Giving Retail Banking An AI Upgrade
Retail banks realize they need to embrace AI and automated machine learning in order to stay competitive. Banks are under pressure from new technology-enabled competitors, increasing regulatory requirements, the pressure to simplify the client experience, and the need to reduce costs.
Here are a few AI and machine learning retail bank use cases with high ROI that can create competitive separation.
Predictive product recommendation: Machine learning models can quickly identify which products a customer will need at different stages of their journey. As a result, banks can develop more efficient marketing campaigns to promote the most relevant products to their customers. LendingTree democratized data science to match borrowers with the right offers. Not only did this align borrowers with the highest value offers increasing client satisfaction but acceptance rates increased.
Pricing optimization and lifetime value: Many banks already employ machine learning to predict potential losses on loans based on factors like a customers’ credit score. Banks can now use machine learning models to improve models and make more informed decisions about underwriting, risk acceptance, and pricing. Wellen used AI to make better small business credit decisions and realized a reduction in defaults, improved pricing, and faster underwriting and credit decisions.
Fraud detection/prevention: Banks must both detect and prevent fraud but not at the risk of impacting legitimate customer business. Machine learning models can save banks millions in losses by more accurately identifying fraudulent loan applications, deposit fraud, and unauthorized account transactions. These models can be quickly deployed and easily adjusted and retrained to address new threats. One large North American bank used AI to strengthen fraud detection and was able to reduce both fraud losses and the false alarms that impact good clients.