends in technologies related to data analysis, encourage a data-driven decision-making and continuously develop your analytical skills. Finally, in ID Finance the team is the center of the success so collaborate closely with your colleagues to enhance risk assessment and mitigation strategies.