10 real-life Business Process Automation examples for Data
10 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
Grab's Gemini LLM-Powered Data Classification
Grab deploy an LLM-based orchestration service to tag sensitive and personal data across its data lake and databases automatically, replacing manual classification by data owners and supporting access control and masking.
Use Case
Automated Sensitive Data Classification
Tools
Azure OpenAI Service & Internal Tools
Input
Data platforms submit table and column names from databases, data lake tables and message stream schemas for scanning and classification.
Process
A batching service sends each schema to GPT-3.5 through Azure OpenAI, which assigns column tags from an internal library using few-shot prompts and a fixed output format.
Output
The system publishes column-level sensitivity tags, such as personal identifiers and contact details, which data owners verify on a weekly basis.
Outcome
Over 20,000 data entities were classified within a month, saving an estimated 360 person-days a year and enabling automated access control and data masking.
Consumer Internet Platforms
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
Grupo Casas Bahia's Genie Self-Service Analytics Assistant
Brazilian retailer Grupo Casas Bahia utilise Databricks AI/BI Genie so that employees can query sales, inventory and fraud data in natural language, reducing analysis tasks from several hours to around two minutes.
Use Case
Conversational Self-Service Business Analytics
Tools
Databricks AI/BI Genie & Databricks
Input
Employees ask questions in Portuguese through Microsoft Teams, drawing on sales, inventory, order, fraud-score and competitor performance data held in Databricks.
Process
Genie translates each question into a query using standardised business logic and calls AutoML forecasting models to answer forward-looking questions such as predicted stockouts.
Output
The assistant returns direct answers, stockout forecasts, sales analysis set against inventory availability, and ranked lists of high-risk orders for fraud review.
Outcome
Analyses that previously took five to six hours are completed in around two minutes, and the central data team handles fewer routine query requests.
Non Food Retail
Data
Uber's QueryGPT Natural Language to SQL Generation
Uber utilise QueryGPT to turn plain-English data questions into SQL queries across its data platform, cutting the time taken to author a query from around 10 minutes to around 3 minutes.
Use Case
Natural Language to SQL Query Generation
Tools
OpenAI & Internal Tools
Input
Employees submit natural-language data questions, which are matched against curated workspaces of table schemas and sample SQL queries for each business domain.
Process
An intent agent maps each question to a domain, a table agent selects tables, and a column-pruning agent trims schemas before a GPT-4 model generates the query.
Output
The system returns a ready-to-run SQL query together with a plain-language explanation of how the query was constructed.
Outcome
Query authoring time falls by roughly 70%, with 78% of around 300 daily users reporting time saved compared with writing queries manually.