FIFI - Project Presentation
Researchers - Users and Industry Have Formed a Target-Oriented Joint Venture

  Video-Presentation - Associated Partnership [20 Minuts- German]


Global supply chains are threatened by illegal activities. Fraud, theft, smuggling, brand piracy, industrial espionage and compliance are challenges for industries and trade. FIFI is intended to deal with open information and internal company data by means of information processing, fusion and artificial intelligence (AI) in order to detect crime.


Globally active companies are worldwide present and connected. Production, logistics, purchasing, sales and markets form a network across national borders. Fraud, theft, smuggling, brand piracy, industrial espionage and compliance threaten industry and their value chains. Illegal activities occur outside and also inside globally operating companies. Damage minimization is the goal of clarification and countermeasures and is the reason for the need for action.

National security agencies cannot prevent these incidents. In principle, necessary cross-border networking and coordinated measures do not take place. Reporting channels, interests, confidentiality, public relations and options for action are defined completely differently in governmental authorities than in industry.

FIFI strengthens the corporate capabilities in the fight against crime, threats and illegal trade. Relevant data and information from a wide range of international sources are collected, evaluated and fused. The effective and efficient processing of this mass data is only possible with the use of AI procedures and extensive automation in information preparation. The project combines research, user knowledge and market-oriented young companies with the aim of providing practice-oriented solutions for suitable assistance systems.

Associated partners with specific focuses and interests are welcome!

The Fraunhofer Institute FKIE, DITS, Analytical Semantics and Traversalsals would like to thank the initiative of Philip Morris International to support the project in the following aspects: Combating illegal trade, together. A global initiative to support projects against illegal trade and related crimes.

Paradigmatic for FIFI are two assumptions:
  • that the early detection of crime is possible with good prospects of success if data and information from all legally accessible sources is suitably processed, merged and aggregated;
  • that proven approaches as well as procedures and tools used by security authorities, for example the methodology of the so-called Intelligence Cycle, can be transferred to project-relevant issues.
The starting point is always a concrete, relevant management question in our case concerning the illegal trade and crime in the business environment.

This is followed by the collection of relevant actual data and information from sources of different origin, type and structure:
  • Data from the company describe the business processes;
  • Press, media, open social networks, the Internet and Darknet characterize the corporate environment and reflect activities outside the company;
  • Politics, market participants, customers and committed contributors provide additional relevant information. The result is topic-relevant, but mostly unstructured mass data.

An important element in the processing chain is the machine translation (MT) of compiled information in text form. In addition to overcoming the skills bottleneck, MT promotes speed and reproducibility and reduces costs when evaluating multilingual sources. By means of various artificial intelligence-based tools (AI), the collected information is processed and classified. Available tools and research results are used for information processing, content indexing and categorization - partly "open source", partly developed and provided by the FIFI consortium.

The collection according to the research question provides mass data that is relatively unspecific in terms of the mission. For each report, AI-based models are used for filtering, prioritization and ranking for data reduction and fusion. Measures and assessments by the analyst support the regular improvement of the models.

The actual analysis takes place on the fully automatically collected, pre-processed and categorized data set. Tools are used to uncover relations, temporal and content-related correlations, changes, trends, anomalies and warnings. The analyst examines data and information. Corrections to the proposed priorities also allow the adaptation of report-specific models for data processing. Relevant information is included in the analysis.

The cycle is completed with the report and its presentation to the principal. The discussion and evaluation of the results provides refinements and adjustments for up-to-date reports of following cycles on unchanged or extended questions.

FIFI - Innovation and Business Intelligence

Innovation stands for a new product and for the fact that the market does not provide such solutions. The project pursues approaches that are not yet commercially available today.

FIFI provides innovative elements in the sub-steps of planning, collection, processing, analysis and dissemination of the aggregated information in report form. The partners have relevant experience and are providing this in the project.

We take these aspects into account and would like to discuss important details with the user and include them in the context of requirements that arise in particular from globalization.

Fight against Fraud and Illegal Trade

We thank the initiative of Philip Morris International, to significantly support the project in the spirit of the following: Combating illegal trade, together. A global initiative to support projects against illegal trade and related crimes.

