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Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting
Dissertation (MEng)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613653417590784 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/71025 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:34.197Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/71025 Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting De Villiers, Johan Pieter u10106929@tuks.co.za Beyers, Frederik Johannes Conradie Punnen, Ajith UCTD Dissertation (MEng)--University of Pretoria, 2018. Liquidity risk is one of the key risks faced by banks in their daily operations. Following the recent financial crisis, more stringent measures have been put in place to ensure that banks adequately cater for sufficient liquidity and stable funding. In liquidity planning a difficult component is the modelling of indeterminate maturity products, which from a liabilities point of view includes transactional and savings accounts (demand deposit accounts). Banks utilise the balances in these products to also partly supply the funds necessary for loans and other forms of credit, which generate most of their profits. The purpose of this study was to find a way to accurately forecast the daily bank balance of a demand deposit account portfolio across the period of a year. This would help the banks to more efficiently handle liquidity planning and also generate more profit by utilising their funds more effectively. In accomplishing this the study also presented the hypothesis that using a hybrid model which combined segmentation with a popular forecasting method such as autoregressive integrated moving average (ARIMA) models would do better than a single time series forecasting model. The purposes of the segmentation was to identify customers with similar current account dynamics e.g. salaried individuals in comparison to a small business owner. Segmentation was facilitated by extracting features from the time series that identified patterns of salaried individuals in comparison to other account holders. These features were used by the k-means algorithm to form the segments. ARIMA models were then implemented for each of the segments and forecasts obtained per segment. These segment level forecasts were then aggregated to obtain the portfolio level forecasts. The results were then compared to building a single model to forecast the portfolio daily balance. Results from the study suggest that the hybrid model statistically performs significantly better than the single model over shorter forecast horizons. This study also attempted to find a way to score customers into one of the identified segments using information available on enrolment. However, results suggested that there is not enough discriminative power available in the information collected at enrolment but rather it is better to include information regarding a customer’s first month’s bank balance which significantly improved the classification accuracy. Electrical, Electronic and Computer Engineering MEng Unrestricted 2019-08-12T11:18:51Z 2019-08-12T11:18:51Z 2019/04/10 2018 Dissertation Punnen, A 2018, Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/71025> A2019 http://hdl.handle.net/2263/71025 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title | Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title_full | Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title_fullStr | Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title_full_unstemmed | Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title_short | Segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| title_sort | segmenting bank customers using similarities in current account dynamics to improve daily bank balance forecasting |
| topic | UCTD |
| url | http://hdl.handle.net/2263/71025 |