Feature selection strategies for improving data-driven decision support in bank telemarketing

The usage of data mining techniques to unveil previously undiscovered knowledge has been applied in past years to a wide number of domains, including banking and marketing. Raw data is the basic ingredient for successfully detecting interesting patterns. A key aspect of raw data manipulation is feature engineering and it is related with the correct characterization or selection of relevant features (or variables) that conceal relations with the target goal. This study is particularly focused on feature engineering, aiming at the unfolding features that best characterize the problem of selling long-term bank deposits through telemarketing campaigns. For the experimental setup, a case-study from a Portuguese bank, ranging the 2008-2013 year period and encompassing the recent global financial crisis, was addressed. To assess the relevance of such problem, a novel literature analysis using text mining and the latent Dirichlet allocation algorithm was conducted, confirming the existence of a research gap for bank telemarketing. Starting from a dataset containing typical telemarketing contacts and client information, research followed three different and complementary strategies: first, by enriching the dataset with social and economic context features; then, by including customer lifetime value related features; finally, by applying a divide and conquer strategy for splitting the problem in smaller fractions, leading to optimized sub-problems. Each of the three approaches improved previous results in terms of model metrics related to prediction performance. The relevance of the proposed features was evaluated, confirming the obtained models as credible and valuable for telemarketing campaign managers.A utilização de técnicas de data mining para a descoberta de conhecimento tem sido aplicada nos últimos anos a uma grande variedade de domínios, incluindo banca e marketing. Os dados no seu estado primitivo constituem o ingrediente básico para a deteção de padrões de informação. Um aspeto chave da manipulação de dados em bruto consiste na "engenharia de atributos", que compreende uma correta definição e seleção de atributos relevantes (ou variáveis) que se relacionem com o alvo da descoberta de conhecimento. Este trabalho foca-se numa abordagem de "engenharia de atributos" para definir as variáveis que melhor caraterizam o problema de vender depósitos bancários a prazo através de campanhas de telemarketing. Sendo um estudo empírico, foi utilizado um caso de estudo de um banco português, abrangendo o período 2008-2013, que inclui os efeitos da crise financeira internacional. Para aferir da importância deste problema, foi realizada uma inovadora análise da literatura recorrendo a text mining e ao algoritmo latent Dirichlet allocation, confirmando a existência de uma lacuna nesta matéria. Utilizando como base um conjunto de dados de contactos de telemarketing e informação sobre os clientes, três estratégias diferentes e complementares foram propostas: primeiro, os dados foram enriquecidos com atributos socioeconómicos; posteriormente, foram adicionadas características associadas ao valor do cliente ao longo do seu tempo de vida; finalmente, o problema foi dividido em problemas mais específicos, permitindo abordagens otimizadas a cada subproblema. Cada abordagem melhorou as métricas associadas à capacidade preditiva do modelo. Adicionalmente, a relevância dos atributos foi avaliada, confirmando os modelos obtidos como credíveis e valiosos para gestores de campanhas de telemarketing

Optimização da gestão de contactos via técnicas de business intelligence: aplicação na banca

Com a massificação de campanhas publicitárias é cada vez mais reduzido o efeito que as mesmas têm sobre o público-alvo, pelo que os responsáveis de Marketing têm cada vez mais apostado em campanhas direccionadas. Desta forma, a área de Business Intelligence reveste-se de um enorme potencial com vista à melhoria da selecção de contactos a efectuar. Em particular, realçam-se as técnicas de Data Mining, a partir das quais se pode extrair conhecimento útil a partir de dados não tratados. Devido a pressões externas, como a crise financeira internacional, e à concorrência interna, as instituições financeiras portuguesas têm apostado no Marketing direccionado, optimizando as suas campanhas de forma a aumentar a sua eficiência. Assim, esta dissertação irá focar-se num estudo de caso de uma instituição bancária nacional, tendo em conta dados de campanhas de subscrição de depósitos a prazo efectuadas entre 2008 e 2010, os quais serão utilizados na aplicação de técnicas de Data Mining para a optimização dessas campanhas. Da investigação resulta um modelo explicativo da evolução das campanhas, nomeadamente na sua capacidade de previsão do sucesso dos contactos. Desta forma, é possível extrair conhecimento útil e que poderá suportar decisões de negócio pelos gestores, que poderão assim conceber campanhas que beneficiem das características identificadas pelo modelo.The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. That led the marketing managers to invest on directed campaigns, which have been enhanced strongly through Business Intelligence techniques to help select the best set of available contacts for each campaign. In particular, by using Data Mining techniques which allow to extract knowledge from raw data. Furthermore, economical pressures and competition has led marketing managers to invest on directed campaigns and its optimization to increase efficiency. Having this information into account, the present dissertation will focus on a case-study about a Portuguese financial institution, by using its data about directed marketing campaigns of long-term deposits subscription (which were executed between 2008 and 2010) and applying Data Mining techniques with a goal on the optimization of future similar campaigns. From this research, an explanatory model is conceived that can, with a good precision, predict success in contacts (subscription of the deposit). Valuable knowledge can be extracted from this model in the form of characteristics that can be used to benefit future similar campaigns

Business Intelligence in Banking: a Literature Analysis from 2002 to 2013 using Text Mining and Latent Dirichlet Allocation

Abstract This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. * Corresponding author (S. Moro). Email addresses: [email protected] (Sérgio Miguel Carneiro Moro), [email protected] (Paulo Alexandre Ribeiro Cortez), [email protected] (Paulo Miguel Rasquinho Ferreira Rita) Preprint submitted to Expert Systems With Applications September 1, 2014 Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research