A small number of multinational top-tier financial institutions are already among the most world's most sophisticated users of enterprise-level artificial intelligence (AI). But while much has been written about the potential for AI to address business problems across the industry, adoption rates and the level of maturity vary widely.

The journey usually begins with data scientists pulling together data sets for experimentation and finding insights and ideas to suggest to innovation teams and business units. From there it often progresses to obvious use cases such as improving money laundering surveillance and financial crime detection.

Eventually, organisations are widening the context of their data through internal consolidation and enrichment with third-party data, and running models and decision engines across multiple functions within the organisation. Depending on the use case, these can deliver streamlined digital channel processing with automated decisions – or rapid presentation of context and insight for human-in-the-loop decisions.

It is the evolution leading to large scale investment, adoption of AI approaches and return on this investment, that this survey sought to illuminate. How many institutions are at each stage of this evolution? What use cases are they focusing on and what challenges are they encountering along the way?

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