Artificial intelligence (AI) is transforming all aspects of the financial services industry. The technology has proven particularly effective in identifying financial crimes, expediting investigations, and recognizing criminals’ evolving schemes faster than ever before. The QuantaVerse Financial Crime Analysis Platform brings advanced technologies together to enable our specialized solutions for identifying and evaluating financial crime.
Our proven technology utilizes proprietary algorithms, or AI agents, to identify instances of financial crime. AI agents employ a variety of techniques such as fuzzy matching, graph traversal, criminality sentiment and more to generate observables indicating the risk potential of a transacting party in three areas:
- Entity Reputation. Detects instances of potential financial crime from structured and unstructured data to calculate an entity’s reputation risk.
- Transaction Monitoring. Detects instances of potential financial crime from transactional data such as SWIFT messages or other data sources that show an exchange of value.
- Intent. Detects instances of potential financial crime from behavioral data derived from transaction history, company records, and other sources.
Risk segmentation and scoring is a tremendous cognitive challenge for compliance teams. Our trained deep neural network produces incredibly detailed and accurate criminal sentiment around entities based on potential financial crime risk. Through our decisioning engine, entities’ transactions are marked as anomalous or non-anomalous and are given a mathematically-generated risk score and confidence level specific to clients’ risk tolerance.
AI and Machine Learning
Artificial intelligence is fully capable of enhancing human cognitive performance or even completely replacing people in the execution of non-routine tasks by enabling machines to emulate human intelligence processes.
Our platform employs supervised machine learning that leverages decades of insights and expertise from financial crime investigators, law enforcement and regulators. We also use unsupervised machine learning techniques to assess risk relevant to financial transactions and their related entities. For example, along with the billions of financial transactions that have taught the QuantaVerse platform, we have fed NACE Classification Codes into our neural network which has learned what types of businesses commonly transact with one another and the nature and volume of those interactions. Machine learning strategies like this make the QuantaVerse solutions exceptional at identifying anomalous behavior that is indicative of financial crime and would otherwise be missed.
As financial crime experts, QuantaVerse understands that decision-related documentation and reporting required by AML investigators and regulators raise important considerations where AI strategies are considered. QuantaVerse’s machine learning techniques replicate the same level of abstraction when explaining and documenting a suspicious case as a human investigator might. At QuantaVerse, each AI decision is accompanied by a confidence level, evidence, rationale for a regulatory violation, anomalies discovered, and, if required, the system can cite cases from which it learned and based its conclusions.
NLP & NLG
Natural Language Processing (NLP) is the ability of a computer to understand written text and derive intelligence that normally would require manual interpretation. QuantaVerse employs an NLP engine to analyze massive numbers of text-based documents from internal and external sources. By automating the review of emails for not only keywords related to suspicious payments but relationships between words, issues can be identified. The QuantaVerse NLP engine further distinguishes risk concerns by analyzing any combination of structured data sources such as travel and expense reports, contract language, proforma invoices, shipping documents and more.
In the field of adverse media, standard NLP algorithms can extract entity names from articles, but are unable to distinguish differences between types of entities. The QuantaVerse NLP is trained to infer criminal sentiment around entities, thereby differentiating between subject roles and relationships in Web text.
Conversely, Natural Language Generation (NLG) technology turns data into plain-language. QuantaVerse uses this technology to report on investigative findings just like a human investigator would. Critical information such as risk scores, negative news, sanctions data and financial crime typologies are automatically compiled in QuantaVerse Financial Crime Reports for investigators speedy review.
Structured and Unstructured Data
The QuantaVerse Financial Crime Analysis Platform is designed to examine very large volumes of internal and external, structured and unstructured data in order to identify the suspicious relationships and patterns that can indicate money laundering and other financial crime activity.
Our proprietary algorithms ingest and process data from a wide variety of external sources, many of which go unassessed by organizations today. These data sources include unindexed deep web data; open source public internet data; and government and commercially produced datasets on known financial criminals and other prohibited or high-risk persons or entities.
We also analyze transaction and customer information from disparate operational systems such as KYC platforms to create an unparalleled understanding of legitimate patterns and quickly identify anomalies that are indicative of illicit activities.
Ultimately, QuantaVerse AI agents query and cross-check multitudes of sources to calculate an entity’s reputational risk and present a comprehensive understanding of how that risk was derived.
Learn More About QuantaVerse AI Solutions
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