Cognitive Ergonomics – The significance of cognitive ergonomics cannot be overlooked in the FinTech sector. Before we begin our conversation, it is crucial to clarify any confusion about the topic. As of now, my pursuit entails scrutinizing the patterns of FinTech advancements after the year 2018. The past year was abundant with notable advancements in the fields of financial technology (FinTech) and artificial intelligence (AI). The rise of fintech can mostly be attributed to the outdated nature of current global banking systems. 

AI in FinTech – FinTech Intelligence

FinTech intelligence, or FinTech with new flavours like AI and its commandos, i.e., ML, DL, ANN, etc., is not new now. This new flavour of FinTech is working like magic. Giving it bluer oceans by focusing on new markets where conditions are somewhat favourable. Beyond the year 2018, FinTech powered by the new electricity of the corporate world, i.e., artificial intelligence, will dominate this space.

To put it simply, blending financial technology (FinTech) with the Internet of Things (IoT), artificial intelligence (AI), and its correlated subcategories is a method to maintain FinTech’s superiority in comparison to conventional banking establishments.

It is easier for people to use mobile payments and web-based financial services since phones are more pervasive and convenient to access than traditional financial instruments.

AI is radically transforming the digital economy through fintech. How it’s doing that we will answer below by taking help through “Cognitive Ergonomics”. Financial engineers are combining data from mobile phones, social networks, and almost every step we take in real life to transform FinTech into new industries. We are assuming AI is more human than it is. Areas like cybersecurity (the biggest ticket item for the year 2018), which we will talk about in our next blog post.

Cognitive Ergonomics Environment

AI is now an integral, critical, and most important component of our personal devices, applications, and transport systems, i.e., self-driving cars. The user experience is now more focused on the extreme analytical framework. In the case of payments or FinTech, this gets focused on where users spend, how much they spend, when they spend, etc. Cognitive technologies, such as artificial intelligence and machine learning, are already influencing our financial planning and investing experiences too much.

Cognitive Ergonomics

Cognitive Ergonomics is becoming an important field for data scientists to understand and implement.

There is very strong proof from the past on how this is related to cognitive ergonomics. The fake tweet came out at midday on April 23rd, 2013, from the official associated press Twitter account. The tweet was related to some fake news about the attack on the White House. The target was never the president or the White House. The target was the financial markets.

In a fraction of a second, a cutting-edge computer with advanced high-frequency trading algorithms identified relevant keywords from a reliable source and promptly responded, assuming that a verified terrorist attack had occurred despite no such incident taking place. Within a span of approximately three minutes, the market incurred a hefty expense of $136 billion. It is highly unlikely for a person to plan and execute an attack of this scale.

Cognitive  & AI Influence on FinTech

Creating a comprehensive customer experience of 720 degrees, which is a key objective for FinTech, entails incorporating cognitive touch into your FinTech offerings. Staying within the blue ocean realm of the business world may not be compelling enough with the availability of a 360-degree experience. Providing an environment and ambience for your fintech services without being prompted by customers is a superior concept.

Understanding context in written words was the biggest break through of AI in this direction. Now implementing and using the same in FinTech is the next challenge.

When you are on the Google search engine, Google knows what you are looking for as soon as you type the first character on the page (80%–90% accurate to predict). How about telling your customer his last five related transactions as soon as the customer is at a location and in the middle of initiating payment? The payment the customer is trying to make and the historical information combined can create some sort of cognitive environment with recommendations that provide a 720-degree view and experience.

One View – AI, ML, Blockchain & FinTech

To put all of them in the picture is not an easy job, though. We don’t have any successful models that have all these niche technologies in the photo frame. Lots of banks and FinTech are working to bring this amazing product, which is fully functional and working, to life.

Banking as a Service (BaaS) This needs time. This will have the security of blockchain, the intelligence of machines and algorithms, and the eyes of AI. To draw the rough sketch just to give one view, it might look like the one below.

Deep learning methods (especially recurrent neural networks) are helping time-series data processing and prediction for financial markets. Generative adversarial neural networks are becoming vital in fraud detection. These techniques for traditional feature extraction via intelligent trading decisions are supporting the FinTech system. The techniques used are applied to several technical indicators and expert rules to extract numerical features. An algorithmic trading framework with the use of deep convolutional neural networks is a good place to start for FinTech.

Points to Note:

All credits, if any, remain with the original contributor only. We have covered all the basics around the myth of mobile payments, its models, and the importance of quality services. In the next upcoming post, we will talk about implementation, usage, and practice experience for markets.

Books + Other readings Referred

  • Research through open internet, news portals, white papers and imparted knowledge via 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, Telecom billing/charging, Data Science or Big Data Analytics? Leave a question in a comment section or ask via email. Will try best to answer it.

Cognitive Ergonomics

Conclusion: To achieve the goals of a cognitive system, it must be able to select and coordinate information processing subsystems, locations in perceptual fields, and categories in memory. This won’t be impossible from 2018 onward. The year 2018 and onwards, i.e., the further we go into the future, the more cognitive and fascinating our experience will become through the applications and devices we will use.

AI will allow us to delve into each customer’s interaction sequences, needs, and level of influence within their social network. Along with their current life events to figure out their current needs and how to serve them better. This learning will come out immediately to provide the best experience and force customers to live under guidance.

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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.


  1. Peterson Jojo at

    Marvellous post …. please explain more on Cognitive Egronomics AI


  2. Kirk Mike at

    Good but could have been more derailed


  3. […] between them and the sample variance of each cluster. The best use for unsupervised is around exploratory analytics to understand the financial, business and operational drivers behind what […]


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