Eqwitty AI ReadinessAI readiness assessment for financial services
Financial services organizations rarely lack AI ambition. What separates the ones that scale from the ones that stall is whether model risk management, regulated data handling and audit trails are built in from the first pilot or retrofitted under pressure.
The diagnostic swaps a handful of its questions for financial-services versions so that governance, data privacy and regulatory readiness are asked in the language of supervisors, model risk teams and compliance rather than in generic terms.
Where AI value shows up first in financial services
The first credible returns in this sector usually come from operations that are document-heavy and rule-bound: onboarding and know-your-customer review, claims and underwriting triage, fraud and financial-crime alerts, and customer service resolution.
How the questions change for financial services
These questions are asked in the financial services edition instead of the standard wording. The pillar, section and scoring are pinned, so your score stays comparable with organizations in other industries.
- AI Governance & SecurityHow do you make sure your AI is used fairly and safely, the way regulators expect — for example checking it lends fairly and won't make costly errors?Regulators expect "model risk management" — a habit of checking AI models before and after they go live so they don't cause unfair or expensive mistakes. Answer based on what you actually do today.Answer options are rewritten for this industry, from “No formal program for fairness or model checks” to “Checks are built in, including automatic fairness testing every time a model changes”.
- AI Governance & SecurityHow ready are you for your regulator's scrutiny of AI (your banking or insurance regulator's AI rules, plus the EU AI Act where it applies)?Most firms are simply "aware". Only a few have an organised program and actually talk to their regulator. Be honest about where you sit.Answer options are rewritten for this industry, from “It's not on our radar” to “We're ahead of the pack and talk to regulators about policy”.
- Data FoundationsHow good is your data for AI in your most tightly-regulated work — for example deciding who gets a loan, spotting suspicious transactions, or pricing policies?Regulated work needs better data than everyday analytics. Answer for your weakest regulated area, not your best.Answer options are rewritten for this industry, from “Poor — we know there are big data problems in at least one regulated area” to “Excellent — automatic quality checks with a clear audit trail for regulators”.
- AI Use-Case PortfolioIn the last year, what was the biggest single payoff from one of your AI tools — in money terms? (In financial services this is often fraud losses avoided, faster claims, or better-balanced lending.)Pick the most defensible number. Losses you avoided count; "we improved the model" without a dollar figure does not.Answer options are rewritten for this industry, from “We don't have one live yet” to “$10M or more (e.g. a big cut in fraud losses)”.
Pillars this edition emphasizes
All nine pillars are scored for every organization. These are the ones where the financial services edition changes what is asked.
One scoring model, comparable across industries
The industry edition changes wording, examples and answer options, never the maths. Every organization is scored on the same nine weighted pillars and lands in one of the same five levels, which is what makes peer benchmarks meaningful. The full method is on the methodology page.
Other industry editions
Find out where you stand
A 15-minute diagnostic, then your nine-pillar score, peer benchmarks and a 90-day plan you can act on Monday. One-time payment of CA$19.80 per report.