AI & ML
Privacy-first approach to personalisation Maintaining privacy while harvesting rich behavioural data presents significant challenges for financial institutions. Dearman advocates for a structured approach to this balancing act.
“ Financial institutions must adopt consent-driven personalisation and compliance by design principles, ensuring that data usage is transparent, ethical and aligned with privacy regulations such as GDPR,” says Mark.
He further explains that techniques like anonymisation and tokenisation allow firms to extract meaningful insights without exposing personally identifiable information. Additionally, federated learning enables AI models to analyse data locally without transferring sensitive information.
“ By embedding privacy-first principles, AI-driven financial product management platforms empower financial institutions to deliver hyper-personalised services while maintaining compliance and consumer trust,” Mark continues. This balance ensures that banks can leverage behavioural insights responsibly, fostering stronger customer relationships without compromising data security.
The implementation of real-time encryption and secure data-sharing frameworks further strengthens privacy measures. These technological safeguards help financial institutions maintain the highest standards of privacy while still delivering the personalised experiences customers increasingly expect.
Black swan events and rapid market shifts One of the enduring challenges for predictive analytics is addressing“ black swan” events- rare, unpredictable occurrences with severe consequences that have no historical precedent in existing datasets.
Traditional predictive models operate on the assumption that historical patterns reliably indicate future developments. While this holds true under stable conditions, it falters during unprecedented market disruptions.
“ Most predictive models are based on the premise that history is a strong predictor of the future. While the most common predictive model in finance is a simple straight-line linear regression, more advanced techniques using models such as xgBoost and Prophet are leveraged by financial institutions globally,” Alan explains.
These models can detect deviations from established patterns but struggle to predict truly exceptional events.
To better capture these rare occurrences, Alan recommends a layered approach:“ A common method to better capture black swan events is to combine simple models for key inputs with‘ physical-based’ models that calculate outcomes based on these more predictable factors.”
He provides a practical example:“ Rather than attempting to directly predict a day of $ 0 business income, you could use a weather forecasting model to predict high winds and then
112 April 2025