FinCrimes.Ai

Identity Verification (KYC): We use state-of-art machine learning facial recognition models to predict client gender, age and detect fake photo for rapid onboarding screening. Based on model probabilities, we automatically estimate the similarity score between persons recent photos and identity card / passport.

We propose a new AI system trained to understand the regulatory process and analyze financial crime compliance risk. It ingests millions of unstructured data and extract relevant articles, automatically detect red flags, process crime sentiment analysis to describe the profile of the client’s reputational risk in the way to ensure KYC and Anti-Money Laundering (AML) /CTF onboarding process.

Our name-matching algorithm effectively manages real time name matching inside international various lists of financial criminals such as : Terrorism Financing, Politically Exposed Persons, Sanctions Embargoes, FBI, Interpol, OFAC, European Union Sanctions Lists and Watchlists.

The AI search engine inside our financial intelligence system is trained to search customer investment behaviour and detects money laundering activities. Its retrieved entity's name related to politically exposed persons, find all entity’s manager names and automatically geolocate them on maps.

Our AI consists of an active annotation and knowledge extraction learning systems that processes unstructured data. It reviews contracts by analysing the links between entities, individuals and countries and assesses the country risk and sanctions, geographical risk, trade based AML/TF transaction risk.

Powering multi-factor transaction monitoring. Our machine learning automatically detects fraud, Anti-Money Laundering Red Flags and Analyze customer financial crime risk profile. 360° money laundering risk view, the model recognizes country risk and offshore entities, corporate transparency and transaction network risk.

We have a search engine trained to detects investments in offshore and tax evasion countries, establish the link between entities and persons to highlight money laundering activities and geolocate them.

This system is a complex network risk-based model, a self learning AI builds to analyze unstructured data. It detects the links between entities and individuals, highlight customer's network risk and fight against the corporate transparency risk.

Our terrorism financing screening system is efficiently built to investigate financial criminal across all international financial crime databases, list of persons, groups and entities involved in terrorist acts and subject to restrictive measures.