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Showing posts from August, 2021

What Are The E-wallet Trends In Malaysia?

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The increasing use of E-wallets in the Southeast Asian region has put Malaysia in the limelight of the digital revolution. Consumers are moving from cash to contactless payments, as they find the modern convenience of managing their daily finances through their mobile phones more enjoyable. However, the E-wallet trend in Malaysia is moving at an even faster pace as the use of cash has decreased during the COVID pandemic. A study conducted by Mastercard in 2020 showed that Malaysia is a leader among its Southeast Asian (SEA) neighbors in terms of digital wallet usage. The study also found that E-wallet usage is 40 percent in Malaysia, 36 percent in the Philippines, 27% in Thailand and 26% in Singapore. Mastercard collected data from 10,000 consumers in the Asia Pacific region. Where customers use E-wallets? In SEA countries, E-wallet usage has increased by 8% since 2019. Among all payment methods, cash was still the most preferred payment method for the SEA population in 2020 followed b

Best Practices for E-Learning Development to Ensure Faster ROI

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  With the increasing demand for online education triggered by Covid-19, edtech startups are coming up with innovative e-learning development methods, which are all set to revolutionize the education industry. The primary reason behind this is that many edtech startups are not developing edtech solutions that keep best development practices in mind. E-Learning Market Trend During the first thirty days of the COVID-19 outbreak, 10.3 million people enrolled for Coursera. This was an increase of 644% in new enrollments as compared to the same period in 2019. According to Globe News Wire, the global e-learning market is estimated to grow from USD 144 billion to USD 374.3 billion in 2019. A Crunchbase report found that investment in edtech grew by almost 58% in the first two quarters of 2020 to reach USD 4.1 billion. Although the number of investment deals decreased in 2020, the size of the deals increased. In 2018, 427 deals raised USD 4 billion, while in 2020, only 279 deals raised USD 4.

Why is Mobile Banking Application Important?

Now consumers can access banking services from anywhere by using mobile banking. In addition, businesses and business owners can now save time by using mobile applications to process their payments and even receive money from customers directly to their phone numbers. The pandemic has highlighted the need to meet the digital banking needs of the customer. Several studies have confirmed that the pandemic has had a dramatic impact on the digital banking infrastructure. For example, a survey by Entersekt and PYMNTS.com found that it has been a 200% jump in mobile banking usage since the start of the pandemic. Therefore, it becomes clear for banks to invest in the development of mobile banking apps. Cost to Develop a Mobile Banking Application Banking app development is dependent on several factors, including the technology used and the features implemented. Operating system, technology preference, and app features are also the direct factors influencing the cost of banking app development

The Complete Guide For Machine Learning App Development

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  Every day, rapid advances in technology are increasing. Still, it entertains us well. If we talk about AR (Augmented Reality), we play games on mobile phones in the real world or AI chatbots talking to us and having real conversations. Due to the rapid development and advancement of technology, ML (Machine Learning) is no longer a strange thing to us. Machine learning app development is gaining popularity and momentum recently due to user needs and greater efficiency.  Now, before moving forward, first, let’s understand what machine learning is?  What is Machine Learning? Machine learning is a method of data analysis that automates analytical model building. Moreover, it is a branch of (AI) artificial intelligence based on the idea that systems can learn from data, make decisions with minimal human intervention, and identify patterns. Machine learning is a way of understanding data that makes up model building. Machine learning apps can be divided into three ways: Reinforcement Machi