Explainable AI for Interpretable Credit Scoring

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.Comment: 19 pages, David C. Wyld et al. (Eds): ACITY, DPPR, VLSI, WeST, DSA, CNDC, IoTE, AIAA, NLPTA - 202

Blockchain technologies in the educational sector. Results of the initial data collection

The education system is subject to an ongoing digital transformation. The administrative departments should be able to handle grading, admissions, enrolments and recognition of certificates securely and quickly. Course managers should not only have faith in e-learning but also in e-assessment. And finally, learners should be able to access course material from anywhere and take exams outside the institutes where they are enrolled. Immutability to changes made retroactively seem to make Blockchain systems the perfect technology to secure data and in combination with digital signatures for identity verification, Blockchain could become the key to digital transformation in education. The paper ‘Blockchain technologies in the educational sector Results of the initial data collection‘ gives a first insight into the level of knowledge of people involved and shows which possibilities Blockchain Technologies could bring to the education sector. Or, more precisely, it shows in which existing applications within the educational system Blockchain Technology should be integrated.peer-reviewe

The use of blockchain technologies to issue and verify micro-credentials for customised educational journeys : presentation of a demonstrator

In recent years, a clear trend towards personalised learning experiences has emerged. Individual preferences are in the foreground and should be made possible, for example, through selectable choices. If these courses take place in the same educational institution, the handling of credit and the clear allocation to learning outcomes is usually possible. However, it becomes difficult when the options also extend to courses outside the main educational institution. It is even more difficult when the study opportunities occur, for example, at educational institutions abroad or at businesses with a strong focus on practice. Here, accreditation is often very difficult, dependent on the case and a painstaking process, unless it is regulated by law, for example through the Bologna Process in the case of universities. New ways in adult education go a whole step further, especially via the so-called second-chance education, when the trainee has a choice of often hundreds of possible courses at various educational and vocational institutions. However, the diploma or work permit is ultimately awarded by the authorities and not a specific university or college. This is an ideal example for the concept of micro-credentials or the possibility of partial achievements being made through various channels, whereby each of these microcredentials clearly defines the educational goal that is required and the extent to which this has been achieved by the learner. The most important factor regarding micro-credentials is a standardised form of storage, presentation, verification and approval processes. By discussing a demonstrator, this paper shows how blockchain technologies in combination with digital identities represent a feasible approach to mapping and comparing micro-credentials.peer-reviewe

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT

Nitrite-derived nitric oxide reduces hypoxia-inducible factor 1α-mediated extracellular vesicle production by endothelial cells

Introduction Extracellular vesicles (EVs) are small, spherical particles enclosed by a phospholipid bilayer (∼30–1000 nm) released from multiple cell types, and have been shown to have pathophysiological roles in a plethora of disease states. The transcription factor hypoxia-inducible factor-1 (HIF-1) allows for adaptation of cellular physiology in hypoxia and may permit the enhanced release of EVs under such conditions. Nitric oxide (NO) plays a pivotal role in vascular homeostasis, and can modulate the cellular response to hypoxia by preventing HIF-1 accumulation. We aimed to selectively target HIF-1 via sodium nitrite (NaNO2) addition, and examine the effect on endothelial EV, size, concentration and function, and delineate the role of HIF-1 in EV biogenesis. Methods Endothelial (HECV) cells were exposed to hypoxic conditions (1% O2, 24 h) and compared to endothelial cells exposed to normoxia (21% O2) with and without the presence of sodium nitrite (NaNO2) (30 μM). Allopurinol (100 μM), an inhibitor of xanthine oxidoreductase, was added both alone and in combination with NaNO2 to cells exposed to hypoxia. EV and cell preparations were quantified by nanoparticle tracking analysis and confirmed by electron microscopy. Western blotting and siRNA were used to confirm the role of HIF-1α and HIF-2α in EV biogenesis. Flow cytometry and time-resolved fluorescence were used to assess the surface and intravesicular protein content. Results Endothelial (HECV) cells exposed to hypoxia (1% O2) produced higher levels of EVs compared to cells exposed to normoxia. This increase was confirmed using the hypoxia-mimetic agent desferrioxamine. Treatment of cells with sodium nitrite (NaNO2) reduced the hypoxic enhancement of EV production. Treatment of cells with the xanthine oxidoreductase inhibitor allopurinol, in addition to NaNO2 attenuated the NaNO2-attributed suppression of hypoxia-mediated EV release. Transfection of cells with HIF-1α siRNA, but not HIF-2α siRNA, prior to hypoxic exposure prevented the enhancement of EV release. Conclusion These data provide evidence that hypoxia enhances the release of EVs in endothelial cells, and that this is mediated by HIF-1α, but not HIF-2α. Furthermore, the reduction of NO2− to NO via xanthine oxidoreductase during hypoxia appears to inhibit HIF-1α-mediated EV production

