The increasing demand for relevant, immediate, and interactive customer engagement on a large scale, within a secure and trustworthy environment, is driving the adoption of AI and Machine Learning (ML) applications. These technologies have significant implications for financial institutions in four key areas: marketing and decision making, operational efficiencies, customer experience, and fraud and credit underwriting.

AI and ML models are now being utilized not only for fraud detection and prevention but also in a broader range of use cases that directly impact the customer experience. These models empower banks to achieve cost and time efficiencies. However, many banks face challenges when it comes to scaling advanced AI and real-time capabilities beyond selected use cases, hindering their ability to generate substantial business benefits throughout the organization. This report identifies the key emerging trends and specific use cases in AI and ML adoption within financial institutions, highlights the challenges they face, and outlines a strategic roadmap for scaling the adoption of AI-driven advanced analytics.

Publication date:

May 2023

Delivery:

The file is directly delivered into your account provided soon after online payment is received. The file contains a pdf A4 file  (28 pages).

The report includes:

This report answers: