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Large-scale data collection is prevalent in the insurance industry. Insurers have been collecting vast amounts of policyholder’s data since the advent of computers. As a result, the insurance industry has substantial historical records, large data sets, and quantifiable methods of measuring results. It makes the industry prime for machine learning (ML). Disrupting the industry through ML is Appsilon, a data science company, which employs a large number of data science consultants who apply ML to immense insurance data to help insurers in fraud detection and risk reduction, sentiment analysis, predictive analytics, underwriting, and other purposes. The company performs data acquisition and uses ML for analysis, however, for enterprise customers to easily understand and interact with the data, Appsilon, provides beautiful and responsive Shiny dashboards. “We constantly improve how data is acquired, processed and used to discover tomorrow’s applications of data and apply them today,” says Filip Stachura, CEO.
Appsilon understands that the insurance industry is prone to frauds, and therefore, the company uses ML to complement human cognitive skills to recognize deceptive patterns. Other than ML, the company uses predictive analysis to identify minute behavioral patterns, and predict the behavior of a user. Further, combining NLP with sentiment analysis helps in assessing the honesty of a debtor while in a chat or a call. The state of the company’s public perception can also be assessed with aggregated Twitter sentiment analysis or a historical news sentiment analysis.
We constantly improve how data is acquired, processed and used to discover tomorrow's applications of data and apply them today
Leveraging NLP further, Appsilon works on satisfying customers by improving the level of automation in document processing and resolving customer issues. Making the underwriting process easier, the company uses reproducible models to alleviate regulatory issues, as it becomes easier to audit the decision process involved. Taking advantage of the recent advances in deep learning and increased image recognition accuracy that surpasses humans, the company automatically reviews claims submitted and flag cases that need manual reviewing almost instantaneously. As a result, customers would receive their claims in minutes, increasing satisfaction dramatically.
For enterprise customers to understand and interact with the data, Appsilon creates, develops, and maintain R Shiny application, a robust data science tool. Appsilon provides scalability and security with unique R packages that native Shiny apps do not offer. Within five years of experience in the market, the company has identified that emerging tech, coupled with good UX, drastically improves the performance of each customer and their accuracy. Besides, simple and clean UI accelerates the adoption of the tool. Appsilon has full-stack engineers and provides dashboard development and data science consulting with advanced statistical models. The team of experts can step-in on every phase of a Shiny project from business analysis and data science consulting to code refactoring. “Appsilon Data Science proved to be an excellent business partner. The high technical skillset, combined with a solid business understanding made the cooperation flawless. Appsilon were flexible with tight schedules. It took us one month to get from sketch to a working application.” extols John Dannberg, Principal, The Boston Consulting Group.
Constantly innovating, the company officially released shiny.router with a new feature of adding routing to the Shiny application. Further, the company is working on a project for building AI model for object recognition in photos. The AI model will benefit not only insurance but also retail, defense, and manufacturing. "Our clients are sitting on untapped potential, and we must bring about unique technological value. This amalgam of individuals gives us a unique blend of passion, the pursuit of knowledge, and more importantly, a balance of computer science, applied mathematics and business know how," concludes Stachura.