How Machine Learning has modernized Mobile App Development?

Published in

Dew Solutions

7 min readOct 21, 2020

Machine Learning has revamped mobile apps as well as the app development process. From minimizing iterations to building more intelligent apps, this promising segment of AI has enhanced various aspects of mobile app development. Embedding machine learning in mobile apps brings in massive improvements in the way users perceive information/content along with significant profits for app development companies.

As technology moves towards offering smart mobile-centric solutions, more machine learning applications are being developed to transform the daily lives of humans. On the other hand, Machine learning technology will aid businesses to comprehend their actual requirements while it will assist developers in faster app development. Reports suggest the global machine learning market is expected to grow at a CAGR of 42.08% from 2018 to 2024.

From making the mobile app development agile to boosting app efficiency and effectiveness, machine learning provides ample benefits. In this article, we will walk you through some advantages of machine learning in mobile app development (from the developer’s perspective) as well as its use in different types of mobile applications. We will initiate with how app developers benefit from machine learning and how they leverage machine learning to create innovative apps.

1. Fraud Detection

Banks and other financial institutions still find it challenging to detect fraud during the use of credit cards and e-wallets. With online banking theft rising in some new ways every day, people are doubting its security level and slowly losing their trust. At the same time, it is also creating issues for banking institutions as it is hampering their growth and conversions.

It is therefore important, for businesses planning a mobile app to streamline their workflow, to ensure that the app can detect frauds. A primary cure for this is integrating Machine learning into mobile app development. At its core, the app would learn the patterns over time and identify whether the transaction has been initiated by the actual user or someone else. If the app notices something fishy, it will immediately notify the user.

2. Virtual Assistant

Machine Learning produces opportunities to create virtual assistants for mobile apps that would help understand users’ needs and aid them in managing or organizing their entire work, leading to enhanced productivity- The goal of a virtual assistant.

By incorporating machine learning technology into a mobile app, you are providing users with an assistant that can help them remember their tasks, remind them about bill payments, book online tickets, do online shopping, or anything that seems possible. Alexa, Siri, and Google Assistant are some of the best examples of virtual assistants that minimize human intervention and improve the overall efficiency.

3. Wireframes and Logic Development

Machine Learning proves handy in creating wireframes for mobile apps by utilizing Big Data. App developers can leverage machine learning to run technical feasibility tests during the development stage, with high speed and accuracy.

Machine Learning also tends to automate logic development. Developers are usually stuck on logic development that covers various possibilities and outcomes of a user’s input. It is not only time-consuming but delays app completion and time-to-market. Machine learning makes sure to take care of the crucial tasks of imagining possible scenarios and enhancing coding by recognizing patterns.

4. Predictive Analysis

Brands are adopting the concept of personalization and improving logic to make their platform more user-centric, in which integration of predictive analytics becomes crucial. However, for predictive analytics to operate on a huge complex platform, multiple resources would be required on-board with each working constantly.

Machine learning with the implementation of predictive analytics ensures faster and better recommendations. It will be able to analyze the past behavior and present needs of users. The technology helps apps process a massive chunk of data and obtain predictions that are customizable based on the users’ needs.

What started simply as a pattern recognition program is now used to perform specific tasks. Whether it’s the concept of a self-driving car or getting recommendations during online shopping, machine learning is behind it.

Machine learning modules have been trained to understand better and react intelligently. For instance, if you mention the word “attachment” inside the email content but there are no files attached, the application will notify the users before sending that the email doesn’t have any attachments.

Let’s now delve into the importance of ML in mobile applications.

1. Personalized Experience

Machine learning algorithms tend to analyze the user information available on social media platforms through their social media activities and use that information to their advantage. Users experience a personalized experience when they browse an app; recommendations start appearing on their feed.

The promotional ads that you witness on social media are also somewhere related to your activities. Machine Learning technology captures your activities and puts forward the relevant content accordingly.

2. Effective Search

As the data-driven world continues to evolve quickly, an effective and efficient search is becoming the need of the hour to provide a better and seamless user experience. Today, when users search for something on the Internet, they expect to get quicker and relevant results. Machine learning applications can facilitate search efforts. The algorithms integrated into the search engine comprehend the data or query and optimize the search outcomes without consuming much time. The search engine also utilizes the behavioral and graphical data to determine which results to display to the users.

3. Consumer Behavior Assessment

With the advancement in Artificial Intelligence, marketers seem highly concerned about consumer preferences. Based on the availability of user data such as their age, gender, geography, search queries, and application usage, behavioral variation can be utilized for an improved experience. Machine learning algorithms exploit this valuable data to evaluate consumer choices and simultaneously improve their marketing strategies and conversion funnel. This not only ensures customer satisfaction but also helps maintain brand equity.

Machine learning techniques have been applied in diverse fields. Take a look at the few areas where machine learning is helping industries become more powerful.

1. Data Mining

Data mining is a process used to discover usable data from significant data sets. It basically implies analyzing data patterns from a large set of data. Machine learning algorithms help identify all the possible connections within data sets. Let’s understand with an example of a travel application. For companies, it’s impossible to analyze the possible variations and customer behavior patterns following a manual approach. So, what they do is acquire the entire user data such as their gender, age, how often they use the app, how often do they travel, and then execute machine learning algorithms to achieve valuable insights about the end-users.

2. Finance

The finance sector has also benefited from machine learning as it enables businesses to cut-down or optimize costs, scale up their services and enhance customer experience. One of the most common applications of ML in Finance is Process Automation that eradicates the manual work and automates repetitive tasks, leading to enhanced productivity. The implementation of ML in Finance apps help assess a user’s previous transactions, schedule payments, and offer deals based on their past usage or transaction history. Robo advisors are a great example that offer financial advice or help in investment management with minimal human intervention.

3. Healthcare

Machine learning technology has transformed the healthcare industry. The value of ML in the healthcare sector lies in its ability to process and analyze massive datasets that are beyond human capability. The analysis provides clinical insights that help medical professionals to better plan and provide healthcare. Today, there are also many mHealth apps equipped with machine learning that help users track their health and aid them with the necessary solutions.

Fitness tracking apps that analyze users’ daily activities such as how many steps they walk, how many calories they burn, etc. are doing exceptionally well in providing a better lifestyle.

With machine learning continuously evolving, the next-gen mobile apps will be even more powerful and user-centric. The technology has already been put into use and many more app development companies are leaning towards machine learning to reap its benefits. It not only helps customize apps as per the users’ needs but provides a fast, efficient, and secure environment.

Planning to launch your mobile app with integrated machine learning techniques? Dew Solutions can do the needful for you. We have a team of experts who possess prowess in providing intelligent solutions that boost your operational efficiency and business growth.