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A Trade-based Inference Algorithm for Counterfactual Performance Estimation
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
En handelsbaseradinferensalgoritm för kontrafaktisk prestandaestimering (Swedish)
Abstract [en]

A methodology for increasing the success rate in debt collection by matching individual call center agents with optimal debtors is developed. This methodology, called the trade algorithm, consists of the following steps. The trade algorithm first identifies groups of debtors for which agent performance varies. Based on these differences in performance, agents are put into clusters. An optimal call allocation for the clusters is then decided. Two methods to estimate the performance of an optimal call allocation are suggested. These methods are combined with Monte Carlo cross-validation and an alternative time-consistent validation procedure. Tests of significance are applied to the results and the effect size is estimated.

The trade algorithm is applied to a dataset from the credit management services company Intrum and is shown to enhance performance.

Abstract [sv]

En metodik för att öka andelen lyckade inkassoärenden genom att para ihop telefonhandläggare med optimala gäldenärer utvecklas. Denna metodik, kallad handels-algoritmen, består av följande steg. Handelsalgoritmen identifierar först grupper av gäldenärer för vilka agenters prestationsförmåga varierar. Utifrån dessa skillnader i prestationsförmåga är agenter placerade i kluster. En optimal samtalsallokering för klustren bestäms sedan. Två metoder för att estimera en optimal samtalsallokerings prestanda föreslås. Dessa metoder kombineras med Monte Carlo-korsvalidering och en alternativ tidskonsistent valideringsteknik. Signifikanstester tillämpas på resultaten och effektstorleken estimeras.

Handelsalgoritmen tillämpas på data från kredithanteringsföretaget Intrum och visas förbättra prestanda.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:273
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-254453OAI: oai:DiVA.org:kth-254453DiVA, id: diva2:1334411
External cooperation
Intrum
Subject / course
Mathematics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-07-02Bibliographically approved

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