Software Engineering Insights

The banks of today are witnessing sweeping changes at an unprecedented pace. Changing consumer expectations, new technology innovations, and unconventional business models are driving these changes in the banking sector. Banking officers operating in the technological sphere are realizing the potential of cloud banking to help them cope with these modernizations.

The FinTech domain has recently become an integral component of banks and Financial Institutions (FI). FI-FinTech collaborations are especially crucial in handling the vast and ever-increasing burden of regulations in the financial sector. Regulatory changes are being rolled out at a staggering rate in the finance sector. This has driven FI stakeholders to turn to niche technology solutions collectively known as Regulatory Technology (RegTech). 

There has been a steep rise in the demand for Emergency Medical Services (EMS) apps and platforms that ensure smooth patient hand-off. The transitioning of patient care from the prehospital team to the Emergency Department (ED) doctors and nurses is the most pivotal aspect of these platforms. 

Log analysis is an integral part of any software development lifecycle's ultimate success. Developers and engineers use logs to assess what is happening at every layer of a software system and track down the root cause of issues. As the software development process produces a large amount of distributed log data, it is often difficult to analyze it all sufficiently.

Most recent conversations around making the software development lifecycle more efficient revolve majorly around two concepts, app containerization, and virtualization. Virtualization and containerization are two of the most widely implemented mechanisms for hosting and developing an application.

Software developers are always on the lookout for reliable open-source infrastructure alternatives for developing and deploying applications. That is where Kubernetes, a cloud-native cluster management software solution, comes in. It provides comprehensive solutions for automated app deployment and interoperability among microservices.

Today, Artificial Intelligence (AI) has found a significant place in our lives and across a broad range of industries and businesses. But most of us, including industry stakeholders, have a very vague understanding of how AI systems make the decisions that they do. That is where Explainable AI (XAI) comes in handy, producing transparent and detailed explanations for the way AI functions.

Healthcare organizations looking to optimize patient outcomes are increasingly adopting Electronic Medical Records (EMR) software. In fact, EMR is now virtually an integral part of healthcare and a widely agreed-upon standard. Taking full advantage of this technology requires proper EMR integrations with third-party ancillary platforms and systems.

The impact of the various applications of AI in automation testing is well known. Moreover, AI is widely accepted as an essential accompaniment for the seamless delivery of any software product these days.

Businesses often need to analyze highly subjective data such as customer feedback, reviews, and recommendations to aid in their brand decision-making. But simply automating data analysis leads to the nuances of this data being overlooked. Sentiment analysis with Machine Learning (ML) models provides a more comprehensive solution to this problem.