> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-2/scenarios-2.md).

# Financial services scenarios

Financial services AI evaluation scenarios — fraud detection, advisor chatbots, KYC, document processing, trading compliance.

Financial-services AI carries asymmetric risk: false negatives cost direct losses (fraud, mis-pricing); false positives drive customer friction and fair-lending exposure. Every scenario below pairs accuracy with fair-treatment evaluation.

## Fraud detection <a href="#fraud" id="fraud"></a>

**Setup:** AI analyzes transaction patterns, account behavior, and device signals to detect potential fraud in real-time.

**Why evaluation is hard:** detection rate is the obvious metric, but **false-positive disparity by demographic** drives fair-lending exposure that's invisible to aggregate accuracy.

**Stakes:** $4.2B annual US card fraud (Federal Reserve / Nilson Report); per-declined-legitimate-transaction friction \~$25 (card-network industry benchmarks); ECOA fair-lending civil penalties on systematic bias.

**Eval criteria:**

* Custom code grader — detection precision/recall on labeled fraud dataset
* Custom code grader — false-positive rate disparity across protected-class proxies (1.25× ratio threshold)
* Faithfulness judge — explanation quality for SAR (Suspicious Activity Report) filing
* Latency scorer — decision time under SLA

**Trace shape:**

```
trace
├── span: feature-extraction (transaction, account, device)
├── span: model-scoring (risk score)
├── span: explanation-generation (for SAR-eligible decisions)
└── span: outcome (approve / decline / flag-for-review)
```

## Advisory / robo-advisor chatbot <a href="#advisor" id="advisor"></a>

**Setup:** AI provides personalized investment guidance, portfolio recommendations, market analysis to retail investors.

**Why evaluation is hard:** suitability violations (recommending unsuitable products to a customer's risk profile) are FINRA enforcement targets — recent sweeps include $1M-$50M fines. Required disclosures must appear consistently.

**Stakes:** SEC / FINRA enforcement bands ($1M-$50M); customer losses + civil litigation; failure to disclose risk-of-loss; bias toward proprietary products.

**Eval criteria:**

* Faithfulness judge (GEPA-tuned ≥50 examples) — performance and product claims grounded in real fund data
* Suitability judge (GEPA-tuned ≥50 examples per risk-profile band) — recommendation matches customer's stated risk tolerance
* Disclaimer-presence scorer — required FINRA disclosures appear in every recommendation
* No-specific-recommendation rule — customer-facing AI never recommends specific securities (deterministic regex)

## KYC / AML document review <a href="#kyc" id="kyc"></a>

**Setup:** AI verifies identity documents (passports, driver's licenses, utility bills) and screens against sanctions / PEP / adverse-media lists.

**Why evaluation is hard:** sanctions-screening recall must be \~100%. Document authenticity detection must catch digitally altered documents.

**Stakes:** FinCEN penalties ($1M-$1B per BSA/AML enforcement action); correspondent banking relationship loss; onboarding fraudulent accounts.

**Eval criteria:**

* Sanctions-coverage rule — every input name screened against the active sanctions list (recall 100%)
* Document-authenticity scorer — image-tamper detection on submitted IDs and bills
* OCR-accuracy scorer — field extraction matches certified-coder ground truth
* Adverse-media judge — relevance scoring for media hits
* PEP-identification scorer — politically-exposed-person matches against the active PEP database

## Loan / mortgage document processing <a href="#loan-docs" id="loan-docs"></a>

**Setup:** AI extracts structured data from loan applications, tax returns, bank statements, pay stubs for underwriting.

**Why evaluation is hard:** Schedule C self-employment income parsing is a known weak point — depreciation add-backs, multiple schedules, net-vs-gross.

**Stakes:** $50K-$500K per bad loan; TILA / ECOA violations; manual rework cost; audit failures.

**Eval criteria:**

* Field-level extraction scorer — names, amounts, dates, account numbers exact-match against ground truth
* Cross-document consistency judge — values match across application + W-2 + bank statement
* Schedule C / self-employment numeric scorer — income computation matches certified-coder ground truth
* PII-redaction rule — no SSN / account-number echo in any logged span

## Trading compliance / SOX-aligned advisory <a href="#trading" id="trading"></a>

**Setup:** AI assists trading workflows — recommendations, audit narration, compliance checks.

**Why evaluation is hard:** SOX trading compliance requires immutable narration of decision rationale. Suitability for institutional vs. retail counterparties differs.

**Stakes:** SOX violations; SEC/FINRA enforcement; fiduciary breach litigation.

**Eval criteria:**

* Audit-narration faithfulness judge — every claim cites the underlying market data or policy
* Suitability judge (per counterparty type) — recommendation matches counterparty's authorized scope
* Position-limit rule — recommendation doesn't breach hard position limits
* Required-disclaimer scorer — SOX/FINRA disclosures present

## See also

* [Industry pattern: fair-lending platform pattern](/4.2-industry-use-cases/pattern-1.md) — these scenarios composed into one runnable evaluation
* [Eval patterns](/more-in-this-section-2/eval-patterns-2.md) | [Compliance](/more-in-this-section-2/compliance-2.md)
* [SDK sample: financial\_fraud.py](https://github.com/layerlens/stratix-python/blob/main/samples/industry/financial_fraud.py)
* [SDK sample: financial\_trading.py](https://github.com/layerlens/stratix-python/blob/main/samples/industry/financial_trading.py)