SCAN-Interfax helps to assess reputational risks by providing access to over 40000 media sources in Russia and the CIS.
Access to an adverse media platform can uncover allegations of fraud, corruption or malpractice within an organisation, preventing irreparable damage to the reputation of businesses and the individuals associated with it.
Mitigating the exposure of your company and brand to reputational risk is vital when it comes to protecting your most valuable asset. A prestigious reputation takes years to build but can be destroyed in a matter of minutes.
The SCAN system for complex media analysis gives you access to over 60 000 news sources from Russia and the CIS countries, allowing you to monitor and assess any reputational risk that may pose a danger to your business. The archive goes back to 1989.
SCAN will gather data about borrowers and counterparties from the mass media, social media and official sources to enable you to supplement your company checks with important information.
Thanks to integration with the SPARK-Interfax counterparty verification system, SCAN searches for mentions of companies by TIN (and other registration codes), although TINs do not appear in new items. To improve determination accuracy, the system takes into account the region, industry, management and other parameters from company cards.
Thus, you only enter the TIN or load a list of counterparties, and SCAN returns news about them with total accuracy and fullness.
Thanks to natural language processing (NLP) and machine learning (ML) technologies, we are able to achieve accuracy of more than 95% to identify legal entities and more than 98% to identify risk factors.
SCAN identifies 65 risk factors, including economic and criminal offenses, litigation and investigative actions against a legal entity, bank fraud, loss of earnings, labor conflict or consumer complaints.
Our team of linguists constantly develops and enhances rules that enable us to detect specific types of events and accurately determine their participants in context. This is done using morphological, syntactic and semantic analysis, as well as unique technology for identifying legal entities.