Modelling a multi-channel messaging framework: a machine learning approach
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Authors
Salami, Olusola
Issue Date
2023-01
Type
Thesis
Language
en
Keywords
Machine learning , Artificial intelligence , Datasets , Multi-channel messaging system , Messaging channels , Machine learning algorithms , Upper confidence bound algorithm , Tug of war algorithm , Multi-armed bandits problem , Design science research , Enterprise service bus , Message producers , Message consumers , Financial services institutions , Customer alert messaging system , Fourth Industrial Revolution and Digitalisation , SDG 9 Industry, Innovation and Infrastructure
Alternative Title
Abstract
Multi-channel messaging (MCM) systems have been implemented to integrate heterogeneous
messaging channels for message delivery to Financial Services Institutions (FSIs) customers
worldwide. However, implementations utilizing machine learning techniques to determine
channel availability, dynamic assignment, and monitoring of customer patterns are being
explored with newer technological advancements. Such approaches are used to investigate
how Integrated multi-channel messaging can be implemented using machine learning
algorithms to enable effective and efficient dynamic channel selection and integration
methods.
This research explored and investigated the various machine learning algorithms for optimal
channel selection. The study delved into applying these algorithms and their use to channel
selection, including the end-user context, focusing on the multi-armed bandit (MAB) problem,
Tug of War and Upper Confidence Bound algorithm in providing a novel approach to solving
this problem.
The study presented a framework that fully integrates different web channels (Facebook,
WhatsApp, Instagram IM, SMS, and Email) with a decision-making module and machine
learning for the model to learn customer patterns over time. This framework uses a minimal
memory and computation capability which employs simple learning procedures from the
machine learning algorithms while relying on the message acknowledgement feedback from
each channel. This work also describes a software architecture to support this and evaluate
its effectiveness in an MCM enabled customer alert system currently used by financial service
institutions. The efficacy of the proposed solution was evaluated using a simulation-based
performance analysis method.
The designed framework took advantage of simple machine learning algorithms and the
inherent flexibility of integrating heterogeneous channels for efficient resource utilization when
messages are transmitted. Furthermore, an Enterprise Service Bus (ESB) assigns weights
dynamically to channels that customer use frequently with the use of the Upper Confidence
Bound algorithm. This method allows flexibility to realise requirements for geolocation sensing,
scheduling schemes and user mobility. The framework can choose channels dynamically,
incorporating a machine learning module for message delivery patterns and supporting an
integration layer via a common data channel-agnostic to each channel integrated. Lessons
learnt from this design should be further refined to motivate future work in this area