Independent Banker - November 2023 - 47

How AI mines data
As in most industries, AI technology is
indispensable in banking for distilling actionable
intelligence from the massive amounts of data
being ingested from customers and generated
by employees.
Community banks can choose from a vast
range of available data mining and AI methods,
depending on desired outcomes and data
availability, says Robert Johnston, CEO of Adlumin
Inc. in Washington, D.C.
" For example, if the goal is to evaluate each
customer for digital marketing suitability for a new
product, 'supervised' methods such as logistic
regression or decision-tree classifier could be
trained on customer data, " Johnston says. " These
use cases require customer data on prior actions,
such as historical responses to marketing emails. "
For a customer segmentation issue,
" unsupervised " methods such as DBSCAN
clustering or PCA dimensionality reduction are
called for, where prior observations aren't imposed
on specific customer actions, but rather aim at
customers according to machine-learned similarity
measurements, he says.
More advanced methods, such as artificial neural
networks (ANNs), are deployed when the use case
depends on learning complex interactions among
numerous factors, such as customer service call
volume and outcome evaluation, or even the
customer classification and clustering problems
mentioned earlier, Johnston says. The data volume,
frequency and computer capacity requirements are
typically heavier for ANNs than for other machine
learning techniques.
The most visible near-term evolution in the
field is the spread of large language models or
generative AI, such as ChatGPT, he says. The
underlying methods behind these emergent AI
technologies are also based on ANNs mentioned
above-only with hugely complicated neural
network architectures and computationally very
expensive learning algorithms.
" Adaptation and adoption of these methods
for customer classification, segmentation and
interaction-facilitation problems will be a trend to
closely follow in the years ahead, " Johnston says.
The central challenge for AI in cyber applications
is to find " needle in haystack " anomalies from
billions of data points that mostly appear
indistinguishable, he says.
The applications in this domain are usefully
grouped under user and entity behavior analytics
(UEBA) and involve mathematical baselining of
users and devices on a computer network followed
by machine-identification of suspicious deviations
from baseline, Johnston says.
independentbanker.org // 47
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Independent Banker - November 2023

Table of Contents for the Digital Edition of Independent Banker - November 2023

Contents
Independent Banker - November 2023 - Intro
Independent Banker - November 2023 - Cover1
Independent Banker - November 2023 - Cover2
Independent Banker - November 2023 - Contents
Independent Banker - November 2023 - 2
Independent Banker - November 2023 - 3
Independent Banker - November 2023 - 4
Independent Banker - November 2023 - 5
Independent Banker - November 2023 - 6
Independent Banker - November 2023 - 7
Independent Banker - November 2023 - 8
Independent Banker - November 2023 - 9
Independent Banker - November 2023 - 10
Independent Banker - November 2023 - 11
Independent Banker - November 2023 - 12
Independent Banker - November 2023 - 13
Independent Banker - November 2023 - 14
Independent Banker - November 2023 - 15
Independent Banker - November 2023 - 16
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Independent Banker - November 2023 - 33
Independent Banker - November 2023 - 34
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Independent Banker - November 2023 - 46
Independent Banker - November 2023 - 47
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Independent Banker - November 2023 - Cover3
Independent Banker - November 2023 - Cover4
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