Top 15 Machine Learning Use Cases in 2024
To get started in your machine learning career, check out our top machine learning use cases across finance, healthcare, marketing, cybersecurity, and retail.
The Machine Learning market is anticipated to be worth $30.6 Billion in 2024. The world is increasingly driven by the Internet of Things (IoT) and Artificially Intelligent (AI) solutions. Machine Learning plays a vital role in the design and development of such solutions. Machine learning is everywhere. We live in an era led by machine learning applications, be it the Voice Assistants on our Smartphones, the Face Unlock feature, the surge pricing on the ride-hailing apps, email filtering, and many more. There could be several machine learning applications that you could be using in your day-to-day life without even knowing about them.
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Top Use Cases for Machine Learning in 2024
Here we will share top machine learning use cases in small businesses and medium and large-scale organizations spread across five sectors: finance, cybersecurity, marketing, healthcare, and retail.
Machine Learning Use Cases in Finance
Fraud Detection for Secure Transactions
According to a study, banks and other financial organizations spend $2.92 against every $1 lost in fraud as the recovery cost. Machine Learning has a wide range of use cases and applications in this area. The machine learning techniques are applicable in enhancing the security of the transactions by detecting the possibilities of fraud in advance. Credit card fraud detection, for instance, is a proven solution to improve transactional and financial security. Deep learning solutions using Python or R programming language can predict fraudulent behavior. These solutions work in real-time to constantly check on the possibility of fraud and generate alerts accordingly. Classification algorithms can effectively label the events as fraudulent or suspected to eliminate the chances of fraud. CitiBank uses Feezai’s anomaly detection system for fraud detection and risk management. The AI and Machine learning-based outlier detection system at CitiBank is in use in over 90 countries. It has helped Citibank better control and monitors the payments while improving the security levels at all times.
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Algorithmic Trading – Sentiment Analysis
Stock market variations depend on several factors, with the sentiments of people being one of the crucial factors for stock price prediction. Organizations worldwide are using machine learning techniques and models to conduct sentiment analysis for stock market price prediction. Various data sources, such as social media, provide data for performing sentiment analysis. The application of natural Language Processing, NLP, along with classification and clustering algorithms, can then classify a stock into three categories as negative, positive, or neutral.
Kavout, a stock trading firm in Seattle, uses its K Score model for sentiment analysis, price prediction, and recommendations. It is continually achieving better model portfolios as a result.
Financial Advisory and Portfolio Management
Several budget management applications are now available in the market, and these have machine learning-based functionalities. Eva Money by Fintel Labs is one such innovative application for iOS and Android platforms. These applications use machine learning algorithms to enable the customers to keep track of their expenses, determine the spending patterns, provide recommendations on better savings, and likewise. Robo-advisors is one of the latest trends for this machine learning use case. These are not the robots but the machine learning algorithms that customize the financial portfolio according to income, risk tolerance, and preferences. ML algorithms also provide recommendations on better trading, investments, saving schemes, etc.
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Cybersecurity Machine Learning Use Cases
Email Monitoring
Emails are now one of the most common tools for personal and professional communications. These are also the primary platforms to give shape to malware or other cybersecurity attacks. Phishing emails, for instance, extract sensitive information by tricking the end-users. Machine learning techniques can be instrumental in the detection of cybersecurity attacks by conducting real-time email monitoring. Natural Language Processing is one of the technologies to analyze email content and determine the possibility of phishing attempts. Anomaly detection using machine learning technologies is also effective in performing email monitoring.
One of the real-world examples is Tessian, a software organization in London. It uses ML-based email monitoring software to prevent phishing attacks, information breaches, and malware attacks. The software combines NLP and anomaly detection to keep track of the cybersecurity issues arising through the mails.
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View All ProjectsMachine Learning Against Bots
Bots contribute to approximately 25% of the overall web traffic. Most of these bots are malicious and can cause cybersecurity attacks, such as data breaches, malware attacks, or other threats. Bots can also take control of the application users and perform malicious activities. It is no longer possible to use traditional security techniques to deal with the bots.
Machine Learning algorithms can be effective in fighting against bots. Bot Twitter accounts, for instance, are problematic as they spread fake information and promote misinformation. Machine learning-based Twitter Bot identification systems use supervised machine learning techniques to identify and classify good and bad bots. Bot detection using machine learning technologies uses numerous factors, such as temporal patterns, message variability, response rate, etc.
