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Banking and Capital Markets

Understand Clients. Manage Risk.

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Identify, Assess and Monitor Risk

Kingland helps clients identify new opportunities across complex global hierarchies and identify emerging risks about customers. Operations and technology teams can efficiently manage data connections, hierarchies and data quality, extracting and analyzing data from any source. Fine-tuned for the financial industry, the platform removes time-consuming burdens of data management and errors from manual ad-hoc processes. 

Backed by nearly 30 years of solving complex risk and data management challenges for the financial industry, Kingland brings unique and deep experiences to enterprise-sized, data challenges.  

Banking

 

Reliably and consistently solve complex enterprise data challenges

Data Management 

Leverage secure and diverse amounts of data to create new opportunities and enhance the customer experience. 

Risk Management

Collect and analyze real-time insights and early warnings by connecting external, unstructured data with risk analysis regarding corporate entities, individuals or portfolios. 

Client Data

Integrate client data sources for managing relationships, enabling individuals to quickly retrieve structured and unstructured data and multiple types of data (e.g., legal entity, securities, hierarchies,natural persons, and more). 

 

 

 

Security
Data
World

"We have 27,000 securities on the base with 5 million data points and we have a confidence rate of over 99%. That's really important because [it's critical] the dealers know information they're using to sell product to their client is accurate."

Mark O’Marketing - VP Sales & Marketing, Xappp

Managing Director
Financial Services

Ready to accelerate your data project?

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More Information 

Text Analytics Improvements

Kingland Increases Accuracy of Text Analytics for Unstructured Data

A-Team Insight discusses the improvements Kingland made to NER training, increasing the accuracy in identifying organizations and people that are intuitively highlighted by text analytics within documents. In this case, targeted retraining of the NER models has increased the accuracy of stock models from 60% to nearly 90% in aggregate over numerous source document sets. 

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DTCC uses Text Analytics to Reduce Data Mining from Files in SEC's EDGAR System

Text Analytics Delivers Results for DTCC

DTCC, a U.S. financial services firm, used artificial intelligence to remove the manual data entry in its central repository of mutual fund data and operations rules which house approximately 27,000 securities and more than 5 million data points. The result: a significant reduction in the time it takes to process and update the mining of data from prospectuses and other documents filed in the SEC's EDGAR system.  

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Text Analytics Uncovers Insights

Using Text Analytics to Uncover Insights within Three Common Use Cases

How can we automate some of our processes to save time and valuable resources and improve how we use unstructured text? Every day you have 8 to 10 hours (on average) to complete work. Google alerts, scanning news headlines and skimming articles for any mention of an event that could trigger a new opportunity or cause risk to the organization is a daily task that can be simplified with text analytics.  

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