3 real-life Business Process Automation examples for Data

3 real-life Business Process Automation examples for Data

Automation is important in data because the function is not just moving information; it is responsible for trust in the information the business uses to operate and decide.

The strongest use cases automate ingestion, transformation, testing, cataloguing, lineage, and quality monitoring so teams spend less time on pipeline maintenance and more time on interpretation, while governance remains explicit and auditable.

Industry

Sector

Plaid's Identity Verification

Plaid leverages ML-powered identity verification to automate the bank account authentication process, replacing slow, manual methods with an AI-driven system that analyzes account patterns and metadata to validate ownership instantly.

Use Case
Bank Account Verification
Tools
Internal Tools
Input
Banking credentials, transaction history, and account metadata
Process
AI verifies bank account ownership and validates user identity by analyzing authentication signals and account patterns in real-time
Output
Instant account verification
Outcome
Reduces verification time from days to seconds with a 99.9% accuracy rate
Payments and Fintech
Data

Wise's Currency Exchange Liquidity Management

Wise utilises predictive machine learning to optimise currency exchange timing and liquidity management, allowing for near real-time conversion at the most favourable rates while bypassing the high fees associated with traditional banking infrastructure.

Use Case
Currency Exchange Rate Optimization
Tools
Internal Tools
Input
Real-time market data, currency fluctuations, and global transaction volumes
Process
AI analyzes market volatility and liquidity patterns to predict optimal conversion windows and automatically manages cross-border liquidity to minimize transaction costs
Output
Optimized conversion timing and automated liquidity balancing
Outcome
$2 billion saved by customers annually in fees compared to traditional banks
Payments and Fintech
Data

Affirm's Automated Creditworthiness Checks

Affirm utilizes proprietary machine learning underwriting models to evaluate creditworthiness in real-time at the point of sale, analyzing thousands of data points to offer instant loan eligibility and terms that outperform traditional credit scoring systems.

Use Case
Real-time Underwriting
Tools
Internal Tools
Input
Consumer data, purchase context, and credit bureau data
Process
AI evaluates creditworthiness by processing thousands of variables simultaneously to determine loan eligibility and specific terms at the moment of purchase
Output
Instant lending decisions
Outcome
20% higher approval rate than traditional methods while maintaining a low 3% charge-off rate
Payments and Fintech
Data
See all 60 Business Process Automation examples