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Financial Crime Typologies

Recap 2020

  • Overall observed increase in Financial Crime, specifically third party fraud
  • COVID-19 caused new specific patterns to occur and fraud wrt to government aid packages to rise
  • Major Fraud Schemes observed remain unchanged (Impersonation Fraud, Marketplace Fraud, CEO/Love Scams)
  • Special Cases Trends observed indicate movement towards Crypto Currency

Focus Germany

  • Expectation of Financial Sector is an observed increase in reported crime and detected crime
  • Regulatory changes will lead to increase in Suspicious Activity Reports filed by Financial Institutions
  • Corona Aid Packages induced Fraud
  • Planned FATF

Fraud Modus Operandi

Example Network Analysis Marketplace


Money Mule Driven Fraud (e.g. Marketplace Fraud) has increased over the past years. Evident usage of organized crime networks. Fraud scales heavy. Indicative data points suggest concentration on location (e.g. West Africa and Parisian Suburbs) and/or nationality/document type)

Fraud is highly pattern driven. Meaning with the utilization of advanced data it is possible to build highly effective rules in Transaction Monitoring, up to 70-80% TP rates.

Type of Crime creates necessity to block transactions real-time, without real-time blocks funds are immediately moved away.

Case Study: Terrorism Financing

  • Well Known Public Figure
  • Denied any connection to such organisations.
  • The subject’s sent funds to a suspicious NGO, a well connected organisation which was recently raided against
  • Police raid on subject’s apartment on April 2019 due to suspected supporting terrorism financing (Salafist movements).
  • Runs his own Charity Organization
  • Bank details of this Charity Organization are stated openly on the NGO’s website.


Key to identifying terrorism financing is a combination of typology driven investigations, utilization of large-scale network analysis and customized monitoring rules

Current Trends observed indicate less “concrete” activity with a tendency to “affiliated” organizations, e.g. in Germany from Right-Wing Extremism to Corona Doubters/Critics

Traditional TF Rules remains highly ineffective and inaccurate, utilization of advanced data and models shows positive indication in identifying TF accounts.


The COVID-19 pandemic has had a significant impact on the serious and organised crime landscape in the EU. Criminals were quick to adapt illegal products, modi operandi and narratives in order to exploit the fear and anxieties of Europeans and to capitalise on the scarcity of some vital goods during the pandemic. While some criminal activities will or have returned to their pre-pandemic state, others will be fundamentally changed by the COVID-19 pandemic.


Corona Soforthilfe

“Corona Soforthilfe” is a financial support program provided by the German government, established to help freelancers and small businesses in the time of the Corona crisis.



Communication is key. If Financial Institutions had received instructions, risks could have been mitigated. A typology paper was released 3 months after 1st payment was issued. By then the typology was long outdated.

Cooperation is key. We observed with direct interaction with the state banks that it was possible to identify and detect fraudulent payments much more effective.

Combination of KYC & Transaction Monitoring is vital in handling crisis. We observed that available public records helped tremendously in identifying fraudulent applications for Corona Aid.

Crime remains on the rise. The possibility of the financial innovation utilized for crime remains untouched. Meaning: Criminals use the known “easy” methods, which are easy to detect. (Because they do not need to use anything else)

Available Mitigation Measure are at an all time high. The possibility for Financial Institutions to mitigate, identify and stop crime are endless. Many solutions and intelligence to known problems can help in identifying the crime in our platforms.

Good Financial Crime Prevention remains expensive and heavy resource dependent. The curve for the positive application of automation occurs only after heavy investment.