Machine learning not only saves time on building a fraud detection routine but also can remove bias against certain taxpayers if done properly. If supervised learning is fishing where people have fished before, then another type of machine learning—unsupervised learning—is fishing where no one has fished before. Both supervised and unsupervised approaches provide tremendous value for government tax authorities, especially when used upon complex data sets like tax returns, financial transactions, taxpayer contacts, accounts receivables, network traffic, and even employee activities.
Privacy and security considerations are the key ingredients of digital trust and must be at the heart of any industry’s digital transformation. The necessarily transversal nature of security and privacy matters needs to be woven into the fabric of an organisation for the digital transformation to succeed over the long-term. At this junction, the traditional role of the CISO – heavily influenced by a technical bias, tactically-oriented and project-driven in many firms – could become exposed.
Blockchain helps insurance sector in providing privacy, borderless reach to make smarter decisions and also deliver insurance with quality. This is due to the features of blockchain that make it possible with the decentralized network. Though blockchain in insurance sector helps the industry to provide a much better service, there are many challenges faced while adopting to blockchain. The question is how far is your insurance company would go to break down these challenges and survive in the market and how well do use these challenges to your advantage.
The Internet of Things (IoT) is increasingly part of our everyday lives, with so-called “smart” devices. But for all their undoubted technical merits, they also represent a growing threat to privacy. There are several aspects to the problem. One is that devices may be monitoring what people say and do directly. Another is the leakage of sensitive information from the data streams of IoT devices. Finally, there is the problem summed up by what is called by some “Hyppönen’s law“: “Whenever an appliance is described as being ‘smart’, it’s vulnerable”.
Fintech firms are increasingly escalating the pace of revolution with the help of cutting-edge technologies. They are now looking to combine two incredible technologies to become a differentiator in the competitive market. Robotic process automation (RPA) and AI have become a disruptive force in the fintech sector. Augmenting AI to a rule-based robotic process automation system gives rise to another tool that not only automates tasks but also possesses decision-making capabilities. All of this will in turn increase accuracy, boost productivity, and increase a company’s bottom line.
Efforts are underway among the ERP vendors to consider more innovative ways to handle the massive data demands of ERP and in fact surface more obvious value from ERP through machine learning and ERP algorithms. AI-enabled ERP can ultimately mean more intuitively surfacing access to all ERP data services through the methods that users would feel most comfortable using. Moving ERP systems from reporting on historical activity and getting them to be helpful with predicting and forecasting behaviour and outcomes become much more of a likelihood when AI and machine learning is applied to ERP systems.
There is great potential in leveraging AI and deep learning to help with the tax process. Tax fraud has existed in many forms for year, but it has become particularly widespread with the huge increase in identity theft. Taxpayers and tax agencies want transparency. Compared to other types of data-driven analysis, the amount and quality of the data is more important with deep learning models. While the picture of deep learning for tax preparation sounds bleak and ominous, there are places in tax administration where deep learning is appropriate and beneficial.
It seems strange then that the United Nations has coined digitization as an instrumental component for achieving sustainable development goals. After all, the concept does have some requirements for moving away from digital mediums as a means to lower dependency. The reality is, many digital tools exist for improving sustainability levels, particularly when it comes to tracking resource consumption. Certain IoT devices, for example, can monitor and report water and energy usage. The incoming information can then improve processes or operations, cutting down on total consumption.
To meet growing service and asset requests, agencies are looking to IT asset management solutions that incorporate robotic process automation and artificial intelligence. In recent years, IT asset management has become an important part of an overall security strategy for many agencies after several highly publicized security breaches. Incorporating RPA with AI into next-generation IT asset management solutions will also help agencies that are struggling to meet IT asset management objectives due to limited resources. Several key areas will see changes as a result of incorporating RPA and AI into IT asset management.
Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. Here are the four steps of AI powered strategy.
Going into business for yourself by forming a corporation or sole proprietorship is an exciting endeavor. Of course, from a taxation standpoint, it can also be a little overwhelming. These seven tips will help you navigate your tax requirements. You can begin to implement some of these tax tips for businesses practically overnight. Others are slightly more involved and might require input from your lawyer or accountant. All of them, however, have the potential to help your business.
