What's also interesting is, despite this workload increase, the majority (77%) feel they have been very effective at supporting employees working from home. This is great to hear, and not entirely surprising, as these companies rely on SaaS to run their businesses. On the flip side, laggards running legacy infrastructures have seen productivity go to zero. This is definitely a tipping point for the adoption of SaaS. Our survey also reinforces this sentiment, as 47 percent of respondents said they will increase the use of SaaS as a result of the pandemic. ... IT teams at every company we work with have had to implement new processes to support the entire employee base, leveraging and adjusting methods, tools and processes to enable business continuity with nearly 100% work-from-home workforce. Work from home is not a new concept, but supporting traditional remote laptop users is not the same challenge as supporting desktop users that may not be using corporate-issued devices and computers. Companies were forced to immediately implement new processes for the entire employee base, leveraging methods that were effective for laptop users who were already effective remote users.
As banks re-evaluate their digital strategies, it only makes sense to ensure compliance is automated in order to easily and efficiently adhere to all AML, KYC and CTF regulations. Regulation technologies, which use Artificial Intelligence (AI), are particularly valuable when it comes to automating compliance. AI can help mine huge volumes of data, automatically flagging risk-relevant facts faster than humanly possible. AI technology dramatically speeds up the onboarding phase. The technology helps to automatically identify illicit client relationships and alert financial institutions to the possibility of criminal or terrorist activity. With regulatory requirements being constantly updated, it can be difficult for banks to keep on top of these changes via manual processes alone. By implementing AI technology, financial institutions are better able to identify gaps in customer information, with the technology automatically prompting them to perform regulatory outreach to collect the outstanding information – a far more streamlined and hands-off approach to what many banks in Singapore are currently using.
Modernization simply means replacing the core platform with the best-in-class option. The reality: Core-platform replacements often have higher up-front investment costs than in-place IT modernization, as they require both software and hardware, experts’ time, and extensive testing. Furthermore, migrating existing policies and their implicit contracts to a new platform is often expensive—these additional costs need to be factored into any decision—and time consuming. One big reason for high modernization costs is the age and quality of the policy data and rules—poorly maintained policies are expensive to refresh and modernize to work in a new system. Product types and geographic context are also considerations. For instance, US personal property and casualty (P&C) policies are generally issued annually and thus have up-to-date policy data and rules; this makes migration efforts more straightforward. By contrast, in countries such as Austria or Germany, policies are refreshed annually to adjust premiums for inflation, but policy data, rules, and terms only change when a customer switches to a new policy—which may not happen for many years. Therefore, policy rules need to be carried over to the target system or customers need to switch to a new policy during modernization, rendering it time consuming.
Microsoft promises the data in the score card is the product of "meticulous and ongoing vulnerability discovery", which involves, for example, comparing collected configurations with collected benchmarks, and collecting best-practice benchmarks from vendors, security feeds, and internal research teams. Defender ATP users will see a list of recommendations based on what the scan finds. It contains the issue, such as whether a built-in administrator account has been disabled, the version of Windows 10 or Windows Server scanned, and a description of the potential risks. For this particular risk, Microsoft explains that the built-in administrator account is a favorite target for password-guessing, brute-force attacks and other techniques, generally after a security breach has already occurred. Defender ATP also provides the number of accounts exposed on the network and an impact score. Users can export a checklist of remediations to be undertaken in CSV format for sharing with team members and to ensure the measures are undertaken at the appropriate time. An organization's security score should improve once remediations are completed.
Physical data models present an image of a data design that has been implemented, or is going to be implemented, in a database management system. It is a database-specific model representing relational data objects (columns, tables, primary and foreign keys), as well as their relationships. Also, physical data models can generate DDL (or data definition language) statements, which are then sent to the database server. Implementing a physical data model requires a good understanding of the characteristics and performance parameters of the database system. For example, when working with a relational database, it is necessary to understand how the columns, tables, and relationships between the columns and tables are organized. Regardless of the type of database (columnar, multidimensional, or some other type of database), understanding the specifics of the DBMS is crucial to integrating the model. According to Pascal Desmarets, Founder and CEO of Hackolade: “Historically, physical Data Modeling has been generally focused on the design of single relational databases, with DDL statements as the expected artifact. Those statements tended to be fairly generic, with fairly minor differences in functionality and SQL dialects between the different vendors. ...”
