What is the objective of the Banking Products Recommendation system?

A recommender system or engine’s objective is to create important suggestions to list clients or customers for things or items that may interest them.

For Example, Recommendations for songs on Gaana, videos on Youtube, or movies on Netflix, and books on Amazon or Flipkart, are real-world examples of recommendation systems.

Like the above examples, banks also recommend few products related to banks to their customers, such as loans, credit cards or debit cards, etc.

Why do we need a Banking Products Recommendation system?

In older processes, banks do not suggest or offer any loans, credit cards, etc. they only accept or reject loans after getting loan request documents from the customers. After some time, they started to offer loans by sending emails or messages to customers, but that did not consider any customer interest or need so that offering is not all time useful for bank customers. So the gap between customer expectation and bank offers or services increased. But this gap should decrease to improve service quality and also the revenue of banks.

Banks need to offer a variety of products to customers, such as

  1. savings accounts,
  2. checking accounts,
  3. investment products (e.g., fixed deposits, call deposits, treasury bills),
  4. loans (e.g., mortgage loan, student loan, lease financing, overdrafts, and others),
  5. digital products and services (such as debit cards, credit cards, international fund transfer services, mobile and online banking services, and others),
  6. wealth management solutions,
  7. currency exchange,
  8. private banking products for high net-worth individuals,
  9. corporate banking solutions, and so on.

Suppose banks offer or suggest all the above bank products to customers based on their interests. In that case, customers will be happy with Bank offers by utilizing them automatically, the growth of the banks will be increased.

How does an AI-based solution work in the Banking Products Recommendation system?

It is imperative for Banks to sell the right products to customers. Banking industries are starting to adopt recommender systems, or engine techniques to offer personalized recommendations to existing and prospective customers could have a tremendous impact on product sales, directly influencing turnover and income. And also, the gap between customer expectations and the bank’s personalized offering will be decreased.

Most of the Recommendation systems use explicit ratings which are given by users to express their opinion about an item (for example, rating the item on a scale 1- 5). This approach is very effective in other domains, such as e-commerce and entertainment platforms, where customers can give product ratings. There is no explicit rating system in the banking domain because banks generally do not ask customers to rate their products. Another issue is knowing what to recommend to a new customer who has not used any banking products.

We can implement solutions for these two issues in the Banking domain by using a hybrid approach to give strengths to collaborative filtering and demographic-based approaches to recommender systems usable in banking.

Complete process

Data extraction

For the recommender system for banking products, we can collect a dataset of customers’ financial investment information from specific banks. This data includes different types of information about the client and transactions used by him like

  • Execution date,
  • User data (client, branch and account identifiers)
  • Traded item data (type of asset, transaction currency, asset country, time to maturity, complexity, industrial sector, industrial group, industrial sub-group, rating, coupon type, trading channel, buy-sell type) etc.

Data annotation

We can build different types of recommender systems, such as by using customer feedback or anything else. In this banking product recommender system, we collect customer data from the database of particular or specific banks. The data of the database contains financial transactions about every customer.

Data processing

We should not feed raw data to any AI-Based models such as machine learning, deep learning, etc. we need to perform some preprocessing techniques on raw data which data collected from transactions of banking customers. This step involves data cleaning, transformations, identifying missing values, and also how they should be treated, etc. Not only cleaning data and treating missing values in preprocessing, we have to use few feature selection techniques like similarity measures such as Euclidean distance, Cosine distance, and also dimensionality reduction by using principal component analysis, single value decomposition, etc.. These techniques are widely used in recommendation systems.

Model development

For this recommendation system for banking products, we will follow five different stages. there are

  • First, we need to build up a product rating (PR) algorithm that certainly infers client preferences from daily life transaction information. This transaction information has been utilized by organizations and analysts to analyze client behavior since it uncovers clients’ spending patterns. This algorithm gives a rating to every item which are utilized by clients by utilizing their banking products to do individual or personal and business-related transactions. 
  • We use the rating dataset, which the PR algorithm created, to predict ratings for unrated products by using the item-based collaborative filtering method.
  • After completing the first two steps, we group the customers into different categories based on their age, gender, marital status, etc. by using K-Mean’s unsupervised machine learning algorithm. And then generated the cluster-product rating data containing the average of the ratings given to a product by all the customers in that cluster or category.
  • For better performance, we combine both the predicted and average ratings using other unique techniques.                 
  • At long last, we recommend top-N unseen products to any given customer, whether the client new or existing, based on the final ratings.

Inference and deployment of the model

This is time to apply our trained model to real-world customer transactions and then suggest or recommend related bank products. But this is not the final product we have to test in all cases like this model recommend the right products to students such as education loans and useful products to senior citizens. After checking all perspectives, if we are satisfied with the results, the product will be released. This recommendation system leads to successfully interacting with clients or customers. It’s all about understanding needs, building trust.

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