An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

BFSI sectors deal with lots of unstructured scanned documents which are archived in document management systems for further use.For example in Insurance sector, when a policy goes for underwriting, underwriters attached several raw notes with the policy, Insureds also attach various kind of scanned documents like identity card, bank statement, letters etc. In later parts of the policy life cycle if claims are made on a policy, releted scanned documents also archeived.Now it becomes a tedious job to identify a particular document from this vast repository. The goal of this case study is to develop a deep learning based solution which can automatically classify scanned documents.

BFSI sectors deal with lots of unstructured scanned documents which are archived in document management systems for further use.For example in Insurance sector, when a policy goes for underwriting, underwriters attached several raw notes with the policy, Insureds also attach various kind of scanned documents like identity card, bank statement, letters etc. In later parts of the policy life cycle if claims are made on a policy, releted scanned documents also archeived.Now it becomes a tedious job to identify a particular document from this vast repository. The goal of this case study is to develop a deep learning based solution which can automatically classify scanned documents.

Deep Learning Research in Auto Insurance

This project compares a custom CNN with pretrained models (MobileNetV2, VGG16) for car damage severity classification. Evaluates accuracy and efficiency for applications in insurance claims, safety checks, and automated assessments using deep learning and transfer learning.

I have implemented deep learning approach on sample insurance dataset. Which will help us to understand the insights of deep learning

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This repository contains the csv files, jupyter files to pre process a huge 7 gb data file of Insurance claims from FEMA (Federal Emergency Management Agency), jupyter notebook of model building and its evaluation and finally Visualization using Power BI

An IoT-based healthcare insurance management system using deep learning (TabNet) for accurate, transparent risk and cost prediction.

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Testing for asymmetric information in insurance with deep learning

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Deep learning project modeling how earthquake events influence insurance stock price dynamics in Japan using time-series methods.

Involved in understanding P&C (Insurance) domain for implementing Machine Learning/Deep Learning concepts with patient information and demographic details to predict the Risk of approving insurance and cost associated with it.

We have create a Insurance model in which insurance company handles claims in day to day life, in order to distinguish whether claim is valid or not we have developed a model using Natural Language processing and deep learning techniques

Optical Character Recognition (OCR) unlocks numerous possibilities in data management. By leveraging multi-modal modeling, paired with robust training and optimization, you'll deploy deep learning techniques to classify insurance codes from scanned insurance documents and develop an advanced OCR model.

The aim is to create machine learning models to predict healthcare insurance costs based on personal demographic and health-related variables. The project looks at many machine learning algorithms, from simpler models such as linear regression to complex deep learning models, to attain the highest accuracy in predictions.

A deep learning-based project for detecting potholes in road surfaces using computer vision. Utilizes YOLO models for accurate, real-time pothole detection from images and videos, with a custom dataset and data augmentation techniques. Ideal for applications in road maintenance, autonomous vehicles, and insurance assessments.