These AI systems are trained on huge amounts of data and you'll find bias when, say there's facial recognition. If all your facial recognition data set is Caucasians, it's going to have trouble identifying people by their races. And being misidentified by facial recognition is not a good thing when it comes to law enforcement, other things like this. So, we're finding, even through the course of making the film, this technology moves so fast, but we've seen a lot being done to address the problem of bias in data sets since we started. And they're finding that more diversity within these data sets actually has helped reduce bias in a lot of these algorithms, which is a positive sign. But at the end of the day, I think we're still at the point where we don't want to give these algorithms too much control. I think there needs to be humans in the loop that understand ethics and not everything in life boils down to zeros and ones, and Xs and Os. So, I think it's good to have humans in the loop and also society in the loop, not just the people designing these technologies, but society as a whole should be hip to what's going on. Because if not, you're going to wake up in 20 years and going to be living in a very different world, I think.
Because 5G technology can now be cloud orchestrated—that is, use software-defined principles to manage the interconnections and interactions among workloads on public and private cloud infrastructure—the behavior of the 5G network can be changed to accommodate specific applications for specific uses. Roese shared a dramatic example of this by describing a telehealth scenario in which suspected stroke victims could be diagnosed and receive initial treatment while en route to the hospital. This would be accomplished by using the continuous collection and streaming of patient data. “In order to do that, a whole bunch of conditions had to be true,” said Roese. “You had to push the code out to an edge, so it can operate in real time. You had to execute a network slice to guarantee the bandwidth and give this a priority service.” If such allocation were done manually, it might take three hours or more to reconfigure the network. One thing that makes mobile triage possible is strength at the edge of the cellular network. That is also crucial for innovation—as well as for the average 5G user. “What that means is you’re walking around in a city and if you constantly get 100-to-200 megabits per second, the peak rates might be five-to-10 gigabits per second,”
Before moving into the explanation part we need to have a clear understanding of concrete class. A class that has an implementation for all of its methods is called Concrete class. They cannot have any unimplemented methods. The concrete class can extend the Abstract class or an interface as long as its implements all the methods of those too. Simply, we can say that all classes which are not Abstract class are considered as Concrete classes. Actually, according to Head First Design Patterns, Simple Factory is not considered as a Design Pattern. Let’s get started understanding the Factory Pattern varieties. The Simple Factory Pattern describes a way of instantiating class using a method with a large conditional that based on method parameters to choose which product class to instantiate and then return. Let’s dive into the coding example where the Simple Factory Pattern comes into play. Imagine a scenario, where we have different brands of smartphones. You need to take the specification details of the respective brands where the brand name is passed as a parameter through the client code.
Instead of having to call up customer care representatives and waiting to get their queries resolved, consumers should be able to quickly get relevant information simply by asking. The financial services industry is addressing the one-click, on-the-go behaviour of consumers by launching various innovative solutions, such as mobile wallets, which have become a highly convenient method of payment; and chatbots, which have become very popular. Banks are constantly looking to enhance customer experience by providing ways to customers to get the desired information as and when they want. The opportunity lies in integrating all branch transactional activities with voice technology. Currently, voice assistants handle basic customer queries, such as checking account balances, making payments, paying bills and getting account-related information. The simple nature of these requests enables institutions to instantly provide the right information at the right time; however, this is unlikely to provide a competitive advantage in future. Companies that reimagine the customer journey across channels, products, and services with end-to-end integration, will emerge as winners.
Distributed leader technologies, widely known as blockchains, have already moved from the shade of public interest and now are treated as paradigm-changing technologies that turn the interaction between Fintech and banks upside down. The research held by Accenture shows that 9 in 10 executives are considering the implementation of blockchain technology into their financial services. Blockchain aims at boosting mutual benefits and reducing business risks from collaboration and mutual Fintech investment banking. Using a decentralized database, banks receive an opportunity to work together on a common solution, keeping their own data security and opening certain pieces of data only when they want to interact and trade. It ensures complete transparency and real-time execution of payments what significantly minimizes the possibility of cyber-attacks as the information doesn’t exist in a centralized database anymore. Blockchain technology is also very helpful in KYC (Know Your Customer) Compliance. In traditional banking, it usually causes delays to banking transactions, entails substantial duplication of effort between banks and third parties, and ends up at high costs.
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