ML Use Cases For Enterprises
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Practical Machine Learning Use Cases Across Industries

16 Jun 2021

Machine learning has emerged as a frontrunner of business transformation, helping companies embrace digitalization at almost every touchpoint. It helps enhance efficiency, boost sales and improve customer relationships. Today, ML algorithms help companies make informed decisions with analytical models to uncover & discover meaningful insights and trends with minimal to no human intervention.

Evolving at a rapid pace, ML is gaining fresh momentum to help global businesses scale and optimize operations. And, industries like fintech, healthcare, logistics, and retail are some of the early adopters of this technology.

Through this article, we aim to present the elements of success stories of machine learning applications across domain verticals. It would also help you uncover opportunities to enhance and improve overall efficiency with potential solutions.

Machine Learning in Retail

Ml in Retail

In the last few years, there has been an increase in the online shopping trend. With learning from the recent pandemic the ability to predict & analyze buying behavior has become a necessity for retailers to survive. The need is for a solution that helps do just that and provides valuable insights that can translate into tangible outcomes like repeat purchasing and more.  With machine learning, retailers can now accurate references to offer a seamless shopping experience and improve the bottom line across channels.

Machine Learning Use Cases in Retail

Recommender System: It helps retailers and eCommerce stores to offer personalized shopping experiences with recommendations of relevant products to end customers. Based on large amounts of customer data around preferences, buying habits and more, the businesses can provide offers to elevate the shopping experience. It helps increase the conversion rates impacting revenue for the retail organization. Recommendation engines also help measure more factors like customer satisfaction and customer retention which has a direct impact on revenue and conversions.

Pricing Optimization: Pricing plays a crucial role in determining a business’s success and profitability. With an automated system, retailers can better assess demand, supply, competition, and other factors. The algorithm provides input based on the history of the competitor’s product pricing. It helps extract critical insights to support promotional campaigns and accurately predict how customers would react to the new pricing structure. With better insights at hand, retailers will be in a better position to make the right business decisions.

AI-Powered Chatbots & Assistants: Chatbots have long been helping transform customer experiences. With a human-like conversation with consumers, these bots address frequently asked questions & solve customer queries with quick and timely responses.

Machine Learning in Healthcare

ML in Healthcare

With a wide adoption in the healthcare industry, ML helps improve patient care and service outcomes. It comprises medical billing, new drug discoveries, disease diagnosis, development of clinical care guidelines, and automation of administrative tasks. While the industry has embraced innovation, there is certainly a brighter future to make full use of the technology’s potential to transform the medicine ecosystem.

Machine Learning Use Cases in Healthcare

Detection and Diagnosis: Artificial intelligence is becoming even more powerful in accurately detecting diseases such as Alzheimer’s, cancer, rheumatoid arthritis, hepatitis, Parkinson’s, and more. Deep learning algorithms are highly effective in learning about new illnesses and work on diagnosis with limited human interference. It provides predictive statistical models that help doctors save time for disease control.

Natural Language Processing (NLP) for Admin Tasks: With efficient managing of the systems & operations being the need of the hour for most healthcare providers, NLP helps simplify this process. The defined algorithms help in identifying the words and phrases that are processed in the EHR. Further, the tools also help doctors review charts in a more structured manner to understand a patient’s overall health. Instead of being buried under a pile of administrative tasks, doctors can focus on improving patient care.

Treatment Recommendation: Deep learning models can identify complex patterns in patients to derive accurate outcomes and treatment options in real-time. It is based on the patient’s health history, lifestyle patterns, and current medication. Physicians can review these recommendations, compare them with learnings and discuss options with patients. This data is shared across hospitals & healthcare centers to generate an ecosystem of precision medicine by deriving insights from various physicians who have treated multiple patients.

Machine Learning in FinTech

Ml in Fintech

FinTech companies even before the global health crisis were focusing on upgrading their business models by leveraging emerging technologies. And, AI & ML, are fueling this growth to offer better services to customers.

ML Use Cases in FinTech

Fraud Detection: To combat the increasing number of threats, financial institutions are leveraging ML algorithms and models to detect all types of possible fraud scenarios that include unauthorized transactions, loan applications and insurance claims. A real-time fraud detection and prevention system supports increasing operational efficiency and monitor patterns by providing recommendations in real-time to identify possible fraud activity across the payment cycle.

Credit Scoring: AI-based tools are widely used in assessing the creditworthiness of customers. Traditional rule-based methods are not as effective since they predominantly depend on the lender’s previous purchase history. With artificial intelligence, the credit scoring process is accelerated and is more accurate. It takes into account online buying & browsing patterns that aren’t otherwise available. Financial institutions can thus take advantage of more legitimate reviews, reduced evaluation costs and lesser default risks.

Portfolio Management: Big data opens up a world of opportunities for fund managers, making information even more readily available. This helps them objectively make decisions about the client’s wealth which a statistical learning model aid in by analyzing real-time data. One of the major benefits of using AI for portfolio management is the comprehensive market stimulations. Sifting through large amounts of data helps in predicting accurate customer investments and thereby, targeting them with the right products.

Machine Learning in Logistics

ML in Logistics

Like all other industries that have embedded machine learning in their operations, the logistics & SCM industry has followed suit. Combating disruption has long been a concern for the industry, especially with factors like volatile demand, evolving supply chain market dynamics and new ways of working. Machine learning helps streamline the entire supply chain management process right from procurement, warehousing, inventory management, shipping & customer service.

ML Use Cases in Logistics

Inventory Management: The proper storage and maintenance of inventory is an expensive affair. Supply chain professionals need to carefully plan their inventory as it directly impacts the cash flow and profit margins. ML can help in solving the challenges relating to over or under-stocking. It taps into large amounts of data to reveal insights like historic analysis, current market environment & seasonal trends to predict future growth.

Warehouse Management: For effective warehouse management, machine learning helps automate admin & mundane tasks and reduce manual work. Industry 4.0 ushered in an era of autonomous robots and vehicles which are widely used in warehouses. These assistants have built-in guides that help them pack/unpack packages, upload/offload boxes and transport parcels. Further, computer vision takes control over the conveyor belt to predict a possible blockage. NLP and OCR (Optical Character Recognition) automatically detect the delivery dates of packages and adjust their delivery statuses if required.

Logistics & Transportation: Machine learning helps identify the status of a package within the logistics cycle. For supply chain managers it becomes easy to track the location of goods at the time of transportation. Further, the sensors help monitor & capture data from natural conditions like temperature, humidity and vibration at the time of transport, which proves to be useful input for many organizations.

Closing Thoughts
The business applications of machine learning have opened a world of opportunities for many industries in a short time. As a driving force for technology transformation, it aims to not only garner better results but enhance the entire human experience in its entirety.

Rishabh Software, a data science services company helps organizations to develop, adopt and leverage custom AI-based solutions. Our data scientists work on building machine learning algorithms to ensure these next-gen systems meet changing customer needs, adjust to market changes & generate measurable results for your business.

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