Management Oversight

Developer Hub

Access real-time data feeds and integrate with our risk & compliance APIs.

API Authentication

All API requests must be authenticated using an API key. Include your key in the `Authorization` header of your request as a Bearer token.

Authorization: Bearer YOUR_API_KEY

SDKs & Client Libraries

Accelerate your development with our official client libraries. We provide SDKs for Python, Java, and TypeScript to simplify integration.

Webhooks

Receive real-time notifications for critical events such as risk limit breaches or compliance status changes by configuring a webhook endpoint.

Available API Feeds

Explore our real-time data feeds to power your own applications and analytics.

Market Risk Data Feed

Real-time VaR, Expected Shortfall (ES), and sensitivity data across all trading desks.

GET/api/v1/risk/market

Example Usage:

fetch('/api/v1/risk/market')
  .then(res => res.json())
  .then(data => console.log(data));

Credit Risk Exposure Feed

Access obligor-level PD, LGD, EAD, and RWA calculations under both SA and IRB approaches.

GET/api/v1/risk/credit

Example Usage:

fetch('/api/v1/risk/credit?portfolio=corporate')
  .then(res => res.json())
  .then(data => console.log(data));

Operational Loss Event Feed

Stream internal and external operational loss events for SMA capital calculation.

POST/api/v1/risk/oploss

Example Usage:

fetch('/api/v1/risk/oploss', {
  method: 'POST',
  body: JSON.stringify({ event_type: 'cyber' })
})
  .then(res => res.json())
  .then(data => console.log(data));

Regulatory Reporting API

Generate and retrieve regulatory reports like FR Y-14Q, COREP, and AnaCredit on demand.

POST/api/v1/reports/generate

Example Usage:

fetch('/api/v1/reports/generate', {
  method: 'POST',
  body: JSON.stringify({ report_id: 'FR_Y-14Q' })
})
  .then(res => res.json())
  .then(data => console.log(data));

Data Platform Integrations

Guides for ingesting BankView 360 data into your existing data ecosystem.

Snowflake Integration

Pull real-time risk and compliance data feeds directly into your Snowflake Data Cloud for enterprise-wide analytics.

Example Connector (Python):

import snowflake.connector
import requests

# Fetch data from BankView API
api_token = 'YOUR_API_KEY'
headers = {'Authorization': f'Bearer {api_token}'}
response = requests.get('https://api.bankview360.com/api/v1/risk/market', headers=headers)
risk_data = response.json()

# Connect to Snowflake
conn = snowflake.connector.connect(
    user='your_user',
    password='your_password',
    account='your_account_url'
)

# Ingest data (example)
conn.cursor().execute("USE DATABASE RISK_DW")
conn.cursor().execute("USE SCHEMA MARKET_RISK")
# ... code to ingest risk_data into a table

Cloudera Integration

Ingest data feeds into Cloudera Data Platform (CDP) using NiFi or custom Spark jobs for use in Cloudera Machine Learning.

Example Connector (Python):

from pyspark.sql import SparkSession
import requests

# Fetch data from BankView API
api_token = 'YOUR_API_KEY'
headers = {'Authorization': f'Bearer {api_token}'}
response = requests.get('https://api.bankview360.com/api/v1/risk/credit', headers=headers)
credit_data = response.json()

# Initialize Spark Session
spark = SparkSession.builder \
    .appName("BankViewIngestion") \
    .getOrCreate()

# Create DataFrame and write to HDFS/Ozone
df = spark.createDataFrame(credit_data)
df.write.format("parquet").save("/data/raw/credit_risk")

IBM Db2 Integration

Connect to your on-premise or cloud-based IBM Db2 warehouse and ingest real-time data feeds for legacy system integration and analysis.

Example Connector (Python):

import ibm_db
import requests

# Fetch data from BankView API
api_token = 'YOUR_API_KEY'
headers = {'Authorization': f'Bearer {api_token}'}
response = requests.get('https://api.bankview360.com/api/v1/risk/oploss', headers=headers)
oploss_data = response.json()

# Connect to IBM Db2
dsn = "DATABASE=BLUDB;HOSTNAME=your-hostname;PORT=50000;PROTOCOL=TCPIP;UID=your-uid;PWD=your-pwd;"
conn = ibm_db.connect(dsn, "", "")

# Ingest data (example)
sql = "INSERT INTO OP_LOSS_EVENTS (EVENT_ID, LOSS_AMOUNT, EVENT_DATE) VALUES (?, ?, ?)"
stmt = ibm_db.prepare(conn, sql)

for event in oploss_data:
  ibm_db.execute(stmt, (event['id'], event['amount'], event['date']))