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Financial Product Recommendation

The world runs on finance, and in the eclectic economic framework of the 21st century, it is recommended that every person choose financial products wisely and according to their means. However, with a vast number of available choices, it can become very difficult to choose which financial product would be the right fit for you.

As with everything, we at SDS believe that technology can be the savior of humanity if used wisely. AI systems that learn unlearn, and relearn can literally shape and change the way we interact with the world.

Keeping the above view in mind, we have created the Financial Product Recommendation system for our client Citigroup, which is the 4th largest bank in the world. Allowing the customers to choose from a plethora of financial products, our recommendation system utilizes high-level Artificial Intelligence to make sure that the customers get the products that they deserve.

So, how did this system come to be, and what problems does it actually solve? We are going to take a deep look at all of this and more in the following article.

So, sit back, relax, and let’s get on with the journey into a smart financial future.

But before all of that, what exactly do we mean by Financial Products?

What Are Financial Products?

In short, financial products are monetary tools that are designed to provide customers with a profitable avenue for investment. These financial products may be in the form of investments and securities that work in short as well as long term; using these products, the investors can grow their wealth. Some such investments also carry with them tax benefits.

Where Does The AI Come In?

The task of choosing the right financial product can be a complex one requiring expert domain knowledge. More often than not, the investors themselves find it difficult to settle on the right monetary instruments that can provide them with the returns they need. For this reason, they may need to consult with a financial advisor who is an expert at recommending financial products.

Banks still looking to catch the bus must engage the right development partners and data analysts to start analyzing how these machine learning tools can be implemented to their information database and marketing operations. Canada-based TD Bank has effectively capitalized on its data, including transaction records and customer service interaction for improved insights and implementing machine-learning tools.[4] The organization has employed several simple open-source tools to build a foundation for gathering, sorting, and analyzing the required data.

Financial experts have extensive experience in dealing with financial products; and are therefore much more qualified to recommend them. But at the end of the day, they are only human.

And humans make mistakes.

Enter our Financial Product Recommendation System. Using the latest advents in deep learning methodology, our system can assess a mind-boggling array of factors like investors' current financial status, future goals, and requirements as well as the market trends and conditions to suggest services that are the right fit for the right person.

AI systems have several benefits over traditional financial product recommendation systems. For starters, they are not constrained by the human disadvantages of tiring and mistaking, so they can be up and running at all times. Further, they are able to analyze and collate a mind-boggling array of factors that can reveal new insights about the direction in which the investors should lean.

Now that we have an understanding of how AI systems can help in financial product recommendation let us take a deep dive into the technologies that have helped us develop our financial recommendation system.

How It All Works Together AI systems for financial product recommendation primarily work on the basis of customer behavior analysis; In our case, we used Tensorflows along with deep and wide neural networks to understand consumer behavior regarding credit card products.

A brief description of Tensorflows and Neural networks is given below.


Tensorflow is a free and open-source dataflow and differentiable programming library that has been developed by the Google Brain team. The Tensorflow library can be effectively used in creating machine learning applications. This is the technology behind the creation of the deep neural network that powers the financial product recommendation system.

Neural Networks

These are the functional building blocks of AI infrastructure. Closely inspired by the human brain, neural networks can route inputs and outputs to create patterns of learning and understanding that cannot be usually achieved through traditional programming. Such networks form the basis of the financial product recommendation system. By analyzing market indicators and customer trends and preferences, the system is able to suggest financial products for use by the investors with marked accuracy and precision.

Final Words

AI has already arrived, albeit in a quiet but steady way, and it's here to stay. In the future, when you look for investment options, you can be sure to expect to talk to virtual recommendation assistants as opposed to a real-world financial advisor. Such systems can improve the level of service offered and also recommend products with far greater effectiveness than comparable human counterparts. So get ready for a new world where AI is the watchword, and digital design the way forward.

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