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Detecting Drive-By Download Attack using Machine Learning
Malicious entities usually exploit browser vulnerabilities. The loopholes in browser plugins or similar vulnerabilities are helpful for the attackers to redirect the users to a malicious website and download the malware. These attacks are drive-by download attacks, and the malware then executes and spreads on the user’s machine. Convolutional Neural Networks, CNN can be effective in controlling such forms of cybersecurity attacks. One such model is Event De-noising CNN, EDCNN. This machine learning model can bring down the adverse impacts of benign URLs.
Marketing Use Cases for Machine Learning
Customer Journey Optimization
Machine Learning techniques are effective in marketing, and one such use case of machine learning in marketing is customer journey optimization. The primary idea of the process is to optimize the customer acquisition cost to a specific conversion point. The top-down approach is one of the popular approaches in use. It considers customer objectives, such as purchase patterns, pricing, comparison with the business, and maps the marketing touchpoints with the customer objectives. This approach, however, does not lead to significant revenue generation due to the absence of data insights.
Data-driven approaches are now more popular in conducting customer journey optimizations. These are the bottom-up approaches and extensively use machine algorithms and techniques. ML algorithms determine all the customer paths and provide a score to each of these paths. This approach considers the customer acquisition costs and customer lifetime value as the factors.
Reinforcement machine learning is one of the techniques to predict/forecast the next touchpoint to enhance the possibility of a specific outcome. Machine learning algorithms can determine the real-time points of interest for the customer journey to develop realistic and data-driven recommendations. This can significantly bring down the costs. Amazon uses this technique to improve customer acquisition and retention strategies.
Curating More Precise Content
Content development and curation can be an extremely time-consuming process. It is, however, an essential component in the current digital marketing era. Machine learning curation tools make these tasks easier for the marketing teams. Curata and Vestorly, for example, are the two machine learning tools for content curation. These tools extract the articles and content from the web sources, such as blogs, social media platforms, etc., and customize the content as per the customer's likings and preferences. Deep learning, natural language processing, and clustering are some of the ML techniques in use. These machine learning tools effectively annotate and organize the content for better customer engagement. A self-learning recommendation engine is one of the features offered by these tools to suggest content to the customers. Many organizations like Zendesk and JP Morgan use these content curation tools to impact their target audience better. These machine learning tools can also have significant improvements in the ROI. Vestorly, for instance, states that ML-based content curation tools can lead to up to a 300% increase in customer engagement levels.
Customer Experience
The magic of machine learning is used to drive customization, understand customers, streamline processes and create a memorable experience. Machine learning can assist in the enhancement of the customer experience in several ways. Communication is one of the keys to achieving marketing goals and objectives. Chatbots using machine learning and AI technologies can lead to higher customer experience levels. Marketing and customer service using chatbots provide the customer with 24x7 availability. In a survey, 77% of respondents preferred chats to get clarification on the queries around a particular product or service. Chatbots contribute to maintaining non-stop and direct communication with the customers.
Machine learning recommender systems further add to the customer experience. It is an effective way to customize the customer experience to achieve better retention and engagement. The tool has an API for training along with the predictions for better results.
Healthcare Use Cases Machine Learning
Skin Cancer Diagnosis
Convolutional Neural Network algorithms are extensively used in the healthcare sector to recognize and classify images. Healthcare is one field that has no margin of error. It is essential for a system or a technology to provide high levels of accuracy and validity in the results. CNN’s are effective in skin cancer detection with high accuracy rates of up to 95% using TensorFlow. Scikit-learn and Keras are other machine learning tools helpful in diagnosing and detecting skin cancer using the CNN technique. Manual efforts and processes in the same method can have a maximum accuracy of 85%.
These ML models use hundreds and thousands of images of benign and malignant skin lesions to provide the outcomes. GitHub has one such open-source project that uses the CNN technique to diagnose skin cancer. Similarly, you can apply various machine learning and deep learning techniques to predict and diagnose other medical conditions, such as Alzheimer’s, Diabetes, etc.
Covid-19 Mortality Risk Predictor
The entire world saw the outbreak of the Covid-19 pandemic in early 2020. Many countries are currently in the third wave of the pandemic with a continuous rise in infections.
Machine Learning techniques can be significantly helpful in pandemic management. Covid-19 mortality risk predictor is one such machine learning use case in healthcare. Timely prediction of patient mortality risk can bring down mortality with effective resource allocation and treatment.
