AI-Based BNPL Offer Recommendation for Precise Consumer Matching

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Solution Overview

Problem

Consumers struggle to identify suitable Buy Now, Pay Later (BNPL) offers most suitable for their particular situation or preference.

Innovation Solution

A system and a method are provided to recommend BNPL loans to consumers based on one or more specific measures the system and a method are provided to recommend Buy Now, Pay Later (BNPL) loans using AI models trained on past transaction patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple installment program providers and merchants participate in offering BNPL products, then the variety and availability of BNPL offers increase, but it becomes difficult for consumers to identify suitable offers for their particular situation

Engineering Contradiction:

Improvevariety of BNPL offersVSAvoiddifficulty for consumers to identify suitable offers

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system enables consumers to automatically receive personalized BNPL recommendations based on their transaction history and preferences without manual intervention. The AI model autonomously analyzes consumer data and generates tailored recommendations, eliminating the need for consumers to manually search through multiple offers

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces an intermediary recommendation system that acts as a mediator between consumers and multiple BNPL providers. This system processes consumer transaction data and matchesthem with suitable offers, simplifying the complex landscape of multiple providers into curated recommendations

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If AI models are used to recommend BNPL loans based on past transaction patterns, then the precision of offer matching improves, but the system complexity increases

Engineering Contradiction:

Improveprecision of offer matchingVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by continuously training AI models on historical transaction data before actual recommendations are needed. This pre-processing of data and model training enables fast, accurate recommendations at the time of consumer purchase without adding complexity to the real-time decision process

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses copying by creating simplified representations of consumer behavior patterns through AI models. These models capture essential transaction patterns without requiring the full complexity of raw data, enabling efficient matching while reducing system complexity

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250390943A1Systems and methods for recommending a buy now, pay later offer

Publication Date: 2025.12.25 MASTERCARD INT INC

AI summary

A system for recommending Buy Now, Pay Later (BNPL) offers receives a request for the recommended BNPL loan offers. The request is associated with a transaction. The system retrieves a BNPL loan offer similarity matrix and a transaction similarity matrix from a database. The system also retrieves consumer historical transaction records associated with the consumer from the database. Using the transaction data, the BNPL loan offer similarity matrix, and the transaction similarity matrix, the system performs both a content-based recommendation calculation and an experience-based recommendation calculation. The system then produces the recommended BNPL loan offers based on the results of the two calculations and transmits the recommended BNPL loan offers to a merchant for completing the transaction.