4 research outputs found
Claims processing automation - Modernization of an insurance company internal process
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDeep learning and text mining are involved in the research. This work includes the project I developed
together with my colleagues at SAS Institute during my internship experience. In this project we had
to support an Insurance company for the automation of their existing claim processing system. In fact,
as of today, the procedure of reading the incoming claim requests, selecting the useful information
and extracting it to a data management software, is done manually for hundreds of claims every day.
The job required by the insurance company is to substitute the existing procedure with an automated
one, by implementing an OCR system to read the raw data contained in the documents sent by the
customers and transform it into clean and useful information to be inserted into the data management
software.
This research will show the investigation on how to deal with this problem and the objective is to
automate the classification of the documents for the company, to provide them a system to prioritize
the most urgent documents and to execute some technical and administrative checks on the extracted
information. The automation is shown to be feasible; the completeness and accuracy of the
information extracted are solid, proving that this specific task in the insurance company sector can be
realized and help to reduce costs while improving time performance
A Survey of Adversarial CAPTCHAs on its History, Classification and Generation
Completely Automated Public Turing test to tell Computers and Humans Apart,
short for CAPTCHA, is an essential and relatively easy way to defend against
malicious attacks implemented by bots. The security and usability trade-off
limits the use of massive geometric transformations to interfere deep model
recognition and deep models even outperformed humans in complex CAPTCHAs. The
discovery of adversarial examples provides an ideal solution to the security
and usability trade-off by integrating adversarial examples and CAPTCHAs to
generate adversarial CAPTCHAs that can fool the deep models. In this paper, we
extend the definition of adversarial CAPTCHAs and propose a classification
method for adversarial CAPTCHAs. Then we systematically review some commonly
used methods to generate adversarial examples and methods that are successfully
used to generate adversarial CAPTCHAs. Also, we analyze some defense methods
that can be used to defend adversarial CAPTCHAs, indicating potential threats
to adversarial CAPTCHAs. Finally, we discuss some possible future research
directions for adversarial CAPTCHAs at the end of this paper.Comment: Submitted to ACM Computing Surveys (Under Review
Generation and Use of Handwritten CAPTCHAs
Automated recognition of unconstrained handwriting continues to be a challenging research task. In contrast to the traditional role of handwriting recognition in applications such as postal automation and bank check reading, in this paper, we explore the use of handwriting recognition in designing CAPTCHAs for cyber security. CAPTCHAs (Completely Automatic Public Turing tests to tell Computers and Humans Apart) are automatic reverse Turing tests designed so that virtually all humans can pass the test, but state-of-the-art computer programs will fail. Machine-printed, text-based CAPTCHAs are now commonly used to defend against bot attacks. Our focus is on exploring the generation and use of handwritten CAPTCHAs. We have used a large repository of handwritten word images that current handwriting recognizers cannot read (even when provided with a lexicon) for this purpose and also used synthetic handwritten samples. We take advantage of both our knowledge of the common source of errors in automated handwriting recognition systems as well as the salient aspects of human reading. The simultaneous interplay of several Gestalt laws of perception and the geon theory of pattern recognition (that implies object recognition occurs by components) allows us to explore the parameters that truly separate human and machine abilities