Data Observability For Financial Services
Achieve precise reporting, gain an accurate customer view, enhance customer models, maximize revenue, and more.
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Why Your Financial Data Problems Go Unseen
Achieve Data Excellence with Acceldata
Precise
Financial Reporting
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.
Prove
Financial Controls
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.
Accurate
Customer 360
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.
Curious how Data Observability can help you?
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Improve
Customer Models
Deliver 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.
Maximize
Revenue Capture
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.
Stop
Revenue Leakage
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.
Ready to start your
data observability journey?
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