Machine learning (ML) is widely used as a predictive technology in fields such as transportation, finance, healthcare, advertising, travel, and several manufacturing industries across the globe. Machine learning and predictive analytics aid companies in making better decisions by anticipating what will happen.
ML and predictive analytics predict future outcomes through the analysis of current and past data. The two terms machine learning and predictive analytics are sometimes used interchangeably, and although related, they are two different disciplines.
Machine learning can be applied to various applications, while predictive analytics focuses on forecasting specific variables and scenarios. Combining predictive analytics with machine learning is a powerful way for financial companies to gain value from the massive amount of data generated and collected through business operations.
We will go through these two concepts and how they can be used to improve processes and be a foundation for a company’s underlying abilities.
Machine Learning and Predictive Analytics, in Brief
Machine learning is a subsection of artificial intelligence (AI) that creates computer algorithms designed to improve their accuracy as they process or “learn” from large data sets. Machine learning’s ability to learn using previous data and its adaptability with a wide array of applications makes it highly beneficial. Fraud and malware detection, spam filtering, and image analysis are a few of the many applications of machine learning by industry.
Predictive analytics uses tools and techniques to build predictive models for forecasting outcomes. Its methods include machine learning algorithms as well as statistical modeling, descriptive analytics, data mining, and advanced mathematics. Predictive analytics is an approach rather than a defined technology.
Predictive Analytics
Predictive Analytics is a type of advanced analysis building upon two earlier analytics types that were done through human coding, descriptive and diagnostic analytics. Companies use descriptive analytics to see, for example, how many items were sold yesterday or this week, while diagnostic analytics subdivides that data to determine why fewer items were sold this week than the week before.
Predictive analytics utilizes measurable variables in order to predict the behaviors of people or things, like buying habits of an individual customer, when a machine requires maintenance or a forecast of a store’s or company’s sales. Classical statistical techniques like linear and logistic regressions, and machine learning techniques such as neural networks, support vector machines, and decision trees are applied to predictive modeling.
The need for expert knowledge of these advanced techniques means that predictive analytics has been the domain of data scientists, analysts, and statisticians. This requirement is beginning to change as business intelligence vendors offer advanced AI capabilities and analytics in their platforms, resulting in the democratization of analysis by business users.
Strong business leadership is needed for the deployment of predictive analytics because the first step of a successful deployment is defining the business’ objectives and the project’s goal. The next priority is the identification of the correct data and analytical techniques needed to build a robust predictive model. Having high-quality data is necessary during the training, especially if the data sets are smaller.
Machine Learning
Artificial intelligence is the replication of human intelligence by computers. AI includes a broad range of diverse technologies beyond machine learning, including robotics, natural language processing, and computer vision. These wide-ranging technologies are all meant to replicate human actions.
Machine Learning is a software-based AI that becomes better at predicting without being programmed to do so. The program learns by detecting patterns in data sets. Machine learning algorithms are created to be versatile, allowing developers to make changes with parameter tuning.
Machine learning is the foundation for neural networks and deep learning, which are used to do such tasks as financial forecasting and the driving done by autonomous vehicles ML can increase the rate at which data is processed and analyzed.
By applying machine learning to predictive analytics applications, algorithms train using extensive data sets and perform complex analyses on several variables with only minor manual changes.
Machine learning and AI provide benefits that make them enterprise staples, and there is no longer debate over their value. In the past, their operationalization required a complicated transition, but the technology is now successfully implemented across multiple industries.
Predictive Analytics Versus Machine Learning
To recap, predictive analytics applies advanced mathematical techniques to discover patterns in current and historical data to predict future events, while machine learning is a tool that automates predictive modeling through training algorithms searching for patterns and behaviors in data while not receiving explicit instructions.
There are several key differences:
- Machine learning can be trained through supervised or unsupervised methods, and it is the foundation of several advanced technologies such as deep learning, computer vision, and autonomous vehicles.
- Predictive analytics is built on the fields of descriptive and diagnostic analytics, and it is a stepping stone to prescriptive analytics. This type of analytics provides guidance on contextual-specific next steps.
- Machine learning algorithms are designed to both evolve and improve their predicting abilities with their continued processing of more data, without being programmed by humans to do so.
- In predictive analytics, some of the models are run by data scientists manually.
- Machine learning works best when the algorithms are provided with very large data sets that have high-quality (clean) data. However, once a machine learning algorithm is trained using clean data, it can be applied to so-called “messy” data.
- Predictive analytics requires data that is accurate to design and build beneficial models.
Just as the value of machine learning and artificial intelligence in business has become widespread, their differences have lessened. As ML gains more widespread understanding and employment in business applications, it becomes a more integral feature in predictive analytics.
Use Cases
The successful application of machine learning and predictive analytics by enterprises is widespread. Here are a few examples:
- Banks and other financial service companies use prediction models for their risk management programs to identify and prevent fraud, help with underwriting decisions, calculate when customers are likely to default on loans, and when to offer increased credit lines.
- Marketing and retail organizations are using various prediction models to refine their strategies. Predictive analytics is being used to spot website user trends, hyper-personalize advertising, and target emails.
- Manufacturers, including airplane makers, are using prediction models to monitor machinery and equipment and identify when failures will happen.
- Healthcare organizations use prediction models to identify outbreaks and extrapolate outcomes beyond drug trials, new drug approvals, and the course of disease based on past data.
- Human resources can now identify the best candidates and predict when top employees might be preparing to quit using prediction models embedded into their information systems.
Challenges
While predictive analytics and ML techniques are becoming embedded in more “novice usable” software resulting in so-called “one-click” forecasting, enterprises will face the usual challenges associated with getting value out of their data. This starts with the data itself.
All types of data, including corporate data, are error-prone, inconsistent, and incomplete. Finding the correct data and preparing it for processing and forecasting is time-consuming. Expertise in deploying and interpreting predictive models is still scarce.
To assume that the one-click solution will be accurate is dangerous and must be tested. Moreover, software for predictive analytics is expensive, and so is the processing required to create effective models.
Finally, machine learning technologies continue to evolve rapidly, resulting in continuous scrutiny on how and when to upgrade to newer approaches.
Financial Applications
The global financial markets have experienced the profound impact of machine learning and predictive analytics on various aspects of digital pricing. From international financial organizations down to retail traders, digital pricing techniques are used to generate maximum profit and returns.
Moreover, when applied, predictive analytics and machine learning can improve trading strategies for all asset classes, including cryptocurrency and digital pricing markets. Similar pricing techniques are being applied for a sustainable future when conducting global business.
Finally, using ML and predictive analytics, organizations conduct faster and cheaper transfers, or exchange currencies more rapidly..
Closing Thoughts
The complementary nature of applying machine learning with predictive analytics makes the combination a powerful tool for forecasting in finance and several other fields. When trained with clean data and then applied in the correct fashion, the accuracy and speed of their abilities exceeds that of several humans combined.
The key to long-term success is to create the proper environment with defined goals and success metrics, using clean data from the beginning and then evaluating the application over time. As ML and predictive analytics applications broaden their reach, their acceptance will soon become commonplace.
Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.
The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, deltecbankstag.wpengine.com.
The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, deltecbankstag.wpengine.com.
The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.
