Science of Patterns – The Science of Patterns is an interesting subject that examines and tries to understand how people behave. It uses machine learning to make accurate guesses and suggestions. The act of predicting how people will behave has always been interesting, even from long ago when people relied on their gut feelings.

In the world we live in now, people like marketers, politicians, and trial lawyers are able to predict what they need to do in order to be successful in their jobs. In the blog post, we will look at what happens when we try to merge human behavior, recommendation engines, and machine learning algorithms trained on past data.

Science of Patterns – Introduction

In the world of FinTech, using artificial intelligence has the potential to greatly improve and simplify processes. Machine learning is now able to perform advanced tasks that require thinking and understanding. A deeper level of machine learning, i.e., deep learning, makes predictions better by using many layers of artificial neural networks that help to make predictions more accurate and complex.

Exploratory Data Analysis

EDA or Exploratory data analysis is now helping fintech to understand the dataset of consumer transactions and uncover patterns, trends, and relationships. This fusion presents a plethora of opportunities for aspiring young entrepreneurs and serves as a catalyst for economic growth.

The future of Fintech and its technology lies in its transformative potential, influencing human interactions, knowledge acquisition, and even subconscious aspects. Techniques such as descriptive statistics, data visualization, and correlation analysis are now employed in Fintech to identify key variables and their potential influence on consumer behavior. It helps to form hypotheses and guides subsequent analysis steps.

The global perspective on mobile money is already triggering major shifts in traditional monetary services, driven by the widespread adoption of innovative technologies like NFC, QR codes, and facial recognition. In tackling intricate tasks like image recognition, neural networks stand unparalleled, showcasing their indispensable role in the ever-evolving technological landscape.

Machine Learning Role

The integration of AI, ML, EDA, and Physics is causing significant disruptions in the field of Mobile Money and Payment Intelligence (PI) Systems, shaping the underlying technologies to perform at its best.

Machine learning playing a vital role in decoding human behavior, Supervised learning algorithms, such as classification and regression, are being used to predict specific behaviors or outcomes based on labeled training data. Unsupervised learning algorithms, such as clustering and dimensionality reduction, enable the identification of hidden patterns and segments within the data.

AI techniques like natural language processing (NLP) are applied to analyze textual data, while computer vision algorithms are employed for image and video analysis.

Human Interaction – Science of Patterns

However, just like our brains, it takes a significant amount of time and computer resources to train machine learning models. Machine learning algorithms can be grouped into different types: linear models, tree-based models, and neural networks. All of these methods need a lot of power to work properly. Just like our brains need a lot of energy to function properly, these models also require a lot of power to work effectively.

The online shopping industry often analyzes data to offer helpful suggestions and make decisions. Online business that use financial technology (FinTech) are at the forefront of improving the e-commerce industry, even during periods of low activity. Recommendation engines use advanced technology like machine learning and artificial intelligence to analyze data and predict what will happen in the future.

Finding the best advice, choosing how to use money, and giving a pleasant improved experience are crucial tasks to complete. Artificial intelligence, when combined with other types of data intelligence, is already driving industry growth and innovation in exciting ways.

The Fusion: Science of Patterns, ML and Recommendation Engines

The science of patterns, when combined with machine learning and recommendation engines, offers a remarkable capability to predict human behavior. By analyzing vast amounts of data, these technologies can uncover hidden patterns and trends that guide human actions and decisions. From personalized product recommendations to targeted marketing campaigns, businesses can leverage these insights to understand and anticipate customer behavior.

How to recommend how much to spend, on what to spend, how to give the best augmented and concise experience to customers. Shopping, of course, is not the only industry to leverage recent advances in machine learning. The list of companies and industries is growing by the day in addition to the various applications of machine learning. Common applications of machine learning in today’s technology include voice recognition, fraud detection, email spam filtering, text processing, search recommendations, video analysis, etc.

How FinTech Intelligence (AI+FinTech = FI) will become better with machine learning. Artificial Intelligence is a field that includes everything that is associated with the data (cleansing, preparation, analysis and many more), Learning processes to describe, diagnose, predict and prescribe with use of AI subfields like Machine Learning, Deep earning and Neural networks. Machine learning allows software applications for making near accurate results based upon quality data. Some of the roles machine learning is playing in the FinTech domain (Not limited to though)

  1. Bringing behavioural biometrics to boost security in FinTech
  2. Deductive, Reasoning, Problem Solving with ML for FinTech
  3. Help and ease for bill payments/collections in an automated way
  4. Big data helps to make a strategy for the future and understand user behaviours.
  5. Secure Electronic Transaction enables interoperability between applications across diverse platforms and operating systems.

