Server-Based Financial Incentive Identification System

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Solution Overview

Problem

Credit card issuers face challenges in effectively identifying and incentivizing customers to use their financial instruments for transactions, particularly in real-time scenarios where competitors' instruments are used, leading to missed opportunities for rewards and revenue.

Innovation Solution

A system and method that utilize location-based notifications and machine learning to identify customer spending opportunities, offering personalized incentives for using the issuer's financial instrument, by integrating with merchants and analyzing transaction data to provide targeted rewards and promotions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If credit card issuers provide rewards for purchases with certain merchants or at certain times, then customer spending is attracted and loyalty is improved, but the complexity of managing and distributing these rewards increases

Engineering Contradiction:

Improvecustomization of rewardsVSAvoidreward management system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system pre-identifies spending opportunities and pre-calculates appropriate rewards before transactions occur. By using machine learning models to predict likely purchases and pre-setting reward parameters, the system avoids complex real-time calculations and manual reward management, thereby maintaining high customization while reducing operational complexity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically identifies spending opportunities, determines appropriate rewards, and distributes them without requiring manual intervention. The machine learning model self-adjusts reward parameters based on transaction data, and the system automatically detects and rewards eligible transactions, eliminating the need for complex manual reward management processes

Inventive Principle:

Principle #25Self-service

2Loss of time

If the system identifies spending opportunities in real-time using location data and notifications, then timely incentives are provided improving customer engagement, but the processing time and computational resources required increase

Engineering Contradiction:

Improveresponse timeVSAvoidtransaction processing efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system performs preliminary identification of spending opportunities by monitoring location data and transaction patterns in advance. Machine learning models pre-process data to identify likely spending scenarios before they occur, so when a transaction opportunity arises, the system can quickly verify and process it without extensive real-time analysis, thus maintaining both speed and efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual or rule-based transaction processing with machine learning models that automatically analyze patterns and make decisions. This substitution of intelligent automation for mechanical processing enables rapid real-time identification of spending opportunities while maintaining high transaction processing efficiency through optimized algorithms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If the system offers personalized incentives based on individual customer behavior and location, then customer loyalty and transaction volume increase, but the difficulty of detecting and measuring spending opportunities increases

Engineering Contradiction:

Improvepersonalization of offersVSAvoidspending opportunity identification

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system continuously monitors customer transactions, location data, and spending patterns, using this feedback to refine machine learning models. By analyzing past customer behavior and reward responses, the system improves its ability to accurately detect and measure spending opportunities, making personalized offer identification more precise over time rather than more difficult

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts detection parameters based on customer profiles, transaction history, and contextual data. Machine learning models modify sensitivity thresholds and detection criteria according to individual customer behaviors and spending patterns, enabling accurate identification of personalized spending opportunities without increasing measurement difficulty

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10915915B1Systems and methods for identifying financial transaction opportunities for individualized offers

Publication Date: 2021.02.09 JPMORGAN CHASE BANK NA

AI summary

Systems and methods for identifying financial transaction opportunities for individualized offers are disclosed. In one embodiment a method for offering rewards to a customer of a financial institution may include (1) a server comprising at least one computer processor identifying a customer spending opportunity for a customer to use a financial instrument issued by a financial institution to conduct a transaction involving the customer spending opportunity; (2) the server determining an incentive to offer the customer for using the financial instrument issued by the financial institution to conduct the transaction involving the customer spending opportunity; and (3) the server communicating the incentive to an electronic device associated with the customer.