Redefine Innovation with AI and ML in Banking

Optimised Customer Acquisition Strategy

Implemented AI-driven models to identify high-propensity customers for lending products, leading to significantly improved acquisition efficiency. The approach enabled better targeting, resulting in lower cost per acquisition (CPA) and an increased share of revolving accounts.

Smarter Suspicious Transaction Reporting

Leveraged advanced data mining tools to scan extensive transactional datasets and surface anomalies. By examining patterns across seasonal trends, customer demographics, and behavioural variables, analysts were able to enhance reporting quality and focus on high-risk clusters through intuitive visualisations.

Elevated Customer Spend through Channel Optimisation

Designed models to identify the most effective engagement channels for different customer segments. The solution considered factors like customer profitability, transaction trends, and channel economics to drive higher spend per card, aligned with strategic goals.

AI Workshops for Product Innovation

Used AI to generate dynamic customer personas and feedback loops, enabling teams to co-create in virtual environments. The approach improved the speed and relevance of product ideation, supporting faster time-to-market and stronger customer alignment.

Smarter Credit Risk, More Approvals

Applied machine learning techniques on bureau and internal data to forecast default risk. This facilitated better segmentation of applicants, allowing financial institutions to extend more approvals while maintaining healthy risk profiles.