Product Recommendation Engines are common in the retail industry with online commerce sales booming due to recommendation engines guiding online customers across a myriad of options. Amazon had even reported that 35% of their sales solely came from recommendation engines. Recommendation engines have worked their way successfully through the e-commerce industry and proven their merit in the entertainment industry with Netflix adapting AI-based personalisation techniques. However, the spotlight has turned to banks to fine-tune their banking system with efficient and powerful recommendation engines.

51% of customers want powerful recommendation at traditional banks while 72% of millennials would be willing to look into non-financial entities like Amazon, Facebook, Google or Apple to satisfy their financial needs. Currently, the banking environment is extremely volatile. Why? With several non-financial entities coming up in the payments domain, each of these non-financial organisations has built a steady customer base and has powerful product recommendation engines. Hence, it is time for banks to get involved in a more personalized relationship with their customer.

Firstly, for powerful recommendation engines, banks need immense amounts of customer data. Banks however, have to abide by a lot of compliance and regulations in the modern age. The financial data, which banks hold, or attempt to hold, classifies itself on the most interesting end of the data spectrum and is of immense importance to customers and hackers. Hence, customers would use the greater rights like Right to Forget as stated in the GDPR laws governing the European Union for such classified data. Banks know this, which makes it extremely important for them to find the right balance between securing customer data and providing accurate and satisfying recommendations.

Rule Number 1: Collect your data from every nook and corner and ANALYZE IT

On an average, data professionals spend only 40% of their time analysing data. However, more than 91% of top-level executives agree that data takes the highest priority in hacking customer experiences.  Now, there are three major types of customer data, which when referenced, provides for a plethora of insightful suggestions.  These 3 data types – Reference Data, Transactional Data and Social Data – are filtered by data analysts.

Reference Data gives information into the customer’s profile and general preferences. These data points always form the base of any customer data lake used in creating a recommendation engine. Transactional Data consists of data, which pinpoints to any customer behaviours in the form of payments and transactions. These data points could include transactional and complaints history, different agendas recorded from the customers’ visit to the bank and so on. Finally, Social Data adds flair to the entire data lake by giving a taste of the customers’ mood by collecting data from their social media engagements and activities online.

Rule Number 2: Governance and Integrity of your Data

89% of CIOs agree that bad data has been hurting their ability to provide enriched customer experiences. During the data ingestion process, it is highly likely that the data obtained would seem corrupted, incomplete and present with several outliers. Corrupted data, when fed into your machine-learning algorithm, affects the recommendation engine and provides a completely different and incorrect view of your customer.

The lack of measures to ensure data integrity would end up being disastrous for any bank, especially in the current highly competitive banking ecosystem.  Besides this, there are many data dependencies within a database. This means that data completeness is crucial to make sense of the data. Each of these tasks requires people with adequate and high levels of technical knowledge, which is often not the case.

Where does all this data usage lead? – Insights

Product Recommendation Engines depend on the self-learning ability of machine learning algorithms. With sufficient and massive chunks of data on the customer, the process of deriving insights becomes easier.

Insightful Cases

A forecast model of the customer’s profitability through wealth management is drawn and the decision making process of the next best action takes place. Segmentation of customers into different classes at banks based on the loyalty quotient and products also takes place, which helps in better customer retention. Segregation of customers also takes place according to their credit risk score. This could help banks to segregate their customers accordingly and could recommend low-risk products for those customers.

The Resolution

Product Recommendation Engines if tuned correctly with massive loads of data, and a technically adequate human workforce can enable better turnovers. Since only 13% of organisations – a few global banks amongst them – have a matured data analytics strategy, it is the best time to take advantage of the empty field and rise above the rest of the competition. Currently, the target for banking products and services are targeted for a larger cluster of customers segmented broadly. Each cluster fails to adequately resonate each of its customer’s financial needs. With a vibrant, rich financial ecosystem and several competitive players in the background, it is important for banks to make the move right now – from precautionary insights to predictive and prescriptive insights.

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