Operationalizing Bayesian multi-state models and financial institution resources for business firm life cycle modelling

Like most living organisms, the life cycle of a business can be divided into distinct, complex phases. These life stages are determined by various internal and external factors such as financial resource availability, managerial ability, and market conditions. The ability to model firm life stages would help financial institutions (FIs) such as ATB identify and meet the time-specific needs and challenges of their business clients. However, properly analyzing the wealth of data collected by FIs is difficult.

An Alberta-based VAR Structural Model

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of changes in creditworthiness for industry sectors.

Slice Finder: Application to Stress Testing

The project aims to use state-of-the-art machine learning techniques to perform model validation. In particular, the intern will validate outcomes from risk assessment models for loan portfolios. The results will be employed to further the efficiency of ATB's internal stress testing models. The benefit for ATB financial will be the possibility to detect subsamples for which model fit might be poor, which will yield insights and, hopefully, improvement to stress testing.

Drivers of Time to Resolution, Application of LASSO Regression and Random Forest

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of default probabilities.

Dynamic Credit Scoring

Banks use a myriad of methodologies to inform their officers on credit extension decisions. One of the most employed approach is to summarize borrower creditworthiness by credit scores, which in turn depend on loan default probabilities. The probability of default depends both on borrower characteristic and on the overall state of the economy. The goal of this project is to create credit scores that are responsive to the expected state of the economy.

Forecasting Ability of Non-consumer Scorecards and their Ability to Predict Probability of Default

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of default probabilities.

Statistical machine learning methods applied to ATB data for debt collection optimization, small business lending decision modelling, and open banking initiatives

The intern will research new modelling technology to determine if the new models can make a significant improvement in servicing customers for loan approvals, debt collections, and open banking. The intern will work closely with the partner to understand the banking process and opportunity. The partner organization will receive several benefits from working with the innovative and knowledgeable intern including cross-training of techniques through collaboration, enhanced model accuracy, and enabling the partner to test new techniques.

Assessing statistical bias in credit markets, an application to SMEs

This research project aims to evaluate whether members of minority groups or women face higher barriers to access credit in the small and medium-sized enterprises credit market. The intern will analyze loan-level data provided by the business partner to evaluate whether these biases are detectable in the portfolio of SME loans of the business partner. Discrimination in credit allocation prevents efficient credit allocation, besides being demeaning for the individual subject to discrimination.

Credit Portfolio Management and Stress Testing Models Research & Development

Consistent with industry norms, ATB Financial conducts both mandatory and discretionary stress tests of the whole institution and of its credit portfolio. This project aims to contribute to the refinement of the in-house expertise on methodologies employed to measure credits risk and the overall level of risk of the institution. These activities normally requires management to provide an estimate for ATB’s financial performance, capital and liquidity position conditional on a set of predefined scenarios.

Statistical machine learning methods applied to ATB data for credit risk modelling

Machine learning (ML) is a method of training a computer to learn from data and predict future outcomes based on existing patterns in the data. This project aims to utilize various ML methods as new and potentially better analytics and predictive tools in the area of credit risk management for ATB. Given that data quality and flows change over time, a new framework built on Google Cloud Platform to update the machine learning models will also be developed.

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