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Unstructured data represents the single greatest challenge for large enterprises. Up to 80 percent of all data exists in various unstructured formats, hidden in documents, emails, spreadsheets and images. Today, analysts spend hundreds of hours reading a multitude of documents in order to extract relevant and actionable information. This is, rigorous, time-consuming and inefficient as critical decision-making depends on this analysis. While traditional data science has evolved and to a great extent, matured, it is not able to effectively analyze unstructured data.

Text Analytics, as developed by Parabole, allows for the extraction of critical information from these sources including documents on Model Risk Management, Risk Policy, Product, Regulatory Guidance, etc. Tactically, it provides structure to unstructured data in order to classify, cluster and contextually search this data.

“Our Text Analytics platform uses Parabole Knowledge Graph which is ingested with credit risk, market risk, liquidity risk, operational risk, finance, treasury and regulatory compliance knowledge and is trained to perform the automated analysis of domain documents” says Rajib Saha, CEO and co-founder of Parabole.

Parabole Text Analytics uses its proprietary Natural Language Processing models trained on risk, finance and compliance domain and a combination of proprietary Machine Learning algorithms to extract knowledge from unstructured content, which is then applied to automate the analysis of this information. Their platform offers several capabilities, including: Term Discovery, Topic Discovery, Content Similarity, and Named Entity Recognition. Term Discovery automates the identification of the key “terms” in a document. This would greatly reduce the effort spent by the data groups to manually read risk-finance documents to identify “Critical Data Elements”, their contextual usage and business side lineage of the data. Today, teams spend significant hours interviewing their respective business groups in order to understand business terms and their contexts. Topic Discovery is another tool that enables the reader to understand the theme of a document in a fraction of the time taken to manually read it. Using Content Similarity, a reader can find similar content across several documents and discover linkages.

The solution also facilitates audit and regulatory review by linking evidence and creating an auditable environment. Parabole’s real-time dashboards provide a 360-degree view of the analysis and role-specific performance.

To cite a case study, Parabole allows one of the global credit card issuers to meet the challenge of analyzing the impact of the new FASB-CECL regulation. Every lender must make changes to their current models, accounting processes and amend the way they calculate and provide capital for credit losses.

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By automating the analysis of a 300-page regulation and mapping it to their current models, accounting policies and process documents, analysis that would typically take 80-90 percent of the overall assigned time, took only 20 percent of this time. This gave the bank more time to validate the analysis and added nearly 50 percent more time for implementation, testing and audit at reduced cost. This automation being regulation agnostic can be used across regulatory regimes and geographies adding much needed muscle to banks compliance programs.

Parabole is building more domain-specific use-cases for banking clients which involves dealing with voluminous documents in risk and finance including but not limited to, data governance, contract management, covenant management, content rationalization and financial research news analysis. “We realized very early company, that we are only solving a part of the puzzle. It’s important to be part of the ecosystem to solve the larger issues of enterprises, and hence, we are working closely with leading companies, to build a cohesive ecosystem,” adds Saha. With its unique platform that is automating domain-rich analytics, Parabole remains a revolution extending big data-centric models into the world of big knowledge.