Saving Over Time: Hierarchical AI Modelling of Longitudinal Multi-Frequency Banking Transaction Behaviour to Understand Savings Behaviour for Financial Resilience
Project Summary
Many UK households have few emergency savings, making unexpected costs or changes in income harder to manage. This challenge may be particularly relevant to credit union customers with limited financial headroom or irregular incomes. The project will use securely managed transaction data from credit union customers on Incuto’s platform to understand how saving behaviour changes over time. It will examine saving broadly, with particular attention to payroll-linked saving, where part of an employee’s pay is transferred into savings.
The project will develop a new, explainable AI framework comprising linked specialist models. Rather than treating all saving behaviour as the same, different models will focus on when people start, continue, reduce, pause, withdraw or resume saving. The framework will combine longer-term monthly patterns with day-to-day transaction activity, capturing broader patterns and immediate changes.
The project will also explore how saving behaviour relates to financial resilience, using indicators such as limited financial headroom, irregular income, repeated borrowing and missed payments. Continuing to save will not automatically be viewed as beneficial, nor will pausing or withdrawing be viewed as failure. The findings could help credit unions, employers, financial-service providers and policymakers design more flexible, responsible and effective support.
Meet The Team

Heather He
Aston University
Lecturer in Business Analytics

Felipe Costa Sperb
Kingston University London
Senior Lecturer in Business Analytics

Shreyank Narayana Gowda
University of Nottingham
Assistant Professor (Computer Science)

Jia Shao
University of Birmingham
Assistant Professor (Mathematics)

Alessio Azzutti
University of Glasgow
Lecturer in Law and Technology (Law)
