Big-Data Risk Management Solutions for Financial Institutions
G-STAT BRAINs™ predictive analysis and machine learning applications help financial institutions to deploy more credit, implement anti-money laundering and data security models, prevent more threats, and reduce credit and operational risks.
Banks, insurance companies, credit cards issuers and other financial institutions face the daily challenges of managing their credit and operational risks. G-STAT BRAINs platform helps them to streamline:
- Credit and collection risk management
- Anti money-laundering management
- Data security operation
- Fraud and embezzlement detection
Statistical-based credit and collection scoring are already common practices for many financial organizations. Yet effectively managing these practices is still a challenging task, which is time consuming and requires massive manual work by data scientists. This is why many organizations fall behind on updating models and credit policies according to changing market and business conditions. Risk BRAINs provides an end-to-end environment for rapid deployment of credit and collection scoring models, without the help of scientists.
With Risk BRAINs, financial organizations can now update 10 times as many risk models in 1/10 the time. For example, you can design different types of application/behavior credit scoring and collection models for different customers segments, such as retail, SME, Micro-finance, VIP, etc. The results are optimized, the credit decision process is more accurate, and the need for expensive data scientists is reduced as you develop, update and deploy these models.
To comply with financial regulations, most organizations implement an anti money-laundering threat-identification process. Common AML tools relay on business rule oriented systems. Use of Risk BRAINs machine learning algorithms increases detection of suspicious money-laundering operations by up to 40%, compared to business rule-based systems, while minimizing false-positive alerts.
Data leakage and e-crime events are a nightmare for senior management in any financial institution. Preventing these events currently relies at most institutions on insufficient business rule-based systems that define "known threats" while the "unknown" is a deep and dark ocean of threats. Fraud BRAINs changes the rules by employing a rule-free, multi-dimensional, machine-learning based behaviour anomaly detection strategy. It analyses the behaviour history of each entity (internal user, station, customer, etc.) to identify new, unknown threats. Fraud BRAINs adds an additional behaviour-analysis based line of defence to complement business rules systems used by the organization.
Using Fraud BRAINs anomaly detection capabilities for profiling complex and multi-dimensional behaviour trends in trading rooms, banks and investment houses can detect more suspicious events of internal fraud and embezzlement in advance, and reduce operational risks.
The common base for G-STAT BRAINs applications for risk management is that they enable the rapid deployment and update of predictive and machine learning based models, shortening time-to-results from many months to only days, and can be operated by non-data scientists. This leads to accurate prediction and anomaly detection, resulting in improved risk assessments and better risk management.