Our latest paper, from our lab member Mahsa Tavakoli, is out. This was in collaboration with Prof. Rohitash Chandra at UNSW in Australia

When it comes to understanding how credit ratings are determined, most studies have focused only on numbers—like financial ratios and balance sheets. But in the real world, a lot of important information is found in written documents, such as company reports or news articles. In our study, we looked at how combining both numbers and text using deep learning models could improve the prediction of credit ratings. We tested different types of models and ways to blend the data, and we found that a model based on CNN (a type of deep learning model) that mixes information early and in the middle of the process gave the best results. Surprisingly, simpler models worked better than more complex ones, and the text information—like what’s written about a company—was even more useful than numbers in predicting credit ratings.

We also checked how reliable our model is when things change, such as during big events like the COVID-19 pandemic. The results showed our model stayed stable and still made good predictions, especially when using text data. Another interesting finding was that credit ratings given by Moody’s were more accurate over time than those from other rating agencies. This could help financial institutions trust those ratings more when making decisions. Overall, our study shows that using both text and numbers together can lead to smarter and more reliable credit rating predictions. It also opens the door for better tools that can help people make confident financial decisions—even during uncertain times.

The paper was published a bit ago at Applied Soft Computing. You can find the paper Open Access here, and its ArXiV version here.

We had a great time attending the 2023 INFORMS Annual Meeting that took place between October 15^th to October 18^th in Phoenix, AZ. This is one of the largest conferences in the field of OR, with 6,000+ attendees and 1,400+ sessions.

The BAL had a strong presence at the conference with four presentations:

• On October 15^th, we had two presentations:
  • Cristián Bravo presented the work with Kamesh Korangi and Christophe Mues on “Large-Scale Portfolio Optimization using Graph Attention Networks”.

• Daniel Abib presented the work with Cristián Bravo, Raffaella Calabrese, and María Óskarsdóttir on “Optimal Feature Split in Classification Models with Dependency”.

• On October 17^th, Sahab Zandi presented the work with Kamesh Korangi, María Óskarsdóttir, Christophe Mues, and Cristián Bravo on “Leveraging Dynamic Multilayer Networks for Modelling Credit Risk Contagion in SMEs”.

• On October 18^th, Yuhao (Jet) Zhou presented the work with Collins Ntim, María Óskarsdóttir, Matthew Davison, and Cristián Bravo on “Uncovering the Network Power Gap: A Deep Learning Approach to Investigating Gender Disparities in the Boardrooms of Canadian Public Firms”.

We also had fun trying a few good restaurants in Scottsdale, AZ!

This was a great chance to showcase the preprints that will come out in the next few months. Stay tuned for them!

We had a great time attending the Credit Scoring and Credit Control Conference XVIII that took place between August 30^th to September 1^st in Edinburgh, UK. This conference bridges the academic/practitioner divide and is the world’s premier conference for credit scoring and credit risk related topics.

The BAL had a strong presence at the conference with six presentations:

• On August 30^th, Cristián presented the work with our PhD student Mahsa Tavakoli, cosupervised by Rohitash Chandra from UNSW, on “Multi-Modal Deep Learning for Midcap Credit Rating Prediction Using Text and Numerical Data”.
• On August 31^st we had two presentations:
  • Our collaborator Prof. María Óskarsdóttir from Reykjavík University, Iceland, presented the work by our PhD students Sahab Zandi and Kamesh Korangi, cosupervised by Prof. Christophe Mues from Southampton University and Cristián, titled “Credit Scoring with Dynamic Multilayer Graph Neural Networks”.
  • Cristián presented the work led by our PhD student Sherly Alfonso Sánchez, cosupervised by Prof. Kristina Sendova here at Western, called “Causal Learning for Credit Limit Adjustment in Revolving Lending Under Adversarial Goals”.
• On September 1^st, we had three:
  • Daniel Abib, who joined earlier this year as a postdoc at the Lab, presented the work coauthored with Prof. Raffaella Calabrese for Edinburgh University, Prof. María Óskarsdóttir, and Cristián. The work was called “Optimal Feature Split in Credit Risk Models with Dependency”.
  • Our PhD student Kamesh Korangi presented the work from his PhD, coauthored with Christophe Mues and Cristián, on “Deep Temporal Graph Networks for Behavioural Scoring Prediction in Revolving Credit Lines”.
  • Our PhD student Sahab Zandi presented the work with coautored with Kamesh, and cosupervised by Prof. María Óskarsdóttir, Prof. Christophe Mues, and Cristián. These last two works are part of the collaboration with one of the largest consumer banks in the world. Sahab’s presentation is titled “Modelling Credit Risk Contagion for SMEs over Supply Chains using Dynamic Multilayer Networks”.

The conference provided a great opportunity to meet and network with people in the field of credit risk from both academia and industry. We were honestly surprised and happy with the reception that we had from the conference attendants. We had many interesting talks and we look forward to what will come out of these chats!

We also had a blast having a reunion with some friends and colleagues after a while in Edinburgh!

Our research focuses on using reinforcement learning (RL) to address the credit limit modification problem for companies offering credit card products. This involves two main challenges: defining the RL problem for this specific task and training the RL agent without conducting online experiments with customers.

To define the RL problem, we consider the financial history of credit card holders and the expected losses due to defaults when deciding whether to increase or maintain their credit limits. The actions available are increasing the limit or keeping it the same. We calculate the reward function based on the expected profit, considering the revolving aspect of credit card usage. This differs from previous studies that overlooked this aspect in profit calculations.

To train the RL agent offline, we use a two-stage model to simulate the balance after taking an action. This involves selecting the balance type and predicting the balance amount using a regressor model. Through our experiments, we found that our trained Double-Q learning agent outperformed other strategies, including the one used by Rappi, a Latin American fintech company known for its delivery and commerce services that has also ventured into banking with its RappiCard credit card, and that was our collaborator in this research.

Our research contributes by providing a conceptual framework for applying RL to credit limit adjustments and emphasizes data-driven decision-making rather than relying solely on expert judgments. Furthermore, we discovered that incorporating additional predictors did not improve the performance of our simulator. This implies that fintech companies do not necessarily have an advantage over traditional banking institutions in this specific task.  Figure 1  provides an overview of the proposed methodology’s general workflow.

Figure 1: Methodology’s general workflow.

Link to the working paper: https://arxiv.org/abs/2306.15585