Bank Prediction Detail

About this dataset

Bank Prediction Detail is a Pro dataset in the Risk & Failures group of the BankRegReports Data API. It is sourced from BRR ML model.

Full model output for one bank: failure, distress and acquisition scores by horizon (the *_prob_* fields are uncalibrated model outputs for ranking, not calibrated probabilities), ranks/percentiles, peer context, top SHAP factors (1yr + 3yr), and score history. Accuracy above about $10 billion in assets is not established. `prediction.prob_10yr` has not been validated out of sample (no 10-year test set has held both outcomes); `prediction.field_notes` says so. `shap_bank_count` is how many banks the latest run explained (drivers are written only for the highest composite scores), and `health_check` is the bank's Health check flag count (tripped of evaluated), or null.

Endpoint

GET /api/v1/banks/{rssd_id}/prediction/detail/

Parameters

Name	Required	Description
rssd_id	required	Federal Reserve RSSD id. e.g. 852218

Example request

curl "https://api.bankregreports.com/api/v1/banks/852218/prediction/detail/" \
  -H "Authorization: Bearer $BANKREG_API_TOKEN"

Responses are JSON in a {"data": ..., "meta": {...}} envelope. Get a token on the setup page.

When to use it

Bank Prediction Detail sits in the Risk & Failures group of the Data API. Reach for it when your work needs this data straight from the API: it comes back as JSON you can load into a notebook, a spreadsheet or a scheduled pipeline, with the dataset key prediction_detail naming it the same way in the REST path, the Python SDK and the OpenAPI document.

Access and authentication

This dataset is in the Pro tier. Send your API token in the Authorization header as a Bearer token, using a GET request to the endpoint above. The Pro plan is the first plan that includes it. Check the plan comparison for request limits, and the data update schedule for when new filings reach the API.

Related datasets

• High-Risk Banks: Banks ranked by the exploratory failure-risk screening score (uncalibrated, not a probability of failure).
• Failure Risk Score: Exploratory 1-year failure-risk screening score (not a calibrated probability) + safety verdict for one bank.
• Recent Failures: Most recent bank failures (FDIC).
• Failures by Year: Bank failure counts and total assets by year.

Keep going

Browse every dataset, follow the setup walkthrough to make a first call, use the Excel add-in for formulas, or connect an assistant through the MCP server.

Python SDK

Install with pip install bankregreports and set BANKREG_API_TOKEN to your key.

import bankregreports

brr = bankregreports.BankReg()  # reads BANKREG_API_TOKEN
rssd_id = 852218  # JPMorgan Chase Bank, N.A.
result = brr.dataset("prediction_detail", rssd_id=rssd_id)