# STALITA: Innovative Platform for Bank Transactions Analysis
## 1 Introduction  
Nowadays, cyber security and digital forensics are the foundations for ensuring security for all digital flows of information. Both main disciplines can be divided into smaller, more specific fields. The discipline of digital forensics encompasses many different areas, including computer forensics, network forensics, mobile forensics, IoT forensics, and malware forensics [1]. The emerging sub-discipline of digital forensics also covers financial technologies, which include different money activities such as payments, fund transfers, and other financial transactions. As they are intended for a wide range of users, they include those with good and bad intentions. The latter are leveraging financial transactions for fraud, extortion, money laundering, and other financial activities in the criminal underground. Those incidents may use and abuse the traditional financial infrastructure, online and mobile payment systems, or independently distributed crypto currency systems [1].  
With the daily increase of different digital frauds, especially those connected to financial fraud, there is a need for more effective tools and approaches to prevent them. Furthermore, there is also a need for fraud investigators. Recently, many traditional digital forensic investigators have had to pay more attention to financial investigations. To fill the gap and facilitate work for investigators, this paper presents STALITA, an innovative platform for the analysis of bank transactions. STALITA enables the import of large-scale heterogeneous data into a complex network represented by a Neo4j graph-based database [2]. Due to data sensitivity, it enables independent transaction inspections. Graph data and their topological properties are analysed using various graph algorithms supported by Neo4j and third-party libraries, while custom-built graph-based visual analytics allow for more in-depth investigations and the discovery of new knowledge.  
Furthermore, the Law Enforcement Agency (LEA) struggles to find useful information in large amounts of heterogeneous data that can be crucial for investigations. In doing so, investigators face the following challenges:* Most of the investigations in the LEA are related to large amounts of electronic data that must be analysed;
* Most of these data are in an unstructured or semi-structured form and various formats that require the use of special domain-specific tools for data gathering, cleaning, and parsing;
* The data can include crucial information for the investigation, but the ratio between useful information in these data and all available data is very low. Therefore, LEAs need efficient tools for knowledge discovery in data;
* Almost each investigation requires a specific domain-specific approach, including various data analysis techniques and tools.  
Thus, LEAs need efficient, user-friendly analytical platforms, including basic and advanced techniques, for the discovery of useful information in a large amount of heterogeneous data related to investigations.  
A new platform (STALITA) for bank transaction analysis is presented in this paper, which supports heterogeneous data sources, most of the advanced techniques for graph analysis due to the use of Neo4j technology, various types of machine learning and graph-based algorithms that can be incorporated for financial fraud investigation, and, finally, various visual analytics tools. The platform supports collaborative and uniform workflow on diverse data sources through web-based applications, user-friendly extensions (template queries) for domain-specific analyses, and a high level of customisability of visual analytics tools.  
The rest of the paper is organised as follows: The related work is explained in the next section. Section 3 provides the methodology used in the proposed paper. The results achieved with the proposed platform are presented in Section 4. Section 5 concludes the paper.