Various techniques in Deep Learning are now being used to not only detect damages on automobiles but also to estimate the severity of damage and estimate the repair costs saving time and costs in the insurance survey process, when used remotely, without having the surveyor visit the location of the vehicle. At AlgoAnalytics, we have our own AI model and tool developed for automated and fast damage detection.
Real-Time Vehicle Tracking & Analytics using Traffic Videos
Traditional traffic monitoring systems usually include a setup based on inductive loops, RADARs, or LIDARs. An increase in the number of traffic video cameras, gives significant opportunity for Real-Time Analytics for these videos. Coupled with Automatic Number Plate Recognition (ANPR) system & Anomaly Detection System it can provide actionable insights which benefit a wide variety of agencies such as the traffic control department and insurance companies.
Signature Verification using Deep Learning
With the recent advancements in Deep Learning, new applications of unstructured data analytics are emerging in multiple domains. Image Analytics is one of the most common forms of unstructured data analytics. Exponential growth in text documents, images, videos in every domain is driving development of multiple image analytics applications to derive insights and enrich customer experience. Image analytics solutions play an important role in security and authentication purposes as well. One such exciting new application of Deep Learning is automated signature verification.
The Role of a Data scientist
A Data Scientist today is in much demand. The kind of skill set and abilities required are not very different from what say a computer engineer would do- coding, programming, analyzing etc. However, a major winner is being ruthlessly analytical and data driven.
Learning and researching about the new Machine Learning models “out there in the market” makes the work of a Data Scientist different from the traditional software developer. One needs to understand the problems of the clients, research on which model/s to be employed, research, and design and tweak Machine Learning models.
Machine Learning and Insurance
Risk and Insurance are words that go in sync with each other- one cannot live without the other. The insurance industry has always been dependent on various factors, the most important being statistics to know their customer’s and the market demand. Machine Learning(ML) and AI has brought in a lot change in the way Insurance strategies are now being structured.
Besides predicting risks, ML and AI have now helped the Insurance industry to monitor, analyze not just their own strategies at one given time period, but keep a constant look out for changes that affect their customers, thereby keeping their policies attractive and thus halting retention.
To get that competitive edge, Insurers need to engage with Machine Learning (ML) to improve not just their business strategies but also tailor the insurance purchases to customer’s unique needs to bring in customer satisfaction.
Text Analytics: Taking search to the next level
Can Text Analytics really help reduce manual overload? Let’s take a look back when we started sharing interesting links, bookmarks, etc. During the early 2000’ there were many “tagging sites” that emerged. Tagging helped in collating our searched links online rather than just in a browser and also helped share the saved links with a single tag. It was soon realized that the one word tagged for different people meant different things and thus context and meaning to that tag was required and with that relevant sub-tags became a part of the search criteria.
Diabetic Retinopathy & Machine Learning
Today nearly 415 million diabetic patients are at risk worldwide. One of the risks is the high prevalence of Diabetic Retinopathy (DR). DR is becoming the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Currently, only about 60 percent of people with diabetes are stated to have yearly screenings for Diabetic Retinopathy. Between 90 percent and 95 percent of all patients with diabetes have Type 2 diabetes. Given such a disproportionately large number, this group consequently comprises a large proportion of patients with visual impairment who suffer from Diabetic Retinopathy, even though Type 1 diabetes is associated with more frequent and more severe ocular complications. With increasing industrialization and globalization, there is a concomitant increasing prevalence of diabetes that is leading to a worldwide epidemic.
Machine Learning & Retail Analytics
Analytics in Retail has moved beyond just forecasting and making simple assumptions about customers. Machine learning and Artificial Intelligence (AI) has started to provide retailers with powerful tools. These tools are just beginning to bridge the gap between marketing automation and retail management consultation.
Copyright 2019 AlgoAnalytics Pvt. Ltd. All Rights Reserved.