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%

Financial

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

There’s no guaranteed way to verify address information or geocode latitude and longitude with complete accuracy.

Disparate siloed datasets

Achieving a reliable, unified view of data is challenging when insurers rely on multiple sources and vendors.

Risky financial data

Inaccurate data in risk assessments exposes financial institutions to costly, long-term losses.

Obsolete IT infrastructure

Legacy mainframe systems continue to underpin much financial data, making data conversion both difficult and costly.

Slow data digitization

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

Inconsistent data standards

The lack of standardized data structures, models, and definitions creates duplicate records.

Solution

DataMatch Enterprise – Manage financial risk with confidence

Data Ladder’s DataMatch Enterprise is a powerful data quality and matching engine that enables banks and insurance companies to integrate and process over 2 billion records, detect transaction anomalies and duplicate entries, and perform precise matching to uncover fraudulent behavior.

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Customer Stories

See what financial institutions are saying...

The step by step and wizard-like tool that walks you through the process of setting up a project. It’s very intuitive and allowed us to build all kinds of projects and bring in all kinds of data sources. One of the reasons we chose Data Ladder was because there is a DB2 import feature that allows us to go right into our DB2 database. The interface allowed us to get good results and it’s very simple to use.

Scott FordIT Solution Architect, Bell Bank
Scott Ford

As part of the insurance industry, we have to provide internal reports. We could not do these reports before. Now, DataMatch™ has become a main staple in my suite of tools that I work with!

Andy GreenStatistics Manager, Zurich NA
Andy Green

It scales our time very well, I can’t quantify in dollar terms since it’s new, but I would say that it represents thousands of dollars since it’s time that is not being spent by our employees

Monitoring and Reporting Director, A Global Private Market Firm

Business Benefits

What’s in it for you?

Detect financial fraud

Uncover identity theft and suspicious transactions with high accuracy and minimal false positives through precise identifier matching and duplicate record detection.

Ensure regulatory compliance

Avoid costly litigation and penalties by applying standard rules to inconsistent records and custom patterns to proprietary data.

PRIVATE

Minimize transaction risks

Anticipate risks such as defaults and other warning signals by eliminating data silos.

Speed up customer onboarding

Streamline the customer journey by removing friction and unifying data across multiple touchpoints.

Reconcile conflicting entities

Resolve duplicate customer records caused by name variations, data entry errors, and inconsistent standards using fuzzy matching and data standardization.

Reduce returned mails

Improve mailing accuracy and cut packaging costs by verifying customer addresses and geocoding them for latitude, longitude, and ZIP+4 data.

Why Data ladder

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions

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