Creating Value in Private Banks by Successfully Using Recommender Systems
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2019
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
With the advent of emerging technologies in the banking industry and the rise of new competitors, banks are forced to become more innovative in order to overcome the ever-increasing margin pressure. In addition to new services recently introduced by private banks such as chatbot support and enhanced mobile capabilities, the industry is focusing on intelligent portfolio advisory robots. Whereas some Fintech solutions offer a fully automated approach, some banks have decided to adapt a hybrid approach which is led by a customer relationship manager and supported by a robo-advisor. Due to the importance of personal relationships between the customer and the relationship manager, private banks may not be fond of fully replacing the customer relationship manager by a robot. Accompanying a Swiss based private bank during the implementation of a machine-learning based recommender system which is generating the next best personalized investment recommendation for customer relationship managers (CRM), this master thesis aims to confirm that the successful use of this system increases the financial performance of the bank. Furthermore, the main goal of this thesis is to identify the critical success factors of a project dealing with the introduction of an AI recommender system as well as to assess, what impact the solution has on the banks' relationship managers and customers....
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
HORN, Daniel, 2019. Creating Value in Private Banks by Successfully Using Recommender Systems. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/40371