Banks are moving into AI faster than the evidence is being read.
Whether a bank is deciding where to start or already weighing initiatives underway, the same things decide whether a use case deserves capital: the value it has actually evidenced, the regulation it draws, the difficulty of building it, and the burden of running it once live.
Parallax Intel exists to close that gap.
What's usually missing is a consistent way to weigh those four things — across use cases, and against what has actually worked at peer banks. Decisions default to vendor pitches and internal conviction instead.
It reads the public record of peer AI deployments — and where disclosure stops, completes the read with structured, clearly labeled inference — scoring any banking AI use case on the same four terms: evidenced value, regulatory exposure, technical complexity, and operating burden.
The result is an evidence-based view of which AI investments deserve capital, and why.
Mahendra Wadhwa
Previously
J.P. Morgan · RBC · Sun Life
Mahendra Wadhwa founded Parallax Intel after eighteen years in capital allocation and corporate development across J.P. Morgan, RBC, Sun Life, and banks in Asia and Europe — evaluating competing investments, structuring transactions, and helping institutions decide where capital should go.
Assessing AI use cases is the same discipline applied to a newer question. An AI portfolio is a set of competing claims on a bank's capital and risk appetite; the framework brings a capital-allocation lens to deciding which of those claims hold up.
He holds an MBA from the Rotman School of Management at the University of Toronto. The practice is based in Toronto.
Deciding where AI capital should go?
A structured, evidence-based read may be useful to how those decisions get made.