Power risk, compliance, fraud, and AI initiatives with certified, trusted, and explainable data pipelines.
AI Ready Data Panel: Sara Sprague (Wells Fargo), Nishith Trivedi (Pfizer), Gabriel Charvat (D&B) at Autonomous 25.
Talk to a Data ExpertFragmented Data Stack | Tight SLAs for Large Volume of Data | Data Quality Mismatch Errors from Legacy Systems
Stay ahead of audits and regulations like AML, BCBS 239, and GDPR by automatically monitoring data quality, lineage, freshness, and completeness. Let your compliance teams sleep better at night.
Get proactive alerts on schema changes, volume spikes, or unexpected data shifts using built-in ML and statistical detection—so your finance team isn’t chasing bad data after it causes damage.
Define clear data expectations across teams—and get notified when something breaks. Acceldata helps you eliminate finger-pointing and build operational trust across your data ecosystem.
From digital banking to KYC updates, Acceldata ensures the data fueling your products is complete, timely, and trustworthy—so you can move faster with confidence.
Train credit risk and fraud models with data that’s been vetted and traced. When regulators ask how the model made a decision, you’ll have the answer.
Give your actuarial and ops teams a break. Acceldata helps you deliver clean, timely data to underwriting and claims systems—so you can reduce leakage and improve efficiency.
Shift-left your data quality checks, continuously verify and alert on the state of data as it passes through your complex data pipelines.
Quickly identify data anomalies and discrepancies to give you time to fix problems before financial and business reports are finalized and published by the reporting deadline.
Minimize human errors and time lags with automatic data lineage mapping and tracking across your critical data assets - from system of origin, to system of record, through to consumption point.
Identify previously unknown gaps in coverage and easily add new enterprise-specific policies as the scope of your Basel Committee’s BCBS 239 and other regulatory programs expand.
Ensure all aspects of Customer 360 such as demographic data, behavioral data, transactional data, are checked for quality, consistency and accuracy.
Set up and monitor freshness thresholds for various data elements that can affect your customer uplift machine learning models and other use cases.
Explore our in-depth whitepaper to building a trusted data foundation for AI success.
Download WhitepaperDeliver fresh good-quality data to your customer uplift models to improve model accuracy.
Add checks and balances in your data pipelines to deliver the right training dataset and reduce model rework and retraining costs.
Detect anomalies and reconcile data between systems to identify mismatches between transactions and invoiced charges, incorrect late payment penalties calculations due to errors in customer account status and other updates.
Identify poorly reconciled data and data transformation errors that could lead to overpayment to vendors, erroneous benefit disbursement and other such forms of payments that should not be made but were allowed to process due to insufficient visibility to these errors.
Improve data quality, reduce risks, and strengthen compliance.
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