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From fraud detection to simplified underwriting, machine learning is improving life insurance.

It is a critical component of any financial plan. Life insurance offers financial protection to individuals and their loved ones in case of unforeseen events like illness, disability, or death.

In the past, the life insurance industry has relied on traditional underwriting methods to determine premiums and policy terms.

With the advent of machine learning, the life insurance industry is experiencing a significant digital shift in the way policies are priced, marketed, and underwritten.

One of the most significant advantages of using machine learning in life insurance is its ability to improve pricing and underwriting accuracy. Insurers traditionally relied on static underwriting factors, such as age, gender, and medical history, to assess risk and determine premiums. It's important to state that this approach is often limited in its scope and fails to capture the complex relationships between risk factors.

Machine learning algorithms can analyze large datasets and identify patterns and relationships that were previously unknown. By incorporating non-traditional data sources, such as social media activity or wearable device data, insurers can create a more comprehensive risk profile for policyholders. This approach can lead to more accurate risk assessments and pricing models, reducing errors and improving policyholder satisfaction.

Another area where machine learning is transforming the life insurance industry is in the creation of personalized policies. Personalized policies are tailored to individual policyholders based on their unique characteristics and risk profiles. Traditional underwriting methods often rely on broad categories, such as age or gender, to determine policy terms. However, this approach fails to capture the individual nuances of each policyholder's risk profile.

Machine learning techniques can analyze vast amounts of data to create personalized policies that better reflect a policyholder's individual risk profile. These policies can be tailored to the specific needs and goals of each policyholder, leading to increased customer satisfaction and loyalty.

Another significant area where machine learning is transforming the life insurance industry is in claims processing. Traditional claims processing is often time-consuming and involves significant manual labor. Insurers must review documents, communicate with policyholders and medical professionals, and make complex calculations to determine payouts.

Machine learning techniques can automate many of these processes, leading to faster and more accurate claims processing. By analyzing data from various sources, such as medical records or police reports, machine learning algorithms can assess the validity of claims and calculate payouts accurately.

Insurance fraud is a significant issue for the life insurance industry. Fraudulent claims can lead to significant financial losses for insurers, which can ultimately impact policyholders. Traditional methods of fraud detection often rely on manual review processes or simple rules-based systems, which can miss more complex cases of fraud.

Machine learning algorithms can analyze large amounts of data to identify patterns and anomalies that may indicate fraudulent behavior. By using advanced techniques such as anomaly detection or clustering, insurers can more accurately detect and prevent fraud, reducing losses and improving policyholder satisfaction.

Machine learning is revolutionizing the life insurance industry in many ways. By improving pricing and underwriting accuracy, creating personalized policies, streamlining claims processing, and detecting fraud, insurers can better meet the needs of policyholders and improve the overall customer experience. There are still challenges to overcome, such as data privacy concerns and the need for continued innovation and adoption. As the life insurance industry continues to evolve, it is clear that machine learning will play a critical role in shaping its future.

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Machine Learning in Life Insurance: Applications and Benefits - BBN Times

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In this talk, Sergey Levine discusses how advances in offline reinforcement learning can enable machine learning systems to learn to make more optimal decisions from data. (Video by: CITRIS and the Banatao Institute)

On Wednesday, April 12, Sergey Levine, associate professor of electrical engineering and computer sciences and the leader of the Robotic AI & Learning (RAIL) Lab at UC Berkeley, delivered the second of four Distinguished Lectures on the Status and Future of AI, co-hosted by CITRIS Research Exchange and the Berkeley Artificial Intelligence Research Group (BAIR).

Levines lecture examined algorithmic advances that can help machine learning systems retain both discernment and flexibility. By training machines with offline reinforcement learning (RL) methods, machines can solve problems in new environments by drawing on large sets of data and lessons previously learned while still maintaining the adaptability to introduce new behaviors, and thus new solutions.

As Levine explained, data-driven, or generative, AI techniques, such as the image generator DALL-E 2, are capable of producing seemingly human-made creations, while RL methods, such as the algorithms that control robots and beat humans at board games, can develop solutions that solve problems in unexpected ways. His research aims to discover how machine learning systems can adapt to unknown situations and make ideal decisions when faced with the full complexity of the real world.

Sergey Levine speaks about using large datasets for reinforcement learning at the Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) at UC Berkeley.

If we really want agents that are goal-directed, that have purpose, that can come up with inventive solutions, itll take more than just learning, said Levine. Learning is important, and the data is important, but the combination of learning and search is a really powerful recipe.

Data without optimization doesnt allow us to solve new problems in new ways. Optimization without data is hard to apply to the real world outside of simulators, he said. If we can get both of those things, maybe we can get closer to this space explorer robot and actually have it come up with novel solutions to new and unexpected problems.

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A path to resourceful autonomous agents | Berkeley News - UC Berkeley

Technology often means our lives are more convenient and secure. At the same time, however, such advances have unlocked more sophisticated ways for cybercriminals to attack us and corrupt our security systems, making them powerless.

Artificial intelligence (AI) can be utilized by cybersecurity professionals and cybercriminals alike; similarly, machine learning (ML) systems can be used for both good and evil. This lack of moral compass has made adversarial attacks in ML a growing challenge. So what actually are adversarial attacks? What are their purpose? And how can you protect against them?