Support Vector Machines, SVMs are machine learning algorithms that can be used for predictive modeling leveraging invasive laboratory and noninvasive clinical information of the patients. Non-invasive features, such as blood oxygen levels, patient age, previous medical conditions, etc., can be fed to the machine learning models to yield accurate predictions. The amalgamation of such ML techniques with IoT solutions like wearable devices can further assist in developing power frameworks. These can be effective in Covid-19 management and patient triage.
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Natural Language Processing for Administrative Tasks
Physician burnout is a common issue in medical organizations due to the excess workload on physicians. The condition became worse with the outbreak of the Covid-19 pandemic. A large number of physicians consider offloading administrative tasks as an effective solution to such problems. It can reduce the physicians’ workload and improve the quality of care as physicians concentrate better on the patient's health. Electronic Health Records, EHR organization, and management are critical administrative tasks in the healthcare sector. It can be made easy with the NLP tools. These tools can automatically categorize the words and phrases to include notes in the EHRs at the patient visit. The tools can also generate visual charts and graphs for physicians to understand the patient’s health better.
Retail or eCommerce Machine Learning Use Cases
Recommendation Engines
The retail sector has massive competition with the rise in the number of retail e-commerce establishments. The demand for e-commerce is at an all-time high. Recommendation engines using machine learning, data science, and AI technologies can provide retail firms with a competitive edge. It can simultaneously analyze the online activities of millions of customers in real-time to provide product/service/price recommendations.
These engines use machine learning and deep learning techniques and leverage them on the large volumes of data collated by these eCommerce giants. It can quickly determine the user's likings and preferences based on the time spent on specific pages, the number of clicks, etc. Customers see personalized recommendations as per data-driven analytics.
Many e-commerce chains, such as Amazon, eBay, etc., use Python-based ML recommendation engines for effective interaction between the functions. It leads to benefits for the e-commerce firms and the customers as they can easily access the products and services according to their choices.
Dynamic Pricing
Machine Learning techniques can be effective in dynamic pricing and can play an essential role in improving revenues and returns. Supervised ML techniques can learn new patterns as per the input data. These algorithms can then continuously update the results as per the trends. Online retailers use ML algorithms and techniques to determine the dynamic pricing of a product or a service. These techniques make it possible to map the best price for each product. The idea behind the process is to enhance sales while optimizing the inventory at the same time. Organizations can put up real-time discounts to engage the customers and maintain an edge in the market.
Amazon is one of the leading players in the e-commerce industry. It uses an ML-based dynamic pricing model as the product prices update at a gap of 10 minutes. This is 50 times more than the major competitors, such as Walmart or Best Buy. It provides Amazon with at least a 25% increase in profits.
Demand Forecasting & Stocking
It is significant for e-commerce and other retail stores to maintain a perfect balance between demand and inventory. Procurement of products higher than the market demand can result in huge losses if the products expire or damage with time. Similarly, running out of stock at the time of demand can cause negative impacts on the customer experience and brand reputation.
Machine Learning techniques can be effective in demand forecasting and stocking. Regression and time series techniques can help predict the expected sales for a specific time frame. ARIMA and exponential smoothing are the two effective time series models used extensively in retail. The former can make accurate predictions when making short-term forecasts. while the latter combines components like error, trend, and seasonality for precise forecasts. The linear regression approach can predict the demand using data point estimates. The forecast error in this technique is usually low and falls below 10%.
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Machine learning has been successful so far, with several real-world machine learning use cases already in the application. These algorithms and models can provide several benefits to businesses and customers. The finance sector, for example, is currently suffering from the issues of fraud and cybersecurity attacks. The frequency is increasing with each passing day. Machine learning can assist in the classification and identification of fraudulent behavior for timely detection. Similarly, these can effectively prevent and detect malware attacks, phishing attacks, information breaches, and other cybersecurity attacks. Marketing applications and use cases of Machine Learning are endless. Machine learning with Big Data and other AI technologies can make digital marketing activities seamless and easier to execute. The healthcare sector can primarily benefit from ML techniques with intelligent diagnosis and administrative management. ML and AI techniques have been effective in pandemic control and management too. Similarly, e-commerce and retail are sectors with numerous possibilities and scope for ML techniques.
These are only some of the examples of machine learning use cases across various industries. With ongoing advancements and innovation, there are a number of ways machine Learning will provide benefits to the providers and the end-users of the technology. Want to learn how to implement these machine learning use cases in real-time, explore ProjectPro’s solved end-to-end data science and machine learning projects to get hands-on experience deploying machine learning models into production.
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