Artificial Intelligence (AI) is quite powerful and is constantly evolving and currently knows no bounds. It is focused on outperforming its limits using the power of Machine Learning (ML). AI is empowering computers to do things that human beings are unable to do efficiently and effectively and machine learning is aiding the computers to do so by breaking the rules of traditional programming.
One of the biggest preventable losses that hurts insurers worldwide is fraudulent insurance claims. The P&C segment accounts for the most fraudulent insurance claims, with auto insurance and workers’ compensation making up the biggest percentage of fraudulent claims that have an annual impact on the insurance business.
Customers are demanding new software products that are easily customizable and more functional and capable than the previous edition, which has presented DevOps with new hurdles. Customers desire these things in the present and at a low cost. In contrast to what they demand, software development has historically been beset by large expenditures and protracted development durations. Fortunately, service virtualization is making it simple and constraint-free for DevOps to satisfy customer demand.
Data annotation is one of the principal factors in the development of artificial intelligence (AI) and machine learning (ML). As technology is advancing at a fast pace, almost all industries will need to make use of data annotations to keep up with the trends. The use cases for data annotation and encompassing computer vision are enormous.
As we transition to a cloud computing architecture, data security and privacy must be given top priority. Data loss or data leakage can negatively affect an organization’s brand, reputation, and customer confidence. Data misuse is possible when multiple organizations share resources. Therefore, it is essential to protect data repositories as well as the data that is involved in processing, transport, or storage to reduce risk.
There has been a lot of buzz around decision intelligence (DI) recently. By 2030, 70% of firms, according to McKinsey, would employ decision intelligence in one way or another. Organizations are more likely to be data-driven as the popularity of big data and cloud computing continues to soar. Organizations who earlier desired to be data-first entities are now searching for more beyond merely being data-driven due to new difficulties and keeping competitive at the forefront.
Businesses need to reimagine the what and how of quality when they implement digital at scale. Quality today encompasses more than just functioning; it also includes customer experience and regulatory compliance. The path forward demands reimagining QA to ensure success in digital considering the increasing adoption of DevOps, Agile, and the influx of AI. Strong quality assurance is crucial as businesses streamline, upgrade, and secure their legacy environments for the digital era.
An agile maturity assessment is an exercise/tool to evaluate the current state of agile maturity and check on how successful the agile implementation is in an organization. The Agile maturity model clearly captures the areas for improvement and establishes goals to upscale the way your team works to adapt to business changes efficiently. Agile adoption statistics show the significant growth of 86% in Agile adoption within software development teams.
A token is a value that goes securely through the network to process payments without exposing actual card data. Tokenization aims to replace or represent specific sensitive information without compromising its security. The 16-digit payment card account number is replaced with a unique digital identifier, or the “token,” for mobile and online transactions. The tokens are randomly assigned, so it’s near impossible to reverse-engineer or compromise a token.