Why exactly are more and more businesses embracing RPA technology and how can they ensure a successful transformation? What has quickly become clear is that RPA has the power to modernise how businesses operate. Deploying a virtual workforce can enable organisations to drive a whole host of workforce advancements, with robots taking over many of the more mundane, rules-based processes. For example, RPA robots can complete tasks such as processing transactions or filling out forms faster, meaning employees will no longer have to make repetitive, transactional decisions.
For programmers, this is a good time to think about new skills you want to learn or interesting projects you want to take part in. Below we present some of the major programming trends to prepare for to help you stay at the top of your game in 2019. The top three programming trends to watch in 2019 are the rise of Python, TypeScript, and Go. All three are great choices if you’re looking for a new language to learn.
Because of the technology, quality standards have improved considerably across the board. Engineering processes have become much more efficient and reliable, operations are better controlled, and the related system architecture is much easier to maintain and manage. While QM technology may be tried and true, various applications are still considered relatively young. These trends are on the bleeding edge of the field, offering their own sets of advantages. Provided the technology and necessary infrastructure is already in place, here are the top quality management technology trends you should be keeping an eye on.
Linear regression is one of the most popular and best understood algorithms in the machine learning landscape. Since regression tasks belong to the most common machine learning problems in supervised learning, every Machine Learning Engineer should have a thorough understanding of how it works. This blog post covers how the linear regression algorithm works, where it is used, how you can evaluate its performance and which tools & techniques should be used along with it.
Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals. But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the necessary capabilities.
Decision tree is a type of supervised learning algorithm that can be used in both regression and classification problems. It works for both categorical and continuous input and output variables. The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
Thanks to emerging laws, there seem to be more ways than ever to run afoul of compliance and reporting requirements. Compliance represents a number of different types of challenges. Some of these, like social and political pressures, are the inevitable consequence of globalization. Others, like technological and regulatory pressures, require that companies balance self-interest and convenience with the needs of the environment and human institutions. Here's a look at how technology helps to address each of these in turn.
Data breaches are causing companies and organizations everywhere to re-examine the things they procure, the services they use, the individuals they hire, and the people and firms with whom they partner and do business. Across the board, organizations have sunk money into staff, strategies, and equipment to comply with new, tighter customer privacy rules and sidestep major fines and other penalties. Privacy laws and regulations worldwide are evolving and expanding. And the ones that took early action to address security concerns are seeing positive results from their investments.
Everyone wants to be innovative. Innovation refers to the successful conversion of concepts and knowledge into new products, services, or processes that delivers new value to society or the marketplace. Most of the project management professionals have no idea how to systematically foster innovation or make it an integral part of the DNA of their projects. As a project manager, you have to actively solicit ideas that add value throughout the project lifecycle in order to ensure that the desired innovative result is achieved. Let’s start with the fundamentals.
Cybersecurity has developed a high profile in many organisations over the past few years. But, who wants to be a Chief Information Security Officer these days? And at which stage in your career should you consider the move? What balance of managerial and technical experience do you need to have? And where do you go from there? Those would be valid questions for many executive positions but when it comes to the role of the CISO, they seem to acquire a different meaning.
Despite a demand for faster release, cycles of weekly rather than monthly, development teams can find it difficult to integrate the necessary set of tools into their pipeline to make updates ahead of a deadline. How do we give teams the capabilities to keep up with this industry wide trend of accelerated release cycles? Successful DevOps involves a tuneup of Agile processes. A thorough spring cleaning can help teams get there. Here is how.
There are many ethical controversies surrounding artificial intelligence algorithms in the past few years. In tandem with advances in artificial intelligence, there is growing interest in establishing criteria and standards to weigh the robustness and trustworthiness of the AI algorithms that are helping or replacing humans in making important and critical decisions. With the field being nascent, there’s little consensus over the definition of ethical and trustworthy AI, and the topic has become the focus of many organizations, tech companies and government institutions.
Each day brings a new set of challenges for early stage ventures. Decisions impact where you spend money, who you hire, and where to focus. The start-up roller coaster makes maintaining a disciplined decision-making process seem impossible. Everything looks important. But they are not. And everything is not urgent. Making good business decisions starts with having a clear understanding of who you are and what you want to be. Start with a target end state and then build a mission and vision statement. Here are 4 simple steps to help make fast, high quality decisions.