Fraud and risks
Models to analyze massive data sets, identify fraud patterns, and flag potential fraudulent transactions in real time.
Risk analysis
By analyzing large amounts of data, a certain risk can be quantified (loans, insurance, accidents, etc.)
Cyber-threat detection
With classification models, anomaly detection, data analysis to identify threats, etc. potential cyber threats can be detected.
Claims fraud detection
With existing fraud patterns, insurance claim fraud is detected and true transactions are identified.
Suppier risk model
Based on data from suppliers and their qualifications, their potential failures are evaluated.
Application / account opening fraud
Techniques such as neural networks, decision trees, etc. they can be used to trace false documents, non-existent companies, false invoices, etc.
Check fraud
Based on data and images of clients, the company, etc. and with predictive models important variations that deserve extra revisions can be detected.
Collusion fraud
Through the analysis of activities and transaction data, patterns that generate suspicion of collusion can be defined.
Transaction fraud
In large data sets, abnormal patterns can be detected in agents that allow defining new forms of fraud to establish countermeasures.
Anti-money laundering
In this case we use AI to detect abnormal patterns in behavior, quantities, values, etc.
Accounting and auditing
Through AI, it is possible, with defined patterns, to maintain a continuous audit that allows transactions and balances to be monitored.
