Transactional Compatible Representations for High Value Client Identification: A Financial Case Study

Irene Unceta, Jordi Nin, Oriol Pujol

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

2 Cites (Scopus)

Resum

High value client identification is a crucial task to any company. In the banking industry, high value is not solely related to purchasing power, but also to an intensive use of financial services, such as card payments or bank wire transfers. This is why transactional data is a valuable source of information. In this work we propose a method to estimate the net worth of individuals for whom we lack any transactional data, either because they are non-clients or because they conduct their main activity elsewhere. We exploit the representation learned by a value prediction model trained over a signed graph of social financial relationships between BBVA clients to infer a transactional compatible representation of clients outside the graph. As a result, we obtain a new model that can predict value labels for both client and non-client data. Our results show an improvement in prediction accuracy over the previous baseline in a 2 million client database.

Idioma originalAnglès
Títol de la publicacióComplex Networks XI - Proceedings of the 11th Conference on Complex Networks, CompleNet 2020
EditorsHugo Barbosa, Ronaldo Menezes, Jesus Gomez-Gardenes, Bruno Gonçalves, Giuseppe Mangioni, Marcos Oliveira
EditorSpringer
Pàgines334-345
Nombre de pàgines12
ISBN (imprès)9783030409425
DOIs
Estat de la publicacióPublicada - 2020
Publicat externament
Esdeveniment11th International Conference on Complex Networks, CompleNet 2020 - Exeter, United Kingdom
Durada: 31 de març 20203 d’abr. 2020

Sèrie de publicacions

NomSpringer Proceedings in Complexity
ISSN (imprès)2213-8684
ISSN (electrònic)2213-8692

Conferència

Conferència11th International Conference on Complex Networks, CompleNet 2020
País/TerritoriUnited Kingdom
CiutatExeter
Període31/03/203/04/20

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