| Abstract: |
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. |