Role: North America Identity & Access Management Practice Lead Experience: 12 plus years of dedicated identity administration and governance experience. Location: Dallas, Texas, and open for other locations. Choose Inspira for a challenging and rewarding cybersecurity career where the variety of opportunities and challenges allows you to make a difference every day. A place where you can develop your potential and grow professionally, working alongside talented colleagues. The only place where you can learn from our unrivaled experience while helping our global clients achieve their cybersecurity objectives. If this is your idea of a typical working day, then Inspira is where you should be. Inspira's Global Cybersecurity practice helps organizations work through complex business and technology challenges and provide a straightforward approach to cybersecurity transformation. Our professionals bring deep technology skills and industry knowledge and work closely with our clients to design and implement solutions that are positioned to achieve their business objectives. Website: https://www.inspiraenterprise.com Responsibilities The North America Identity & Access Management (IAM) Practice Lead (Senior Manager) will be responsible for performing the necessary leadership, facilitation, planning, analysis, and design tasks related to building an industry-leading IAM practice. The IAM Practice Lead will partner and work closely with key strategic vendors, service providers, and the Companyʼs solution architects to plan, design & execute the Companyʼs go-to-market IAM strategy to support cross-functional business needs and align with longer- term best practice standards and viable technology roadmaps. Responsibilities of IAM Practice Lead: Manages, develops, and matures processes and acts as the Companyʼs regional (North Americas) business partner between the Companyʼs industry sector leaders and the global IAM practice leads, to develop industry-specific market offerings in alignment with the Companyʼs global IAM strategy • Manages and develops a team of IAM professionals responsible for business development, client delivery and operations, and innovation • Manages the review and architecture of new IAM security technologies to ensure alignment with the Companyʼs go-to- market strategy • • Lead the development, implementation, and manage company-wide IAM strategy This role will report to the Global Cyber Security Head and have a responsibility to influence cyber security strategy actions cross-functionally and must possess a high degree of domain competency in the field of information security and risk management; specifically, IAM • • Collaborate with Industry sector leaders to define and build sector-specific IAM strategies and solutions Serve as a trusted advisor on cyber security/IAM-related inquiries from client engagement teams, client leadership, Company leaders and third-party regulators • • Drives planning and execution of identity management roadmaps and technology enhancements EXPERTISE AND QUALIFICATIONS 12+ years of directly related experience in the IAM domain with specialization in one or more industry sectors, including broad cybersecurity and/or risk management experience • The ideal candidate is an integrator of people and processes, a thought leader, a problem solver, is knowledgeable about cybersecurity, and has a strong knowledge of security best practices and security technologies • Ability to gain and reach consensus and collaborate across leadership (Industry Sector, Regional Cybersecurity & IAM leaders) resolving differences by determining needs and creating mutually beneficial market-facing solutions • Comfortable dealing with complexity and ambiguity, but able to challenge and enquire to develop appropriate and achievable solutions to problems • • Ability to manage results by balancing strategic goals with tactical wins that focus on pragmatic solutions for the business Experience with growing an IAM practice including managing and coaching large teams of IAM strategists, architects, engineers, analysts, and operators. • Subject matter understanding of two or more Identity & Access Management solutions, with experience with developing IAM strategy/roadmap, performing IAM assessments, implementing IAM solutions, and operating IAM platforms (cloud, on-premises etc.) • • Subject matter understanding of IAM domains and experience with two or more domains: Identity Governance, Access Management, Advanced Authentication, Consumer Identity, Privileged Access Management, and Cloud Identity Ability to frame and communicate security and risk-related concepts to technical and non-technical audiences at various levels • Advanced generalist - organizational skills and experience, including project- or role-based experience in the following: policy and standards, risk management and reporting, and change management / adoption-level and executive interaction experience • • Demonstrated experience driving strategy with cross-functional executive level stakeholders Demonstrated ability to drive organizational change and work with multiple business units of an organization to effect change • • Bachelor's Degree or equivalent experience in Information Security, Computer Science, or Information System • Professional security certifications such as CISSP, CISA, or equivalent experience. Certified in one or more IAM products Broad knowledge and experience across IT infrastructure with security frameworks and standards such as NIST 800-153, 800-171, PCI, IRS, ISO 17799/27001, and other relevant security-related regulations • • Excellent communication, interpersonal and analytical skills with critical and logical thinking. Must be able to interact effectively with professionals at all levels and communicate recommendations with diplomacy and tact • Benefits: Open Culture– We are not a Company, we are a team - A team of forward-thinking professionals from diverse backgrounds, united by our passion for excellence. We practice an open-door policy, so our members can approach our leaders to talk about growth, address their grievances, or get guidance. At Inspira Learning never stops - In fact, it's encouraged. We've tied up with LinkedIn learning to help people skill up their technical and behavioral aspects through e-learning or self-paced programs. These include Leadership development programs, one on one coaching for High Potential employees, special incentive programs for cyber security, cloud and such other niche skill-based certifications. Career development opportunities: We Care - We want our employees to know we care. That's why we offer best-in-class benefits including: • Paid Time off & Holidays. • Medical, Dental & Vision Insurance. • Short Term & Long-Term disability. • Voluntary Term Life Insurance. • Benefit - 401K. Employee Wellbeing – Ensuring the mental wellbeing of our employees, we've tied up with SilverOak for our ʻEmployee Assistance and Well-being Programʼ. We conduct regular workshops for employees on topics such as Mindfulness at Work, Dealing with stress for better stress management, etc. Diversity, Inclusion & Equal opportunity – Inspira aims to ensure non-discrimination and equal access to work-related opportunities. Inspira strongly prohibits discrimination based on race, color, religion, gender, national origin, genetic information, age, or disability.

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Insurance companies take risks over customers. Risk management is a very important aspect of the insurance industry. Insurers consider every quantifiable factor to develop profiles of high and low insurance risks. Insurers collect vast amounts of information about policyholders and analyse the data. As a Data scientist in an insurance company, you need to analyse the available data and predict whether to approve the insurance or not

predicting using ANN

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deep learning in finance and insurance