3 Possible Application of Machine Learning in finance
One area of fascinating area of technological advancement is machine learning and artificial intelligence. Even in India, Technology has enabled Finance more accessible and the result is Reserve Bank of India is finalising draft for Peer to Peer Lending License in India. Let’s evaluate some future financial areas that can really benefit from machine learning Evaluating credit score of clients It is becoming extremely hard to correctly determine the eligibility of a loan borrower. Even after careful evaluation of all available parameters, some successful companies and individuals still default their bank loan. This is not a nice trend. Loan eligibility evaluation tasks will be taken over by the smart machine learning technology. To determine the credit score of a client, machine learning can apply regression algorithms which are accurate.
The Future of the Bitcoin Market
Nobody takes dollars at 100% interest at exchanges, even though BTC is growing faster. And no wonder – cheaper dollars can be gotten locally in any country, the same cannot be said for the BTC, whose market is international, somewhat transparent as far as exchange price dynamics are concerned and visible to all, except in cryptopyramids, of course. A currency with such a swap rate should lose its value over a long period of time, e.g. as we have seen, historically, on the USDRUR graph. But a looming fall does not mean there can be no lengthy perk-up periods with so-called carry trade. Or let us point to how the Italian lira showed strong vs. The German mark in 1996-1999, when the swap rate reached 11% annual. An even more immediate example is carry trade with USDRUR in 1999-2008 and 2016-2017 at higher interest rates
The role of DevOps and its connection to enterprise architecture
Every DevOps model should be linked to any application that it supports, and businesses should then identify the EA business processes that those applications support in turn. This lets enterprise architects map out a zone of business impact for each DevOps process, and this mapping should be as fundamental a part of DevOps documentation as the target applications or components impacted would be. That way, the impact of DevOps changes on business processes -- even if the impact is simply a risk of disruption during the change -- can be assessed. That requirement will also insure that development teams understand the business process lifecycle, or even lifecycles, that their application lifecycles may impact.
How can CIOs help create the next generation of IT leaders?
"The connection between the CIO and business partners is now occurring at a much higher level," he says. "That's the direction we're heading in -- the digital leader as an agent of change is on the rise, while overhead and service provider roles are on the wane. CIOs are now truly embroiled in business strategy and the direction of the company." This emphasis on change has led some experts to suggest that CIOs could be surpassed by new up-and-coming C-suite positions, such as chief digital officer and chief data officer. The rise of these new roles might leave up-and-coming IT professionals to conclude that their senior career objectives would be better served by avoiding the CIO role. Mitchell is unconvinced. While he has heard anecdotal evidence of firms appointing CDOs to lead digital change, there are executives in other organisations who believe a CIO's hard-won experience of running transformation is worth its weight in gold.
Using machine learning to improve patient care
“The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment,” says PhD student Harini Suresh, lead author on the paper about ICU Intervene. “The goal is to leverage data from medical records to improve health care and predict actionable interventions.” Another team developed an approach called “EHR Model Transfer” that can facilitate the application of predictive models on an electronic health record (EHR) system, despite being trained on data from a different EHR system. Specifically, using this approach the team showed that predictive models for mortality and prolonged length of stay can be trained on one EHR system and used to make predictions in another.
Top 5 traits of highly effective data scientists
Using data to solve problems is an essential element of the job, but data scientists must be able to ‘think outside of the box’ in other aspects of the position as well. Because the industry is so new, data scientists might find themselves without the proper tools and resources to complete a certain task. According to the CrowdFlower survey, 14 percent found themselves without adequate tools. HR managers should look for candidates that can get around this problem and use resources at hand to complete data tasks. Alternatively, data scientists that know what resources are necessary to get the job done and can request these tools are strong candidates as well. As the industry catches up with the need, this will change, but data scientists should be able to cope with the lack of technology and still complete necessary projects.
Why is data integration critical for business success?
As they attempt to support their digital transformation, companies and governments have to face the fact that expectations are continuing to increase exponentially; therefore they need to support increasing data volumes, more data types and data sources, more complex use cases and to deliver data insights out to more and more end users. Also, organisations face problems as data reside not only on-premise, but also in different applications, databases, file formats, and as well as in the cloud. We help them to get value out of that data by leveraging next generation technologies like real-time, machine learning, and self-service capabilities. We are seeing a lot of our customers moving to multi and hybrid cloud environment, and we can help ease this migration.
What Is IaaS? The Modern Data Center Platform
Similar to other cloud computing services, IaaS provides access to IT resources in a virtualized environment, across a public connection that’s typically the internet. But with IaaS, you are provided access to virtualized components so that you can create your own IT platforms on it—rather than in your own datacenter. The pool of IaaS services offered to clients is pulled from multiple servers and networks that are generally distributed across numerous datacenters owned and maintained by the cloud provider. IaaS resources can be either single-tenant or multitenant, and they are hosted at the service provider’s. “Multitenant” means multiple clients share those resources, even though their systems are kept separate. This is the most common way to deliver IaaS because it is both highly efficient and scalable, allowing cloud computing’s generally lower costs.
True Democratization of Analytics with Meta-Learning
The democratization of analytics has become a popular term, and a quick Google search will generate results that explore the necessity of empowering more people with analytics and the rise of citizen data scientists. The ability to easily make better use of your (constantly growing) pool of data is a critical driver of business success, but many of the existing solutions that claim to democratize analytics only do so within severe limits. If you have a complex business scenario and are looking to get revolutionary insights using them, it’s easy to come away disappointed. However, the democratization of analytics isn’t just a buzzword that refers to a narrow approach. It’s possible to do so much more. Let’s quickly review the current state of the market that you’re likely familiar with, and then dive into our proposed solution.
Artificial Intelligence And Big Data: Good For Innovation?
The most dramatic advances in AI are coming from a data-intensive technique known as machine learning. Machine learning requires lots of data to create, test and “train” the AI. Thus, as AI is becoming more important to the economy, so too is data. The Economist highlighted the important role of data in a recent cover story in which it stated “the world’s most valuable resource is no longer oil, but data.” In this sense, both the ability to obtain data about customers, together with the ability to program AI to analyze the data, have become important tools businesses use to compete against each other, and against potential entrants. A potential entrant that lacks access to good data faces substantial hurdles, and this has led some regulators to question the extent to which control over data creates barriers to entry
Quote for the day:
"When a person can no longer laugh at themself, it is time for others to laugh at them." -- Thomas Szasz
No comments:
Post a Comment