Fusion Powering The Businesses

At the heart of predicting human behavior lies the ability to identify patterns. Human behavior is influenced by a multitude of factors, including individual preferences, societal norms, and personal experiences. By analyzing large datasets, machine learning algorithms can identify correlations and patterns that might not be obvious to human observers. These algorithms can process vast amounts of information, recognize complex relationships, and generate predictions based on probabilistic models.

By harnessing the power of machine learning algorithms and recommendation engines, organizations can optimize their strategies, enhance customer experiences, and drive successful outcomes. The science of patterns opens up a new realm of possibilities, where businesses can navigate human behavior with precision and achieve remarkable results.

Recommendation Engines Role

Recommendation engines playing a significant role in decoding human behavior by providing personalized suggestions and predictions. Collaborative filtering techniques, content-based filtering, and hybrid approaches are being employed to power up the recommendation systems. These engines leveraging data on past behavior, preferences, and similarities among users to generate tailored recommendations, influencing human behavior in various domains such as e-commerce, entertainment, and content consumption.

It uses data-driven algorithms to provide personalized suggestions and recommendations to users. By examining past user behavior, such as purchases, clicks, and ratings, recommendation engines predicts future preferences and offer relevant content or products. This enhances user experiences, increases engagement, and drives customer satisfaction.

In the e-commerce industry, prediction algorithms and recommendation engines have become ubiquitous. Online retailers leverage these technologies to personalize product recommendations, improve customer engagement, and increase sales. By analyzing a customer’s browsing history, purchase patterns, and demographic information, recommendation engines can offer tailored suggestions, leading to higher conversion rates and customer loyalty.

Harnessing the science of patterns – Benefits

One of the key advantages of harnessing the science of patterns and utilizing machine learning algorithms is the ability to make near accurate predictions at scale. Traditional methods of understanding human behavior, such as surveys or focus groups, have their limitations. They can be time-consuming, costly, and often provide limited insights. Machine learning, on the other hand, analyze massive datasets in real-time, enable businesses to adapt strategies dynamically and respond to changing customer behaviors instantly.

In the entertainment industry, platforms like Netflix and Spotify leverage these technologies to curate personalized content recommendations, enhancing user satisfaction and retention. Social media platforms employ algorithms to predict user interests and behaviors, delivering targeted advertisements and content. This not only benefits businesses but also enhances user experiences by providing relevant and engaging content.

Predicting human behavior is not limited to commercial applications. In healthcare, machine learning algorithms can analyze patient data to predict disease outcomes, identify potential health risks, and personalize treatment plans. By analyzing a patient’s medical history, genetic information, and lifestyle factors, algorithms can assist healthcare professionals in making informed decisions. This way its helpful for improving patient outcomes, and optimizing resource allocation.

Ethical Considerations and Privacy

In todays time of PDA (personal data protection act) laws across the globe. It is super essential to recognize the ethical considerations surrounding the use of predictive technologies.

  • Privacy and data security are critical concerns.
  • Collection and analysis of personal data raise questions about consent and data protection.
  • Transparency and accountability in the use of predictive algorithms are crucial to building trust and ensuring ethical practices.

As with any data-driven analysis, ethical considerations and privacy are of utmost importance when decoding human behavior. Data anonymization techniques are employed to protect personal information and comply with privacy regulations. Policy like below should be enforced at every level

  • Transparent and responsible data usage practices, including informed consent and data governance frameworks

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around data models or the importance of quality data and training data. In the next upcoming post will talk about implementation, usage and practice experience for markets.

Books + Other readings Referred

  • Research through open internet, news portals, white papers and live conferences & lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about AI, Machine Learning, FinTech, Data Science or Big Data Analytics? Leave a question in a comment section or ask via email. Will try best to answer it.


Conclusion – the science of patterns, combined with machine learning and recommendation engines, presents a powerful tool for predicting human behavior. By analyzing vast amounts of data, machine learning algorithms uncover hidden patterns and make accurate predictions about our future actions. Recommendation engines, in particular, play a vital role in delivering personalized suggestions and enhancing user experiences. From e-commerce to healthcare, the applications of these technologies are wide-ranging and impactful. However, it is essential to navigate the ethical considerations and ensure responsible and transparent

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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