Machine Learning-based Customer Profiling
With an immense amount of available data, customer profiling has never been easier - and more difficult. As an industrial doctorate student you can now make a difference to automate that profiling process by using advanced machine learning techniques. The EIT Digital Industrial Doctorate programme by OTP Bank Hungary and ELTE University is to result in optimising interactions with bank clients thanks to model-based recommendations. You can apply now.
OTP Bank Hungary aims to create a model-based operation and decision-making environment via being among the first participants in the retail banking sector who implement advanced machine and deep learning techniques in the whole spectrum of their daily operation. The automation of predictive modelling and customer segmentation will be the cornerstone of providing real time, actionable insights for the decision makers and suitable recommendations for the clients.
The technological revolution of the past decades caused a high-speed accumulation of business related data in the retail banking sector; this trend is expected to accelerate during the coming years due to the spreading of smart appliances and online banking. Most importantly, clients' behaviour is now recorded in more detail than ever before, with thousands of features about their demographic background, savings, investment portfolio, payment, browsing and purchasing history, and the financial products they own. The massive volume of client data - combined with external data sources like satellite images, maps, macroeconomic indices - requires new analytic approaches to create useful insights for decision makers and develop/implement automated tools to collect, clean, validate, analyse data and generate real time recommendations. New methods are constantly developed in the data science community, but they are mostly focused on engineering problems like image and speech recognition. The challenge is how to use these techniques for retail banking purposes.
This industrial doctorate work will be advanced in the customer profiling domain, allowing larger banks to create constantly evolving customer micro-profiles, which will allow them to provide personalized services, thereby optimizing their interactions with customers, offering the next best product and avoiding churn.
The research questions of the PhD will be formulated in line with the bank's need to identify clients' future behaviour, which is a true pain point, whose solution has the potential to revolutionise the interaction between the bank and its customers. A draft list of novel research questions which will be analysed during the PhD project follows: churn prediction/survival analysis, recommendation systems, data visualization.
The result of the PhD will be a toolbox with implemented algorithms able to automatically collect, structure and clean customer data, give model-based recommendations and predictions. It will be implemented in a financial system's production environment (for example OTP Bank) as a business supporting tool. The measurable outputs shall include documentation of existing industrial solutions in the field of research, implementation of existing solutions utilized as baselines, implemented behaviour prediction toolbox, a detailed analysis of the candidate's contribution and the impact on the bank's operational and business processes, published papers in journals and conferences.
The doctoral student involved in this industrial doctorate programme, will share his time between the EIT Digital Doctoral Training Centre in Budapest, the premises of OTP Bank Hungary, and ELTE.
- Industrial partner: OTP Bank Hungary
- Academic/research partner: ELTE
- Number of available PhD positions: 1
- Duration: 4 years
- This PhD will be funded by EIT Digital, ELTE, and OTP Bank Hungary
If you are interested in applying for this position, please send an e-mail to the EIT Digital Budapest DTC lead email@example.com, including a CV, a motivation letter, and documents showing your academic track records.
Please apply before 24 August 2018, 12.00 CET.