Adversarial ML or adversarial attacks are cyberattacks that aim to trick an ML model with malicious input and thus lead to lower accuracy and poor performance. So, despite its name, adversarial ML is not a type of machine learning but a variety of techniques that cybercriminalsaka adversariesuse to target ML systems.

The main objective of such attacks is usually to trick the model into handing out sensitive information, failing to detect fraudulent activities, producing incorrect predictions, or corrupting analysis-based reports. While there are several types of adversarial attacks, they frequently target deep learning-based spam detection.

Youve probably heard about an adversary-in-the-middle attack, which is a new and more effective sophisticated phishing technique that involves the theft of private information, session cookies, and even bypassing multi-factor authentication (MFA) methods. Fortunately, you can combat these with phishing-resistant MFA technology.

The simplest way to classify types of adversarial attacks is to separate them into two main categoriestargeted attacks and untargeted attacks. As is suggested, targeted attacks have a specific target (like a particular person) while untargeted ones dont have anyone specific in mind: they can target almost anybody. Not surprisingly, untargeted attacks are less time-consuming but also less successful than their targeted counterparts.

These two types can be further subdivided into white-box and black-box adversarial attacks, where the color suggests the knowledge or the lack of knowledge of the targeted ML model. Before we dive deeper into white-box and black-box attacks, lets take a quick look at the most common types of adversarial attacks.

What sets these three types of adversarial attacks apart is the amount of knowledge adversaries have about the inner workings of the ML systems theyre planning to attack. While the white-box method requires exhaustive information about the targeted ML model (including its architecture and parameters), the black-box method requires no information and can only observe its outputs.

The grey-box model, meanwhile, stands in the middle of these two extremes. According to it, adversaries can have some information about the data set or other details about the ML model but not all of it.

While humans are still the critical component in strengthening cybersecurity, AI and ML have learned how to detect and prevent malicious attacksthey can increase the accuracy of detecting malicious threats, monitoring user activity, identifying suspicious content, and much more. But can they push back adversarial attacks and protect ML models?

One way we can combat cyberattacks is to train ML systems to recognize adversarial attacks ahead of time by adding examples to their training procedure.

Unlike this brute force approach, the defensive distillation method proposes we use the primary, more efficient model to figure out the critical features of a secondary, less efficient model and then improve the accuracy of the secondary with the primary one. ML models trained with defensive distillation are less sensitive to adversarial samples, which makes them less susceptible to exploitation.

We could also constantly modify the algorithms the ML models use for data classification, which could make adversarial attacks less successful.

Another notable technique is feature squeezing, which will cut back the search space available to adversaries by squeezing out unnecessary input features. Here, the aim is to minimize false positives and make adversarial examples detection more effective.

Adversarial attacks have shown us that many ML models can be shattered in surprising ways. After all, adversarial machine learning is still a new research field within the realm of cybersecurity, and it comes with many complex problems for AI and ML.

While there isnt a magical solution for protecting these models against all adversarial attacks, the future will likely bring more advanced techniques and smarter strategies for tackling this terrible adversary.

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What Are Adversarial Attacks in Machine Learning and How Can We ... - MUO - MakeUseOf

Researchers with the prestigious Indian Institute of Technology-Madras (IIT-M) have developed a machine learning-based computational tool for better detection of cancer-causing tumours in the brain and spinal cord. The web server known as GBMDriver (GlioBlastoma Mutiforme Drivers) is now publicly available online.

The GBMDriver was developed specifically to identify driver mutations and passenger mutations (passenger mutations are neutral mutations) in Glioblastoma. In order to develop this web server, a variety of factors such as amino acid properties, di- and tri-peptide motifs, conservation scores, and Position Specific Scoring Matrices (PSSM) were taken into account.

In this study, 9,386 driver mutations and 8728 passenger mutations in glioblastoma were analysed. Driver mutations in glioblastoma were identified with an accuracy of 81.99 per cent, in a blind set of 1809 mutants, which is better than existing computational methods. This method is completely dependent on the protein sequence.

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Glioblastoma is a fast and aggressively growing tumour in the brain and spinal cord. Although there has been research undertaken to understand this tumour, therapeutic options remain limited with an expected survival rate of less than two years from the initial diagnosis, the IIT-M said,

The research was led by Prof. M. Michael Gromiha, Department of Biotechnology, IIT-M and the findings have been published in the reputed peer-reviewed journal Briefings in Bioinformatics.

We have identified the important amino acid features for identifying cancer-causing mutations and achieved the highest accuracy for distinguishing between driver and neutral mutations. We hope that this tool (GBMDriver) could help to prioritize driver mutations in glioblastoma and assist in identifying potential therapeutic targets, thus helping to develop drug design strategies, Prof Gromiha said.

The Key Applications of this research include the methodology and features that are portable to apply for other diseases, and this could serve as one of the important criteria for disease prognosis.

Our method showed an accuracy and AUC of 73.59% and 0.82 respectively on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets, Ms Medha Pandey, a PhD Student at IIT-M, said.

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IIT Madras researchers develop machine learning tool to detect tumours in brain, spinal cord - Deccan Herald