In the rapidly evolving digital era, industries increasingly harness technology to optimize operations. The banking sector, in particular, stands out as a prominent example, having integrated Artificial Intelligence (AI) to streamline processes and improve efficiency. Our study focuses on one key aspect: automating loan management, specifically through Intelligent Document Processing (IDP). While automation technologies have been widely studied, a notable gap exists in sector-specific knowledge, especially within the banking industry. This paper conducts a Systematic Literature Review (SLR), examining 48 primary studies, to analyze the state-of-the-art of this problem. This comprehensive analysis reveals how IDP reshapes banking processes, providing sector-specific insights. Our findings reveal the profound impact of automation in banking, along with 8 notable challenges that remain to be addressed. This contributes to future research and enriches our understanding of IDP’s current and potential applications in the sector.

Robotic Process Automation (RPA) enables subject matter experts to use the graphical user interface as a means to automate and integrate systems. This is a fast method to automate repetitive, mundane tasks. To avoid constructing a software robot from scratch, Task Mining approaches can be used to monitor human behavior through a series of timestamped events, such as mouse clicks and keystrokes. From a so-called User Interface log (UI Log), it is possible to automatically discover the process model behind this behavior. However, when the discovered process model shows different process variants, it is hard to determine what drives a human’s decision to execute one variant over the other. Existing approaches do analyze the UI Log in search for the underlying rules, but neglect what can be seen on the screen. As a result, a major part of the human decision-making remains hidden. To address this gap, this paper describes a Task Mining framework that uses the screenshot of each event in the UI Log as an additional source of information. From such an enriched UI Log, by using image-processing techniques and Machine Learning algorithms, a decision tree is created, which offers a more complete explanation of the human decision-making process. The presented framework can express the decision tree graphically, explicitly identifying which elements in the screenshots are relevant to make the decision. The framework has been evaluated through a case study that involves a process with real-life screenshots. The results indicate a satisfactorily high accuracy of the overall approach, even if a small UI Log is used. The evaluation also identifies challenges for applying the framework in a real-life setting when a high density of interface elements is present.

Public funding for information and communication technology (ICT) innovation in Spain appears to be slow, bureaucratic, highly restrictive, and not agile. Therefore, the innovation process is negatively affected. These restrictions could be attributed to inadequate trust from public funders toward executors and ontological problems regarding the definitions of ICT innovation (i.e., the I+D+i formula), affecting all Quadruple Helix stakeholders. In this study, a Delphi study was proposed to reach a consensus among 81 experts (i.e., innovation managers, public funders, and consultants) to validate this hypothesis. The study included 41 statements and 59 questions organized into the following five objectives: (1) concept of innovation, (2) public funding and its restrictions, (3) theoretical model of innovation, (4) public funders’ trust and executors’ freedom, (5) assessment of capabilities and maturity for innovation. The experts discussed, evaluated, and reached a consensus, after two rounds, on 52 of the 59 questions. The results revealed wide dispersion of the proposed ICT innovation questions. They demonstrated that the innovation management ecosystem in Spain’s ICT context is immature and the I+D+i formula did not represent the innovation process. The study reached a consensus on requirements for an Agile Innovation Funding Framework (AIFF) oriented toward obtaining an improved competitive advantage for ICT products or services based on trust, transparency, inspection, and adaptation principles. The results revealed that a joint framework involving public funders and executors based on organizational capability and maturity positively affects the innovation process. The capabilities of the executors should be standardized and measured, and public funders must move from supervisors to mentors to acquire new capabilities. Furthermore, innovation regulation and the various types of calls for proposals should be analyzed globally to change their fiscal and controlling nature restricting innovation.

Abstract The robotic process automation (RPA) paradigm is a discipline that is becoming increasingly popular thanks to the great interest shown by the industry. In such context, RPA solutions based on artificial intelligence, i.e., cognitive solutions, are receiving increasing attention. In a cognitive RPA project, the RPA developer is in charge of selecting the most suitable components that solve specific tasks from the sets of components provided by different RPA platforms. This selection is very challenging, especially since there is no homogeneity in component names or component classifications. Such a situation turns an RPA project’s development into a time-consuming, error-prone, and very tedious process. Therefore, supporting the RPA developer in developing a cognitive RPA project is desired. The industry has also pointed out this need. This work presents a proposal for supporting the users in developing a cognitive RPA project. To be more precise, an incremental method to automatically generate taxonomies from cognitive RPA platforms is proposed. Such taxonomies can be dynamically adapted when necessary. In previous work, the initial aspects of this research were presented. However, the current work greatly enhances such previous work by: (1) extending the proposed method to improve the management of real-world use cases from industry, (2) developing a proof-of-concept tool that is based on the proposed approach, (3) validating the proposed method by applying it to real-world use cases from industry, and (4) performing a literature review on related topics. The results obtained are auspicious and demonstrate that the proposed approach substantially improves the support given to users during the development of a cognitive RPA project.

Robotic process automation (RPA) has received increasing attention in recent years. It enables task automation by software components, which interact with user interfaces in a similar way to that of humans. An RPA project life cycle is closely resembling a software project one. However, in certain contexts (e.g., business process outsourcing), a testing environment is not always available. Thus, deploying the robots in the production environment entails high risk. To mitigate it, an innovative approach to automatically generate a testing environment and a test suite for an RPA project is presented. The activities of the humans whose processes are to be robotized are monitored and a UI log is confirmed. On one side, the test environment is generated as a fake application, which mimics the real environment by leveraging the UI log information. The control flow of the application is governed by an invisible control layer that decides which image to show depending on the interface actions that it receives. On the other side, the test case checks whether the robot can reproduce the behaviour of the UI log. Promising results were obtained and a number of limitations were identified such that it may be applied in more realistic domains.

