AI Product Recommendations systems have become a fundamental part of e-commerce and finance helping businesses deliver personalised services. That effectively engage customers. These recommendations are based on machine learning (ML) algorithms that analyse customer data to create customised offers.
Using data on browsing patterns, transaction history, and contextual information, AI-powered systems can predict the products or services that are most relevant to each user. How exactly do they work in the banking sector? Find out in this article!
AI Recommendations Fundamentals in Finance
AI product recommendations systems rely on structured and unstructured data to deeply understand customer preferences. This data includes transactional behaviour, demographic information, and real-time interaction information.
Graph neural networks (GNNs) are particularly effective in financial AI applications because they map complex relationships in the data, allowing algorithms to learn subtle connections between different features, behaviours, and attributes of a customer’s product.
GNNs improve recommendation accuracy by connecting these dots, often leading to increased customer engagement and higher sales conversions.
Recommendation engines use several types of machine learning models to generate suggestions, including:
collaborative filtering – studying patterns in groups of users, matching people with similar behaviours or preferences;
content-based filtering – analysing certain product features and matching them to customers based on their past choices;
and hybrid models – a combination of the two models above.
From these models, they can personalise the products (and content) each customer receives. The result is that customers receive recommendations that they are more likely to act on, increasing conversion rates at banks and other financial institutions.
Using real-time data for dynamic recommendations
The defining feature of AI-powered product recommendations is their capability to adapt to changing customer behaviour in real time. Advanced recommendation systems process data as it arrives, allowing for minute-by-minute adjustments to the suggestions presented to users.
For example, when a customer starts looking for investment opportunities, the system can immediately prioritise related product recommendations or informative content about portfolio management and investment options.
In this way, you can target a customer when they are more likely to read certain content or purchase a product, making them more likely to choose your recommendations.
Security Challenge in AI Product Recommendation Models
While AI-powered product recommendations are a great option for improving sales, there are some ethical considerations you need to consider. First, these models deal with a lot of customer data, often sensitive information. Therefore, the cybersecurity of such models is critical.
Second, these algorithms are prone to bias, leading to unfair judgments. Imagine offering your customers a credit card, but due to data bias, they end up with a lower credit score and card limit than they should.
Not only is this risky, as the customer may accuse you of discrimination, but it also threatens the customer’s decision to buy the product because the limit is not what they expected. Therefore, besides data security, it is necessary to ensure data quality and eliminate potential bias.
Conclusion
AI-powered product recommendations use data to provide customers with personalised solutions that meet their requirements and expectations and solve their problems. These systems are highly effective in increasing sales, but they also come with risks – you must ensure your data is free from bias and securely protected from cyber-attacks!
FAQ
What is product recommendation in AI?
Product recommendation in AI refers to using machine learning algorithms to suggest specific products or items to customers based on their past behaviors, preferences, and interactions with a platform or e-commerce website. The goal is to provide personalized and relevant product suggestions to enhance the customer experience and drive sales.
How could one use AI to recommend products to customers?
AI recommends products to customers by analyzing their historical data, including browsing history, purchase history, and demographic particulars, to identify patterns and preferences. Algorithms then align these patterns with similar customer profiles and product attributes, generating real-time tailored recommendations. These recommendations are often displayed on the website, within email campaigns, or integrated into mobile apps.
What is an example of a product recommendation system?
Insider is an example of a product recommendation tool, particularly designed for ecommerce platforms. By analyzing a user’s browsing and purchase history, as well as drawing insights from similar customer behaviors, you can craft personalized product suggestions. These tailored recommendations, such as “Customers who bought this also bought…” or “Recommended for you” significantly enhance the shopping experience, guiding customers towards products that match their interests and needs.