# Building a defensible ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → model for AI pilots
A vice president of compliance walks into a finance committee meeting with a slide claiming an AI trade-surveillance tool will "save analysts 40% of their time." The committee chair asks: "40% of what, exactly, and where does that money show up on the P&L?" Silence. The pilot gets tabled for two quarters.
This happens constantly. AI pilots in banking die not because the technology fails, but because the business case collapses under basic scrutiny. This lesson shows you how to build one that doesn't.
Most AI pilot proposals in banking mix two very different kinds of benefit:
Finance committees, internal audit, and model risk teams (governed in the US under the Federal Reserve's SR 11-7 guidance on model risk management) are trained to distrust soft claims. If your ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → model blends the two without separating them, the whole case becomes suspect, even the parts that are true.
The fix is structural: build two separate ledgers, hard and soft, and never let them touch until the end.
Trade surveillance systems monitor trading activity for market abuse: insider dealing, spoofing (placing orders with no intent to execute), layering, and wash trading. In the EU this obligation sits under the Market Abuse Regulation (MAR); in the US it falls under FINRA and SEC rules, notably FINRA Rule 3110.
Hard, defensible savings typically fall into three buckets:
1. Headcount and workload reduction
If your legacy rules-based system generates 10,000 alerts a month and analysts spend an average of 20 minutes triaging each false positive, that's a calculable labor cost. An AI system that cuts false positives by a verified percentage (from a controlled pilot, not a vendor brochure) frees analyst hours that can be reallocated or reduced.
2. Penalties and fines avoided
This is harder to claim credibly (you can't prove a fine that didn't happen), but you can anchor it to industry benchmarks. Deutsche Bank, Goldman Sachs, and other major banks have paid nine-figure surveillance-related settlements over the past decade for gaps in detecting manipulative trading. Citing these as *sector risk exposure*, not a guaranteed avoided cost, is defensible; claiming "this will prevent a $100 million fine" is not.
3. Investigation and remediation cost
Every alert that escalates to a full investigation costs money: compliance officer time, legal review, sometimes external counsel. Reducing false-positive escalation rates reduces this directly.
Assume (all figures illustrative estimates for teaching purposes, not sourced from any real vendor or bank):
Rough calculation:
Baseline analyst cost: $1,560,000/year
Time freed by AI (25%): $390,000/year equivalent capacity
Redeployment assumption: 50% redeployed to higher-value review,
50% counted as hard headcount saving
Hard saving claimed: $195,000/yearNote what happened: only half the freed capacity became a "hard" saving. The other half is redeployment, which is a soft benefit (better coverage, not lower cost) unless you actually cut a role. This distinction is what survives committee questioning.
Soft benefits are real but must be labeled as such, quantified separately, and never added directly to the hard savings total. Typical soft claims in trade surveillance AI:
Treat these as a secondary narrative, presented after the hard numbers, framed as "additional expected value not included in the ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → calculation." This protects your core numbers from being dismissed as inflated.
A defensible ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → model for an AI pilot has four components, always kept visible and separate:
1. Cost of the pilot: licensing, integration, data engineering, model validation, ongoing monitoring (this last one is often omitted and shouldn't be)
2. Hard savings: labor, penalty exposure reduction (framed conservatively), investigation cost reduction
3. Soft benefits: listed, described, explicitly not counted in the ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → ratio
4. Risk adjustments: model risk, vendor lock-in, regulatory approval delay
A basic ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → ratio:
ROI = (Hard savings - Total pilot cost) / Total pilot costIf your hard savings are $195,000/year and your pilot costs $150,000/year in software, model validation, and integration, ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → is roughly 30%. That's a modest, credible number, far more persuasive than an unsubstantiated "5x productivity gain."
AI surveillance tools, especially those using machine learning to score trades, fall under model risk management frameworks. In the US, SR 11-7 requires independent validation of models used in decision-making, including ongoing performance monitoring. In the EU, the EU AI Act (entered into force 2024, phased obligations through 2026-2027) classifies certain financial-sector AI uses, and supervisory expectations from the European Central Bank (ECB) and European Banking Authority (EBA) reinforce similar validation duties.
This means: budget for a validation team, documentation, and periodic re-testing. These are recurring costs, not one-time pilot costs, and they must appear in your model or your ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → is overstated.
Vérification des acquis
1. Why does mixing hard savings and soft claims in a single ROI model tend to sink the entire business case, rather than just weakening the soft portion?
2. A VP claims an AI tool will 'save analysts 40% of their time.' What is the core problem the finance committee is reacting to?
3. In the trade surveillance example, why is 'headcount and workload reduction' framed as a hard saving rather than a soft claim?
4. Select ALL correct answers about what makes a benefit claim a 'hard saving' in an AI pilot ROI model.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers about why model risk and internal audit teams in banking are trained to be skeptical of soft AI benefit claims.
Sélectionnez toutes les réponses correctes.
Committees scrutinizing AI spend want three things on one page:
1. A hard-savings number they can trace to a headcount or cost line
2. A clearly labeled "not included" section for soft benefits
3. A sensitivity range, not a single number ("if false-positive reduction is only 15%, not 30%, ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → drops to X%")
Sensitivity analysis is the single most credibility-building element you can add. It signals you're not cherry-picking the best-case scenario.
🎬 [VIDEO: "How Banks Use AI for Fraud and Trade Surveillance" - youtube.com - search for recent explainer content from Bank for International Settlements (BIS) or major consultancy channels covering surveillance AI use cases, useful for a visual walkthrough of alert triage workflows]
For a broader grounding in how AI is applied and governed across banking more generally, the Bank for International Settlements' work on AI in finance is a solid, free, non-vendor resource.