Buy Financial Mathematics Dissertation

Posted: September 16th, 2022

Buy Financial Mathematics Dissertation
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Signed ……………………………………………. (candidate) Date 06-09-2019 Gautam Prasad
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Buy Financial Mathematics Dissertation
 
Executive Summary Companies want to interact with their customers in a way that is not limited by time, human
resource availability or language. They need to pre-empt the needs of their clientele in order to
keep them satisfied and thereby reduce customer churn. Businesses such as Vodafone, Royal
Bank of Scotland (RBS) and NatWest and other businesses in the telecom, and finance domain
are investing in chatbots to build and maintain new relationships looking to lower overheads,
costs and training time.
There are three significant types of chatbot expert systems in use; chitchat bots, short tail/ task-
focused bots, and longtail search or FAQ bots. These have primarily been used individually
based on business requirements. Chitchat focuses on having the most natural conversation with
a user based on their inputs; Short tail looks at helping the user complete a small number of
regularly performed tasks and requires high training effort to scale efficiently but tends to pro-
vides consistent results. Longtail systems are focussed on information retrieval and require a
more significant training effort at the start, provides a wider variety of answers at lower confi-
dences; however, scales more efficiently. Developing a mixture of experts’ system, that is ca-
pable of combining these three technologies into a single personality with balanced relevancies
of which response is to be used, is of high interest to organisations. This will enable them to
better lever their existing investments in brand personality (chitchat systems) and human-read-
able material (long tail systems) while allowing them to rapidly develop new task-focused
(short tail) systems in a wide variety of their business domains using modern API based con-
versational short tail systems. This project looks at the best method to bring these disparate
types of systems together into a coherent conversational personality.
The analysis looked at different scenarios using unmodified confidence scores from the expert
systems, building a high-level classifier to determine the best system to answer, and simulating
fallback rules used in systems like IBM Watson. The outputs from all the scenarios were opti-
mised using evolutionary algorithms and optimised using a prepared data set on the same topic
that was applicable to all three system types, before being compared using an accuracy metric
to determine the most successful strategy. The additional effect of concurrency between each
user utterance was then evaluated against these strategies.
The study concluded that merging the chitchat and short-tail training data to reduce the number
of expert systems from 3 to 2 and then use of the fallback rules works best if the confidence
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