Business process analysis is a key factor in the lifecycle of Robotic Process Automation. Currently, task mining techniques provide mechanisms to analyze information about the process tasks to be automated, e.g., identify repetitive tasks or process variations. Existing proposals mainly rely on the user interactions with the UIs of the system (i.e., keyboard and mouse level) and information that can be gathered from them (e.g., the window name) which is stored in a UI event log. In some contexts, the latter information is limited because the system is accessed through virtualized environments (e.g., Citrix or Teamviewer). Other approaches extend the UI Log, including screenshots to address this issue. Regardless of the context, the aim is to store as much information as possible in the UI Log so that is can be analyzed later on, e.g., by extracting features from the screenshots. This amount of information can introduce much noise in the log that messes up what is relevant to the process. To amend this, the current approach proposes a method to include a gaze analyzer, which helps to identify which is process-relevant information between all the information. More precisely, the proposal extends the UI Log definition with the attention change level, which records when the user’s attention changes from one element on the screen to another. This paper sets the research settings for the approach and enumerates the future steps to conduct it.

Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct.

Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. \nHowever, developing a ML project entails the effort of many ML experts who have to select and configure \nthe appropriate algorithm to process the data to learn from, between other things. Since there exist thousands \nof algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to \nprovide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute \nforce procedures to try different algorithms or configuration and select the one which gives better results. \nTo make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this \npaper proposes (1) an approach towards a common language to consolidate the current distributed knowledge \nsources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this \nlanguage in a unified store that can be exploited later on. The preliminary evaluations of this approach allow \nto create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research \nlines to conduct.

The use of prototypes as an excellent mechanism for communication between software users has been fully accepted in the literature. Both the academic and business worlds agree on its use as a software technique, used primarily to capture requirements and as a means of communication with the user. However, often, prototypes are developed very quickly or without the collaboration of the users. This is one of the main reasons why the real power of the prototypes is not used in software development. The initial hypothesis of this research is that this problem occurs because prototypes are regarded as disposable milestones. This paper analyzes whether there are adequate tools on the market to help development teams to reuse prototypes as an exceedingly mechanism for starting and expediting new software development projects. With the present study we show different tools for prototypes that, even though they are a preliminary evaluation, show the disadvantages to be solved in the future development and research of solutions.

Assisted reproduction has become a service that more and more people access. Current problems such as the delay in the age of motherhood, single-parent couples, etc. they have proliferated the options and the different treatments that are put at the service of society. A fundamental part of these processes lies in the work of laboratories, in the samples that are handled in clinical processes and then be implanted in future mothers. The management of the samples is a critical aspect that requires all the opportune mechanisms that guarantee the traceability of said samples, avoiding fatal errors. The correct identification, monitoring and control of them is a fundamental aspect and of special relevance. However, the systems currently offered to clinics present important problems. On the one hand, they offer little security, are very expensive or very independent of a specific provider, so that the traceability system cannot be connected to the hospital central management system, or they are very intrusive control systems in the daily work of the laboratory technicians. In this paper a software solution based on the definition of automatic models and protocols is proposed. It includes the appropriate devices, to manage the traceability of the samples in parallel to the work of the laboratory technician.

Entity reconciliation (ER) aims to combine data from different sources for a unified vision. The management of large volumes of data has given rise to significant challenges to the ER problem due to facts such as data becoming more unstructured, unclean, and incomplete or the existence of many datasets that store information about the same topic. Testing the applications that implement the ER problem is crucial to ensure both the correctness of the reconciliation process and the quality of the reconciled data. This paper presents an approach based on model-driven engineering that allows the creation of test models for the early integration testing of ER applications, contributing in three main aspects: the description of the elements of the proposed framework, the definition of the testing model, and the validation of the proposal through two real-world case studies. This validation verifies that the early integration testing of the ER application is capable of detecting a series of deficiencies, which a priori are not known and that will help to improve the final result that the ER application offers.

Today's world economic situation is ruled by issues such as reducing cost, improving quality, maximizing profit and improving and optimizing production processes at any kind of organization. In this context, BPM (Business Processes Management) can be an essential strategy. However, there are business situations where is necessary to define innovation solutions to apply BPM in a proper way (e.g., needs of interoperability between process modeling languages, mechanisms to generate the executable model of the process maintaining the traceability with the definition model of the process, mechanisms to manage the measurement of processes from early stages, etc.). This paper describes and analysis some of these situations and proposes a framework based on MDE (Model-Driven Engineering) to support these needs. This MDE-based framework is also supported by the EMPOWER platform which has been developed in an innovation project carried out by Servinform Company and our research group (IWT2, University of Seville, Spain). This platform allows applying advanced MDE techniques to improve the application of BPM in business environments.

Within the development of software systems, the development of web applications may be one of the most widespread at present due to the great number of advantages they provide such as: multiplatform, speed of access or the not requiring extremely powerful hardware among others. The fact that so many web applications are being developed, makes grotesque the volume of information that it is generated daily. In the management of all this information, it appears the entity reconciliation problem, which is to identify objects referring to the same real-world entity. This paper proposes to give a solution to this problem through a web perspective. To this end, the NDT methodology has been taken as a reference and has been extended adding new activities, artefacts and documents to cover this problem.

REP (Requirements Engineering Process) is one of the most essential processes within the software project life cycle because it allows describing software product requirements. This specification should be as consistent as possible to enable estimating in a suitable manner the effort required to obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed and heterogeneous projects with multiple analyst teams and high integration among functional modules. This paper presents an approach for the systematic conciliation of functional requirements in big projects dealing with a modelbased approach. It also explains how this approach may be implemented in the context of NDT (Navigational Development Techniques) methodology and finally, it describes a preliminary evaluation of our proposal in CALIPSOneo project by analyzing the improvements obtained with our approach.