Use Cases for Text Analytics in Finance and Insurance

Amenity Analytics offers an innovative approach to text analytics, combining machine learning with NLP and other forms of artificial intelligence. Our technology is guided by industry experts, with the idea of solving actual business problems.

Finance
Operations

Earnings Call Transcript Analysis

Fundamental hedge funds use Amenity’s Insights Platform to increase the efficiency and coverage of their analysts during peak earnings season.

Amenity Viewer is a cloud-based AI/NLP software tool that analyzes earnings call transcripts to uncover real-time, actionable insights. Viewer is trained to think like the analysts who use it. Viewer performs full grammatical parsing and paragraph-level contextual analysis to identify the critical indicators and trends that move markets and drive performance.

Our institutional investor customers use the Viewer to transform the way they consume earnings call information. What took hours now takes minutes, while the analyst’s scale of coverage increased by a multiple of 10.

Risk

Text Analytics & NLP for Banking and Finance

The investment arm of a financial institution used Amenity's API to create a dataset on the biggest players in the autonomous car market to support its strategy and business development efforts.

Amenity extracted the most critical autonomous car industry news on a daily basis, identifying actionable patterns across the industry over time. The industry analysis model featured custom event types and modified versions of core taxonomies to best identify insights that are meaningful to the autonomous car industry. 

Amenity was able to acheive a high degree of accuracy using its proprietary NLP API including tokenization, lemmatization, named entity recognition (NER), dependency parsing and semantic role labeling. The result was a comprehensive analysis that provided a 360 degree view on the Autonomous Car industry financials, the supply chain, retail, OEM, suppliers, and technological trends.

Risk

Underwriting and Claims Text Analytics

The Project

Using Amenity's data analytics tools, a major insurance company created automated extractions of real-time events that impact underwriting and claims decisions. Amenity parsed information from a variety of sources to create an emerging risk model that provides up to date information on areas of exposure in the insurance and legal ecosystem.

The Results

The real-time “watchdog” system scans the environment and populates underwriting and claims platforms with extractions from transcripts, news, SEC filings, and court cases regarding dangerous precedents and specific events that need to be examined. These highlights determine the impact on underwriting, claims and business development decisions across the company.

R&D

Insurance Industry NLP Model

Project

Created insurance-specific vocabulary and rules to give a Leading Insurance Provider a better picture of their competitive environment, industry best-practices and risk tolerance levels. Amenity's proprietary NLP model was customized with insurance-specific taxonomies to identify patterns across the insurance industry and the impact of pivotal industry events through contextual sentiment analysis.

Results

Insurance strategy and risk monitoring groups received a tailored, insurance-focused competitive intelligence platform that provides detailed insights on insurance profitability, trends, and the impact of weather and catastrophic events. These insights explain how and why various lines of business, geographies, and companies perform relative to the market.

Operations

Sales Process Text Analytics

The Project

A Fortune 500 Company uses Amenity's API to provide its sales organization with up-to-date business information about their top clients and prospects. Armed with the key business drivers, the Sales Team is better able to gauge which customers are most likely to buy their services.

The Results

Amenity runs its proprietary NLP text analysis tool over news articles and earnings call transcripts. Customers and prospects are each given a score based on their likelihood of purchase. A positive score indicates that the customer may be open to expanding their relationship. 

The score is based on positive news regarding financial performance, expansion into new markets, the launch of a new product or the closing of an important deal. A negative score indicates that there is little news about the company or the news is mixed or negative.

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