Data quality for finance and insurance
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How bad data affects finance and insurance?
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Considerable financial data is still based on outdated legacy mainframe systems that create data conversion challenges.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
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.
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!
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