Unstructured document review & analysis

Gain a deeper understanding of both individuals and businesses.

Financial service firms face great risk of losses and financial crimes when inadequately analyzing data in the complex documents they handle. This makes it more important than ever to leverage any information they have to help mitigate risk.

Natural Language Processing enhances analysis of documents, media, and records

Natural Language Processing (NLP) helps firms unearth valuable data and facts from unstructured documents and optimize the tedious review of complex documents such as claims, contracts, applications, agreements, and other customer documents. Valuable facts can be hidden in a variety of sources, requiring employees to comb through hundreds of reports, contracts, and other data manually, which is prone to errors and comes at a high cost for the skilled employees needed for this work. Moreover, manual investigation is inherently inaccurate, inconsistent, and provides a greater risk of missing important details. The ability to analyze these and other data sources efficiently can help ensure that banks, insurance companies, and other financial service companies make sound decisions about current and future customers.

Features & benefits

Automated content analytics can be applied against a range of customer and public data to gain a deeper understanding of both individuals and businesses. Examples of data sources include:

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Bank records, claims, customer files (dossiers), and applications that may contain key information covering a wide range of data.

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Financial reports, statutory orders, constituent documents, and other evidence of a customer’s credibility and creditworthiness, as well as the reliability of their investors, partners, and other third parties.

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Contracts, agreements, assets, and property descriptions can also characterize the financial status of a customer.

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Records in various court online databases and judiciary search systems.

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News, blogs, and social media content may also contain important details about a customer and his public life.

Read about other process scenarios in financial services

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