7 real-life Business Process Automation examples for Data

7 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

Moorfields Eye Hospital's RETFound AI foundation model for eye disease

Moorfields Eye Hospital and UCL develop RETFound, the first AI foundation model in ophthalmology, trained on 1.6 million retinal scans from the NHS, enabling rapid automated diagnosis of sight-threatening eye diseases and prediction of systemic conditions including stroke and Parkinson's disease.

Use Case
AI-Powered Retinal Scan Diagnosis and Disease Prediction
Tools
RETFound
Input
1.6 million de-identified retinal optical coherence tomography scans from NHS patient records, along with clinical diagnoses and referral decisions.
Process
A self-supervised AI foundation model pre-trained on the NHS scan dataset is fine-tuned for specific diagnostic tasks, identifying disease markers across more than 50 eye conditions and systemic health indicators.
Output
Automated disease classification reports with referral urgency recommendations and systemic disease risk flags provided to clinical teams alongside patient scan images.
Outcome
RETFound matches world-leading expert accuracy for over 50 eye diseases, has been made freely available open-source globally and enables earlier detection of conditions affecting over 625,000 UK patients.
Hospitals and Clinics
Data

Lloyds Banking Group's AI-Driven Data Quality Automation

Lloyds Banking Group uses Ataccama ONE to automate data quality monitoring, profiling, and anomaly detection across its systems of record. The platform federates data accountability to Business Platform teams and replaces manual data hygiene processes, enabling the bank to move from assumption to assurance over its critical data.

Use Case
Automated Data Quality Monitoring and Governance
Tools
Ataccama ONE
Input
System-of-record data ingested from across the Group's Business Platform teams, including customer, transaction and operational datasets.
Process
Ataccama ONE automatically profiles datasets, detects duplicates, missing values, and inconsistencies and enforces standardised data quality rules across the organisation using AI-based anomaly detection.
Output
Real-time data quality scores, anomaly alerts, audit-ready quality reports, and remediation recommendations surfaced to data stewards and Business Platform teams.
Outcome
The bank transitions from reactive to proactive data quality management, improving trust in data used for regulatory reporting, AI initiatives, and customer-facing services, while reducing manual data stewardship effort.
Retail and SME Banking
Data

AstraZeneca's Scalable ML Data Pipeline Platform

AstraZeneca adopted Databricks and AWS SageMaker to automate the ingestion, processing, and deployment of scientific data pipelines across its drug development operations. The platform enables data science teams to build scalable pipelines, deploy ML models reliably, and analyse millions of data points from scientific literature and proprietary research at scale.

Use Case
Automated ML Pipeline Orchestration and Deployment
Tools
Databricks & AWS SageMaker
Input
Scientific literature, clinical datasets, proprietary research databases, and real-world evidence ingested from hundreds of internal and external sources.
Process
Databricks orchestrates NLP-powered data pipelines that process and analyse large scientific datasets, while AWS SageMaker automates ML model training, registry, monitoring, and deployment across teams.
Output
Scalable, production-ready ML models and enriched data assets delivered to research and commercial teams with automated monitoring ensuring ongoing model performance.
Outcome
Data science productivity improves materially, reducing the time required to build and deploy models, and enabling researchers to analyse complex multi-source datasets faster to accelerate drug discovery timelines.
Pharma and Biotech
Data

Reuters' News Tracer Breaking News Detection

Reuters deploy News Tracer, a machine learning system that monitors over 12 million tweets daily to automatically detect, verify, and distribute breaking news events, giving journalists an 8- to 60-minute head start over rival outlets.

Use Case
Automated Social Media News Detection
Tools
Internal Tools
Input
A continuous stream of over 12 million tweets per day, filtered to isolate event-like clusters of social media conversation.
Process
Machine learning models detect emerging news event clusters, assess veracity using 40 verification factors, classify topic and geography, and generate automated headlines for internal distribution.
Output
Verified breaking news alerts with headlines, topic classification, location tagging, and veracity scores, distributed to Reuters journalists globally in real time.
Outcome
Reuters secure an 8- to 60-minute head start over competing outlets on major breaking stories, directly benefiting the agency's financial data clients who place a premium on news speed.
Consumer Internet Platforms
Data
See all 121 Business Process Automation examples