Hyper-personalized cardholder experiences through enriched data attributes.
Enhanced spend analysis and management.
Improved portfolio management with cardholder and account-level insights.
Financial/ non- financial, authorized/ declined, recurring transactions.
Credit limits, delinquency levels, account opening/closing dates.
Total balances, cash credit limits, minimum amounts due.
Product name, expiry date, supplementary card details.
Includes billing cycles, payment streams, and installment details.
To enable real-time data streaming, the following infrastructure components are typically required:
Streaming Platform:
Kafka: A distributed event-streaming platform for handling high-throughput data.
Azure Event Hubs: A cloud-based service for big data streaming and event ingestion.
Data Ingestion and Processing Tools:
Stream Processors: Tools like Apache Flink or Spark Streaming for processing data in real-time.
ETL Tools: Extract, Transform, and Load systems to prepare and integrate data.
Storage Solutions:
Scalable storage like cloud data lakes (e.g., Amazon S3, Azure Blob Storage).
Real-time databases (e.g., MongoDB, Cassandra) for fast querying and data retrieval.
Authorisation Data
Attributes:
Transaction Details: Date/Time, Amount, Currency.
POS/ATM Information: POS/Ecommerce flag, Entry mode (contactless, manual, magnetic stripe).
Transaction Types: Retail, pre-auth, balance inquiry, etc.
Response Codes: Authorized/Declined with decline reasons.
Merchant Details: Name, category, location, and acquirer name.
Wallet Information: For applicable transactions.
CMS Data Attributes
Cardholder Demographics: Name, DOB, Gender, ID details.
Account Details: Account number, credit limits, delinquency levels
Balances: Total balance, cash credit limit, minimum due.
Card Information: Product type, expiry date, supplementary cards.
Billing and Payment: Installment details, billing cycles, and payment streams.