 

FIFI Publication and Use Cases
Experts present use cases


As part of the 2-year research project, DITS experts addressed various issues. Experiences and findings were also presented at DITS 15/15 online events. The contributions were published in book form. This book is available for purchase. We are happy to receive any orders. You also support the work of our initiative.

Intro Wednesday, 14.07.2021 - 16:00 to 17:30
Prof. Dr. Elmar Nöth et.al.

Semantic Analysis of Spoken and Written Documents with Deep Learning and Linguistic Analysis Methods
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One approach for the early detection of crime is the targeted collection, processing, fusion and aggregation of all legally accessible data and information, the majority of which is available as unstructured, multilingual speech and text documents.
Automation is essential for the efficient processing of such mass data. Modern machine learning methods allow the automatic analysis of large collections of linguistic documents. In the past, this was mostly done in a knowledge-based way, whereas nowadays deep neural networks are almost exclusively used for analysis. In this paper, we discuss the approach to linguistic retrieval of linguistic documents using machine learning methods.

Intro Prof. Dr. Prof. h.c. Arndt Sinn

Threats to the Supply Chain from (Organized) Crime - New Aspects from a Supply Chain Law?
Protecting the supply chain is in the interest of both the business owner and society as a whole. Only a secure supply chain guarantees that goods and services reach consumers, production facilities do not come to a standstill and jobs are secured. Reinvestment of profits creates new jobs, innovation and prosperity. But security goes beyond the functioning of production processes, because the product or service must also be safe. Security in a supply chain also includes security against crime. A large part of criminally generated profits is obtained by (organized) crime along the value chain.

Intro 04.03.2021
Col. (ret.) Franz Berger et.al.

Anomaly Detection in Supply Chains - Using "Cargo Theft" as an Example
Global value chains are threatened by illegal activities, and globally active companies are affected in particular. In many cases, however, the respective threat manifests itself on a small scale, across nations, and transcending national borders. Organized crime (OC) takes particular advantage of the current lack of cooperation in matters of security and law, and secures its own power externally through structural violence.

Intro Prof. Klaus Schmidt et.al.

Trade-Based Money Laundering - Risk Factors and Diligence Requirements
Money laundering is the smuggling of illegally generated funds into the legal financial and economic cycle. Preventing, detecting and combating it is a task for society as a whole and a multidisciplinary task for various state and non-state actors.
Drug trafficking, prostitution and human trafficking, illegal gambling and auctions, illegal trade in art goods, precious stones, precious metals and weapons, corruption, terrorist financing, illegal real estate trading, accounting fraud, insider trading and market manipulation represent, with reference to money laundering activities, some of the most important fields of activity of criminal and terrorist organizations.
A new development is the anonymous trading of virtual or so-called cryptocurrencies (Bitcoin, Ethereum, Ripple, etc.) by state and non-state actors, especially for the purpose of financing terrorism or undermining state extraterritorial sanctions.

Intro Thursday, 22.07.2021 - 16:00 to 17:30
Manfred Heer et.al.

Risks - Identification and Tracking - Using the Example of Product and Brand Piracy
Companies as well as organisations identify and analyse risks in order to enter the phase of risk management. The regular, global collection and evaluation of (mass) data, information, texts from internal or open sources by means of automated procedures provide the desired indications.
Organized crime (OC) has discovered product piracy as a business field. This causes massive damage to the national economy. The amount of damage, is very difficult to determine precisely. But according to the latest available data from the EUIPO (EU Intellectual Property Office) and the OECD from 2017, product copies/counterfeits worth around 450 billion Euros were on the market worldwide. According to estimates by the Chamber of Industry and Commerce, counterfeit products have already destroyed around 65,000 jobs in Germany alone.

 

Team - Research - Users - Industry
Application-oriented research is only successful in a team

The consortium combines research, state-of-the-art products, experience and interests for the sustainable use of innovative solutions in security organization.

The aim is to obtain reliable bases for decision-making in order to protect industrial companies as well as law and order and to ensure their continuing existence and success. Data and information from open sources, the Internet and also social networks are merged with own data and information from the business process. We conduct research on the fundamentals of routine, automatic fusion and analysis of data and information with the aim of detecting anomalies.

