Contextual Recommendation System for Banking
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Solution Overview
Problem
Customers face frustration in banking interactions due to the cumbersome process of communicating their changing needs based on geography and location, often failing to accurately convey their requirements.
Innovation Solution
A contextual recommendation system that uses location-based services and external events to predict customer needs, providing targeted recommendations such as ATM or bank branch locations, and adjusting account settings based on location context information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers directly communicate their banking needs to the bank, then the bank can understand customer requirements, but customers feel frustrated when their needs are not immediately fulfilled and often fail to accurately communicate their needs
Solution Approach 1:
The system automatically identifies customer banking needs by analyzing location data and contextual information without requiring customers to manually communicate their requirements. The system serves itself by predicting needs based on geographic context, eliminating the frustration of unfulfilled requests and improving accuracy of need identification.
Solution Approach 2:
The system continuously monitors customer location and contextual data, compares it with banking patterns, and provides real-time recommendations or alerts. This feedback loop enables the system to understand and anticipate customer needs dynamically, resolving the contradiction between accurate need identification and ease of interaction.
2Reliability
If the system provides automated contextual recommendations based on location, then customer satisfaction improves and need identification accuracy increases, but the system complexity increases
Solution Approach 1:
The system uses a unified location-based framework that handles multiple banking scenarios (ATM recommendations, branch directions, fraud alerts, account modifications) through a single automated architecture. This multi-functional approach improves customer satisfaction across diverse needs while managing system complexity through standardized processing.
Solution Approach 2:
The system introduces a contextual recommendation engine as an intermediary between location data and banking actions. This mediator processes location information, predicts customer needs, and generates appropriate recommendations or alerts, thereby improving reliability while containing complexity within a dedicated processing layer.
3Loss of time
If the system monitors location continuously to predict needs, then the timeliness of banking services improves, but the use of energy and data processing requirements increase
Solution Approach 1:
The system monitors location periodically rather than continuously, triggering analysis only when location changes or contextual events occur. This periodic monitoring maintains timely response to customer needs while significantly reducing energy consumption and data processing requirements compared to continuous tracking.
Solution Approach 2:
The system pre-processes location data and establishes geographic context in advance, so when a customer need arises, the analysis is already prepared. This preliminary action reduces real-time processing requirements, enabling timely service delivery with lower energy consumption during active banking interactions.
Data Source
AI summary
A method may include receiving, from a customer device, contextual location information about a customer and an interaction type, determining a location-based recommendation and an urgency level for the customer using the contextual location information, generating a routing protocol for communication with the customer based on the interaction type and the urgency level, and providing a communication to the customer according to the routing protocol, the communication including the location-based recommendation.