PhD on optimisation of internal processes in financial systems
If you are into data digging, here's your chance to make the world of finance a better place by developing algorithms that will help financial institutions to cut through the clutter regarding their piles of information. The EIT Digital Doctoral School offers you an industrial doctorate that runs at OTP Bank Hungary under supervision of the ELTE University. You will have to come up with new methods to highly optimise data processing and analysis. Apply now.
The vast amount of structured and unstructured data that is generated by the internal processes of modern financial institutions requires efficient solutions for automated data preparation like collecting and cleaning, modelling, analysis and predictive modelling. The main task of this industrial doctorate is to develop process- and data mining algorithms, which will enable financial systems to significantly optimise their internal processes on one hand, while maintaining the current information security posture and protection of personally identifiable information (PII). This requires an in-depth understanding of the way companies in this field operate. The industrial partner will guide the doctoral student through business process identification, cost measurement and optimisation.
Modern financial institutions are faced with the challenges of collecting, cleaning, storing and analysing large volumes of information generated by their business processes. Apart from recording information about their interactions with their customers, modern banks are also equipped with the necessary tools to record rich sets of information about their internal business processes in the form of documentation, application logs, transaction logs, etc. It is a challenging task to identify the most relevant information, whose collection and analysis might bring the highest yield in the mid and long run.
This industrial doctorate will tackle the challenge by:
- Identifying the relevant system processes, like human-system interactions, intra-division processes, processes involving multiple bank divisions (system-system) and integrations with external systems, collecting and analysing their available data,
- Identifying sub-optimal internal processes, and
- Optimising those processes wherever possible, thereby allowing financial systems to have measurable cost savings. The project will be implemented iteratively, and feedback will be given to the hosting financial institution (OTP) both about the quality and quantity of relevant information which they make accessible to this research. It will be possible to communicate the measurable impact in cost savings or other forms. The research will result in industry-grade solutions, applicable both in the target financial institution, but at other similar companies as well.
Large financial institutions maintain complex information systems which consist of a myriad of different processes which are executed for and by different stakeholders. The day-by-day execution of those processes can be monitored, and potential bottlenecks can be identified. The intended solution will collect, clean and analyse the (big) data collected about real-life business processes and advance the state-of-the-art by leveraging the latest developments in the data science domain to identify pain points and bottlenecks which increase the workload and costs on one hand, and lower stakeholder satisfaction on the other hand. Once identified, solutions will be devised to address the weak links in the process ecosystem of financial systems.
The research questions of the PhD will be formulated in line with financial institutions' need to optimise their internal processes, which is a true pain point, whose solution has potential to revolutionize how banks operate internally. A draft list of novel research questions which will be analysed during the PhD project follows:
- Process cost modelling, novel methods to estimate the costs incurred by the financial institution when executing specific business processes, which range between simple bank account opening, implementing complex processes involving multiple departments in customer risk analysis or interactions with external entities.
- Sub-optimal process identification, identify processes which contain excessive numbers of stakeholders or steps. Identify bottlenecks, long delays, participation of too many or too few parties, or process flows involving excessively high (financial) risk.
- Process optimization, developing and proposing solutions which will allow financial systems to optimise their internal processes, thereby lowering costs and increasing stakeholder satisfaction.
The results of the PhD will be incorporated into OTP Bank's and ELTE's joint EIT Innovation and Research projects in the Digital Finance domain. A toolbox capable to automatically collect, structure and clean process data, give model-based recommendations and predictions will be implemented. It will be transferred to OTP for its implementation in a financial system's production environment as a business support tool.
The measurable outputs shall include:
- Documentation of existing industrial solutions in the field of research.
- Implementation of existing solutions utilized as baselines.
- Business process optimization toolbox.
- Process re-engineering course
- 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 firstname.lastname@example.org, including a CV, a motivation letter, and documents showing your academic track records.
Please apply before 24 August 2018, 12.00 CET.