Data quality for finance and insurance

Break disparate, siloed datasets to achieve a single, consolidated view of your banking and insurance customers and vendors for AML, KYC compliance requirements, and various other use-cases.

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How bad data affects finance and insurance?



24 percent of insurers say that they are ‘not very confident’ about the data that they use to assess and price risk.

Unreliable address data

No foolproof method exists to verify address information and geocode latitude and longitude values.

Disparate siloed datasets

Impossible to find the correct view of data as insurers tend to pull data from multiple sources and vendors.

Risky financial data

Financial institutions incur significant long-term losses by basing risk assessments on false information.

Obsolete IT infrastructure

Considerable financial data is still based on outdated legacy mainframe systems that create data conversion challenges.

Slow data digitization

Finance and insurance businesses experience slow and gradual data digitization as compared to other industries.

Inconsistent data standards

Lack of common and standardized data structures, models, and definitions create duplicate records.


DataMatch Enterprise – Manage financial risk with confidence

Data Ladder’s DataMatch Enterprise is an industrial-strength data quality and matching engine designed to help banks and insurance companies integrate and process more than 2 billion records to identify transaction anomalies and duplicate records and carry out precise matching to identify fraudulent behavior.

Customer Stories

See what financial institutions are saying...

Business Benefits

What’s in it for you?

Detect financial fraud

Detect identity theft and suspicious transaction activity with minimal false positives through accurate matching of unique identifiers and detection of duplicate records.

Ensure regulatory compliance

Avoid costly litigation and penalties by implementing standard rules for inconsistent records as well as custom patterns for proprietary data.


Minimize transaction risks

Anticipate transaction risks such as likelihood of default and other warning signals by removing siloed datasets.

Speed up customer onboarding

Eliminate frictions to accelerate the customer journey process with a single, consolidated view of data across multiple touchpoints.

Reconcile conflicting entities

Resolve similar customers arising from name variations, data entry errors, and inconsistent data standards with fuzzy matching and standardization features.

Reduce returned mails

Verify customer addresses and geocode to yield latitude, longitude, and ZIP+4 values to improve mailing accuracy and reduce packaging costs.

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions

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