About us
AI adoption in banking has outpaced the frameworks commonly used to evaluate it—resulting in limited visibility into technical complexity, regulatory exposure, and performance at scale. Parallax Intel provides a structured, evidence-based view that enables informed early-stage decision-making.
Our work:
Surfaces trade-offs and hidden cost drivers that standard ROI models miss.
Creates a coherent view of the AI estate across business, technology, and risk silos.
It brings the same cross-functional rigor to AI decisions that banks apply to major capital allocation initiatives.
Our goal is to help banks move from reactive to deliberate—building AI strategies that hold up under real-world regulatory, technical, and governance conditions.
Methodology
We help banks make informed, early-stage decisions about value, governance, technical complexity, and regulatory exposure—before committing to an AI initiative.
Our analysis draws from disclosed use cases, regulatory instruments, and technology and operating model benchmarks across major banks and jurisdictions.
Where disclosure is limited, we benchmark against comparable initiatives across banks to form conservative, evidence-linked inferences—clearly labeled as inferred.
Our methodology shows how early design choices compound into downstream cost, complexity, and regulatory sensitivity.
Results
We evaluate AI initiatives across four dimensions.
Business Value identifies which use cases scale and what outcomes peers have disclosed.
Technical Complexity estimates engineering and integration complexity, benchmarked against peer implementations.
Regulatory Sensitivity maps likely exposure across U.S., Canada, EU, and UK frameworks—highlighting where scrutiny tends to concentrate based on use case design.
Operating Model outlines how systems are typically built and operated (e.g., monitoring, human oversight, fallback paths) and highlights where risk surfaces under different deployment scenarios.
Our work supports executive decision-making; it does not replace internal engineering, risk, or compliance functions, nor does it provide compliance advice or remediation prescriptions.
It helps banks answer questions like:
Which use cases should we scale, pause, or redesign—and why?
Which use cases are technically complex relative to the value they deliver?
Which regulatory frameworks are most relevant for a given use case, and where does scrutiny tend to concentrate?
Which use cases are more likely to fall into higher-risk regulatory categories (e.g., “high-risk” classifications), based on observable characteristics?
Where does risk concentrate across operating-model choices (monitoring, human oversight, fallback paths, third-party dependencies)?
How do we ensure that what is said to regulators, boards, and investors is consistent with how systems are actually designed and operated?
Founder
Parallax Intel was founded by Mahendra Wadhwa, who spent 15 years in banking analytics and strategic initiatives at RBC, JPMorgan, RBS, Sun Life and ABN AMRO.
Contact Us
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