Channel Affinity Switching With Deep Links for Frictionless Transactions
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Solution Overview
Problem
Existing enterprise systems require manual login and navigation through multiple steps on a new channel to complete a transaction not supported on the incoming channel, leading to inefficiencies and increased time and cost.
Innovation Solution
An enterprise computing platform uses an eligibility machine learning model to determine a preferred channel based on customer unique IDs and transaction intent, generating a deep link that automatically authenticates and completes the transaction without manual login on the new channel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual login and navigation through multiple steps on a new channel is required to complete a transaction, then the transaction can be completed on a channel that supports the requested transaction functionality, but the transaction time increases and cost efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-extracting numerical features and intent features from channel input data before the transaction occurs. The eligibility machine learning model processes these features in advance to determine the preferred channel, so when the transaction needs to be completed, the channel selection and authentication preparation is already done, eliminating manual login steps and reducing transaction time.
Solution Approach 2:
The eligibility machine learning model acts as an intermediary between the incoming channel and the preferred channel. It receives channel input data, extracts features, processes them through the machine learning model, and outputs the preferred channel, mediating the transition and enabling seamless channel switching without manual user intervention.
2Reliability
If manual login and navigation through multiple steps on a new channel is required to complete a transaction, then the transaction can be completed on a channel that supports the requested transaction functionality, but the operational complexity increases
Solution Approach 1:
The system implements self-service by automatically determining the preferred channel using the eligibility machine learning model and generating deep links that perform automatic authentication. The user does not need to manually navigate through login steps or configure channel preferences, as the system serves itself by using extracted numerical and intent features to automatically select and switch channels.
Solution Approach 2:
The patent replaces the mechanical manual login process with an automated machine learning-based channel selection system. Instead of requiring users to manually navigate through multiple steps on a new channel, the eligibility machine learning model processes channel input data and automatically determines the preferred channel, substituting manual operations with automated intelligent decision-making.
3Reliability
If channel switching is performed manually by the user, then the user can complete transactions on supported channels, but the cost efficiency of channels decreases
Solution Approach 1:
The system changes parameters by extracting and processing numerical features and intent features from channel input data. The eligibility machine learning model uses these processed parameters to determine the optimal channel selection, enabling cost-efficient channel switching based on data-driven insights rather than manual user choices, thereby improving overall cost efficiency.
Data Source
AI summary
Systems and methods are disclosed for real time, frictionless channel switching to map and transpose a transaction on a preferred channel from an incoming channel. The systems and methods may use a machine learning model that processes features from a plurality of channels to determine the preferred channel. An omni channel processor is used to determine intended functionality data of the transaction for the preferred channel. The systems and methods may generate transposed intent data based on the intended functionality data of the transaction for the preferred channel. A deep link is generated and embedded with the transposed intent data and a channel access token for the transaction on the preferred channel. The deep link is configured to access and complete the requested transaction on the preferred channel.