Banking AI

Why Modern Banks Must Make the Most of Machine Learning in Recommendation Engines for Better Consumer Outreach

The banks of the 21st century need to rethink the way the industry has been doing business traditionally to thrive. In the age of tech giants, the banks cannot afford to not keep pace with the marketing practices of large tech corporations, especially with entities such as Amazon and Google providing alternative financing options to consumers. In other words, a bank sitting on its traditional operations does not stand a chance to compete in the modern market. It’s no wonder that the AI-based recommendation engines have become a relevant trend for banks and financial institutions.

These recommenders predict consumer choices based on existing buying patterns and portfolio data if they will be interested in a new product such as a credit card, a loan, or investment securities. This employs the recommender space better for delivering the most relevant product recommendations to the consumer and results in better engagement and a better rate of leads turning into sales.

These recommenders are formed on the basis of different techniques, including collaborative filtering, content filtering, and hybrid filtering. While collaborative filtering depends on the patterns of a purchase made by a similar user or recommending a similar item to a consumer, who has made a purchase, with the latter being the most common and cost-effective recommender type. Content filtering goes deeper and understands the description of the item before recommending it to a user with similar interests based on keywords. Many sophisticated non-banking platforms such as Netflix go with a hybrid system as well, which are found to be more effective.[1]

However, it is important to understand that the machine learning recommenders for banks and investment institutions work differently from your other online shopping platforms. The recommendation engines need to intelligently respond to a consumer’s credit profile and current portfolio to offer them suitable investment options as per their investment goals. While the mix of factors that financial AI recommendation engines need to consider are more complex, you could argue that their utility for investing consumers could be even greater.

Personal relationship managers are expensive, but even they cannot constantly keep track of the investment needs of every single client. Furthermore, an intelligent recommendation system can help an investor navigate through investment options far more easily than with the need to converse with a portfolio manager. This ends up in a more personalized and desirable experience for a banking consumer who can find useful advice with these recommendations. According to Brett King, the CEO of the mobile banking app Moven, consumer spending advice has tremendous potential in banking. "For me, the advice is the next big disruption. For instance, in banking, you do need real-time advice. The ability of humans to provide that is poor, and, as humans, we're inconsistent, and we make mistakes. Artificial Intelligence will not."[2]

It’s no surprise that according to a 2018 survey by data science firm Narrative Science, 32% of executives from the financial industry confirmed using AI technologies such as predictive analytics and recommendation engines.[3]

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.

It sounds like a revolutionary solution, but in order to put together such a recommender algorithm, data is the magic word. Paradoxically, it is the word that will alarm most people when used in the context of banks and investment firms. Using financial data for developing machine learning recommenders could raise questions about the security and integrity of data. Modern banks are aware that they are playing with fire whenever they use consumer data for developing solutions. This is why banks need to be careful about engaging the right partners and developers who capitalize on this resource for developing a machine learning recommender so that the consumer data at their disposal is used securely for the desired results.

My team at SDS specializes in developing solutions such as machine learning responders for banking and other organizations, as well as other intelligent automation tools. We provide full-service consulting and code implementation for both businesses and executives and create tailored solutions by employing data science methodology. If you are a banking executive looking to implement a machine learning recommender to your operations securely, our team will deliver just the right solution to you.

You can learn more about our services by calling +1 (214) 518-9248 or visit the following website. Or simply drop an email at [email protected]

[1] Dataconomy, An Introduction to Recommendation Engines – March 13, 2015 –

[2] Peter Hinssen, An Interview with Brett King: Why real innovation will not come from within your own industry – Feb. 10, 2016 -

[3] Narrative Science, Research Report: The Rise of AI in Financial Services, 2018 -

[4] Forbes, Bernard Marr, The Amazing Ways TD Bank, Canada's Second-Largest Bank, Uses Big Data, AI & Machine Learning – Dec. 18, 2018 -

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