Frequency drift in MR spectroscopy at 3T

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B-0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.</p

Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems

Whilst the interest of many former studies on the application of AI in finance is solely on predicting market movements, trading practitioners are predominantly concerned about risk-adjusted performance. This paper provides new insights into improving the time-varying risk-adjusted performance of trading systems controlled by Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Systems (ANFIS) or Dynamic Evolving Neuro Fuzzy Systems (DENFIS). Contrary to most former studies which focus on daily predictions, we compare these models in an intraday stock trading scenario using high-frequency data. Firstly, we propose a dynamic extension of the popular moving average rule and enhance it with a model validation methodology using heat maps to analyse favourable profitability in specific holding time and signal regions. Secondly, we study the effect of realistic constraints such as transaction costs and intraday trading hours, which many existing approaches in the literature ignore. Thirdly, unlike most former studies that only aim to minimise statistical error measures, we compare this approach with financially more relevant risk-adjusted objective functions. To this end, we also consider an innovative ANFIS ensemble architecture which on an intraday level dynamically selects between different risk-adjusted models. Our study shows that accounting for transaction costs and the use of risk-return objective functions provide better results in out-of-sample tests. Overall, the ANN model is identified as a viable model, however ANFIS shows more stable time-varying performance across multiple market regimes. Moreover, we find that combining multiple risk-adjusted objective functions using an ANFIS ensemble yields promising results

Enhancing intraday trading performance of Neural Network using dynamic volatility clustering fuzzy filter

We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our algorithm automatically extracts fuzzy rules from past trades by taking into account the predicted return size and intraday time varying realized volatility, the latter used as a proxy for uncertainty. The model identifies unique intraday scenarios and subsequently creates a dynamic and visually apprehensible risk-return search space to control algorithmic trading decisions. Our results show that this method can be successfully applied to support high-frequency intraday trading strategies, outperforming both standard NN and buy-and-hold models

State of the Environment Report for Malta 2002

One of the very first paragraphs of the final chapter of the report, which I am commending to everyone’s attention, says that the “institutional development” by which the Environment has been brought under my tutelage “has led to various reactions from the public”. Not a whiff of justificatory comment on the move follows. That is the ideally objective and scientific spirit in which the environmental balance sheet of our country has been correctly couched. A reader of the WWF report on the Planet (written in preparation for the UN World Summit to open in Johannesburg on the 26th August) cannot help being struck by its contrastingly apocalyptic tone: it calculates that in just 50 years time, humankind may be forced to emigrate to some other planet, if it wants to survive. Two observations suggest themselves. On the one hand, a comparison of the figures and facts in the two reports indeed show that environmentally Malta is doing much better than the world as a whole – although that is a very relative judgment. On the other hand, because the environment is just one for the whole planet, it is in our interest, as well as our duty, to do our utmost to ensure an optimal outcome at Johannesburg. It is striking that the key concept emerging as central to the earth Summit is an unfortunately somewhat debased derivative of the Maltese concept of the “common heritage of mankind” and that the absolutely vital importance for the environmental future, especially in the context of climate change, of the Oceans (to which the Maltese concept was first applied) is being universally acknowledged. The reader of the Report will also be struck by the rare occurrence of such sentences as this: “Malta is currently transposing several items of EU legislation dealing with solid waste management into national legislation” (p575). Nobody will be unhappy at that – since solid waste disposal has been perhaps our most intractable environmental problem for years. In our negotiations with the EU, we have been striving hard, on the one hand, to get the proof that, even in the environmental area, the EU respects the great ecological value of diversity and individual, historically, geographically and culturally conditioned identity. However, there can be no doubt, that membership of the European Union will be a great boon for the Maltese environment both in its individuality and in the global context. The Report is written in the language of experts who speak of the situation with independent and non-political eyes. Other authorities may not agree completely with all their statements. But the Government as a whole will certainly use it as a management tool. So too, I am sure, will all the relevant, competent authorities. [preface]peer-reviewe

Confirmation of temperature independence in the fluorescence lifetime of the 3P0 → 3F2 transition in praseodymium-doped fluoride glass

The dependence of the fluorescence lifetime from the 3P0 → 3F2 transition in praseodymium-doped fluoride glass as a function of dopant concentration and temperature was investigated. It was found that the fluorescence lifetime at the concentration of 7000 ppm was constant with temperature, confirming the prediction of temperature independence in the lifetime for this transition in Pr3+-doped ZBLAN glass