Use casesDevelopersFinance
Drex for finance
Fraud, credit and filing decisions for fintech. You list the options, Drex picks one and says how sure it is. It can't answer outside your list.
Accuracy on finance decisions
Share of 3,361 decisions answered right, across 48 public finance test sets the model never saw in training.
See every category- Drex for Finance
- 85.7%
- pplx-decider v1.2
- 77.4%
- Jev 1.14
- 76.7%
- GPT-6 Luna
- 70.9%
What you can use it for
Documents & filings
Is this invoice a duplicate of one already paid?
- Yes
- No
Credit & lending
Which risk band does this applicant fall in?
- Low
- Medium
- High
- Decline
Fraud & AML
Which chargeback reason fits this dispute?
- Fraud
- Not received
- Duplicate
- Other
Markets & trading
How does this Fed statement read?
- Hawkish
- Neutral
- Dovish
Insurance & regtech
Is this claim covered under the policy as written?
- Covered
- Not covered
- Needs review
Banking & operations
Which team should this banking request go to?
- Cards
- Loans
- Payments
- Fraud team
Benchmark results against Jev 1.14
The Drex Finance Index combines scores on 48 public finance test sets the model never saw in training. Drex for Finance scores higher than Jev 1.14 in all 9 categories.
| Benchmark | Drex for FinanceNace | Jev 1.14TypeSafe | GPT-6 LunaOpenAI | pplx-decider v1.2Perplexity |
|---|---|---|---|---|
| Overall | ||||
| Drex Finance Index | 77.3 | 63.6 | 58.4 | 65.0 |
| Accuracy, % | 85.7 | 76.7 | 70.9 | 77.4 |
| Language & signals | ||||
| Sentiment & tone | 80.7 | 69.1 | 68.6 | 73.9 |
| News & headlines | 89.5 | 66.4 | 60.7 | 68.8 |
| Banking & support intent | 87.1 | 71.6 | 71.9 | 73.7 |
| Documents & filings | ||||
| Documents & extraction | 92.3 | 85.9 | 48.8 | 87.1 |
| Financial QA & filings | 96.7 | 86.4 | 88.1 | 82.6 |
| Risk, macro & markets | ||||
| Credit & risk | 48.3 | 30.0 | 38.3 | 11.7 |
| Monetary policy | 53.8 | 45.0 | 38.8 | 35.0 |
| ESG & climate | 73.0 | 59.1 | 48.1 | 54.4 |
| Markets & reasoning | 41.5 | 28.1 | 30.1 | 43.1 |
0 is a coin flip, 100 is every answer right. Bold is the best score in each row. The finance training didn't cost general skill: JevBench went from 93.9% to 94.4%. All seven models are compared in the announcement.
How to get started
Install nace-sdk for Python or TypeScript and set the model to drex-max-finance. Already on Jev? Point the TypeSafe SDK at the Console and change the model name.
# pip install nace-sdk
from nace_sdk import NaceClient, Noul
with NaceClient(model="drex-max-finance") as client:
result = client.system_one(
state="INV-2304, Northwind Supply, $9,870.00. "
"Paid Oct 12: INV-2304A, Northwind Supply, $9,870.00.",
questions={
"duplicate": Noul(instructions="Is this invoice a duplicate?"),
},
)
print(result.nouls["duplicate"].noul)
Put a decision model where the guesswork is
Free to try today. Tuned on your decisions and served in your environment when you are ready.