Control of non-performing loans in retail banking by raising financial and consumer awareness of clients

Study aims to quantitatively evaluate the impact, which borrowers’ consumer and financial behavior has on the volume of non-performing retail loans (NPL) in commercial banks of Lithuania and identifies the ways of its control. A strong link between NPL volume and these factors was established. Both are education-sensitive, therefore by investing into financial and consumer awareness raising of clients, commercial banks could improve business conditions in retail-loan market. Recommendations for commercial banks to undertake measures reducing NPL and invest in them were developed as well as methodology for estimation of payback potential of the investment. Study implies that wider context, covering both financial and consumer behavior of clients should be applied when analyzing reasons of poor performance of borrowers. Research methods used: comparative analysis, processing of statistical data, expert evaluation, mathematical analysis

Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope. To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub

6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Data Representation and Indexing in Location-Enabled M-Services

Rapid, sustained advances in key computing technologies combine to enable a new class of computing services that aim to meet needs of mobile users. These ubiquitous and intelligent services adapt to each user's particular preferences and current circumstances---they are personalized. The services exploit data available from multiple sources, including data on past interactions with the users, data accessible via the Internet, and data obtained from sensors. The user's geographical location is particularly central to these services. We outline some of the research challenges that aim to meet the data representation and indexing needs of such services