Questions from the company's management board are analyzed in reports in a timely manner and a basis for decision-making is created. We take into account the transfer of research results into a permanent and sustainable utilization by the user.

The team brings together research - users and industry

 

Demonstrator
Ein cloud-basierter SaaS Demonstrator erlaubt die Erprobung

FIFI Demo Traversals

Traversals is responsible for integrating the research and development results into a demonstrator. In addition to the funding, own financial resources are used to a considerable extent.

Federated Search

The Federated Search extends the reach of Traversals by seamlessly integrating open and dark web resources together with your organization's proprietary data systems. With a single search API, OSINT in the data sources can be searched with Traversals' full computing and analysis power. Records of interest can be promoted and merged with Traversals' Enterprise Knowledge Graph while respecting the origin and security of the data.

Access Multiple Data Sources with One Federated Search Call

Federated Search integrates classical search engines, such as Google or Yandex, social media, such as Twitter, and non-classical data sources known from the open-source intelligence (OSINT) sector. The powerful search language allows text-based, semantic and geo queries against all data sources with only one call.

Run one Query and Understand Multilingual Information

Analysts using Federated Search benefit from the deep integration of state-of-the-art AI algorithms. An AI-based keywords optimization is carried out before each search call. The list of keywords is extended, e.g. by synonyms, to get a more penetrating search depth. Multilingual search results are post-processed using NLP so that they can be understood by analysts without having expertise in the given language.

Rank Information in Your Context as Part of the Search

The Federated Search applies a context-specific ranking algorithm to all collected information including their NLP-based metadata. Analysts benefit from the fact that the most important information appears at the top. Big Data becomes Smart Data which results in massive time saving for investigations and associated cost reductions for the organizations.

AI + Human-in-the-Loop

Federated Search plays a significant role in Traversals' Intelligence Platform. It is an integral part of manual deep-dive searches and also a core element for repetitive and automated searches used for constant updates on a certain topic.


 

Downloads and FAQs

Downloads


Frequently Asked Questions

Critical importance of technology in combatting illicit trade nowadays - can you even fight it properly without the proper tools?

How much of a difference does it make?

  • We can use the technologies available today to increase effectiveness and efficiency.
  • Technical solutions allow us to deal with mass data and generate progress in combination with the cognitive abilities of the analyst.
  • Reduced costs today allow broad deployment beyond government applications.
  • Not to take advantage of these opportunities is doomed to failure and will result in disadvantages for a companys global competitiveness.
Which types of criminality can technology help fight and which require "old school" approaches?
  • Fight against any type of crime benefits from a comprehensive information base.
  • Digitalisation and artificial intelligence help to objectively process mass data and generate a basis for decision-making.
  • But "old school" is still in place.
  • Technology provides critical support, but will not replace competent leadership and decision-making abilities.
Pitfalls and problems in the development and implementation of changed paradigms - lessons we already know and need to be learned also in the future.
  • Structured decision-making processes have been developed, described and applied in governmental and military organizations.
  • Civilian organizations are not really familiar with such principles.
  • It is a great challenge to structure decision making and leadership and to place best practices above intuitive decision-making processes.
  • We are working to integrate information processing into decision-making processes without questioning the significance, competence and capability of leadership.
Reduction of the influence of unimportant data and noise on a case-by-case basis.

  • Prevent data overload and promote the extraction of desired information and trends from all available data and information.
  • Data and information that are insignificant in a case are called noise by technicians.
  • Distinguishing relevant information from noise is a challenging task.
  • Human analysts have the experience and knowledge but not the time to do the job, entirely.
  • Mass data can only be evaluated accordingly using technical algorithms.
  • Artificial intelligence and other methods of automatic information processing help and support human analysts.
Private data collation and analysis platforms - viable alternative to state-developed tools used by intelligence services?
  • Competition or complementarity?
  • State institutions have different objectives and tasks compared to industrial institutions.
  • The industry pursues the preservation of its own existence, the enterprise value and the avoidance of risks as central goals.
  • The industry must include the processing of information as an important and central process in the management.
  • This applies in particular to all tasks in the area of corporate security and the avoidance of risks from illegal trade.
  • The coordination of public and private security concepts would be highly desirable and has to be communicated to all parties involved as a significant advantage.

 

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