  Financial inclusion ensures that individuals have access to financial
products and services that meet their needs. As a key contributing factor to
economic growth and investment opportunity, financial inclusion increases
consumer spending and consequently business development. It has been shown that
institutions are more profitable when they provide marginalised social groups
access to financial services. Customer segmentation based on consumer
transaction data is a well-known strategy used to promote financial inclusion.
While the required data is available to modern institutions, the challenge
remains that segment annotations are usually difficult and/or expensive to
obtain. This prevents the usage of time series classification models for
customer segmentation based on domain expert knowledge. As a result, clustering
is an attractive alternative to partition customers into homogeneous groups
based on the spending behaviour encoded within their transaction data. In this
paper, we present a solution to one of the key challenges preventing modern
financial institutions from providing financially inclusive credit, savings and
insurance products: the inability to understand consumer financial behaviour,
and hence risk, without the introduction of restrictive conventional credit
scoring techniques. We present a novel time series clustering algorithm that
allows institutions to understand the financial behaviour of their customers.
This enables unique product offerings to be provided based on the needs of the
customer, without reliance on restrictive credit practices.
