Analytical. Advanced. Specialized. Rigorous.
We develop sophisticated analytical models for Financial Crime Risk Management (FCRM). We apply advanced Artificial Intelligence (AI) or Machine Learning (ML) technologies. We provide specialized validation of Anti-Money Laundering (AML) solutions. We follow rigorous standards of the OCC Model Risk Management (MRM).
Celebrating 15 years of service!
Thanks to our great customers, partners, and our excellent and dedicated team, Exprentis is very fortunate to celebrate its 15th year of operations. This has been an exciting and extremely rewarding journey, especially for those of us with a background in AI and ML. We’ve had a chance to witness firsthand the evolution and advancement of AI/ML technologies from academic projects noticed by few in the industry to the must-have components of modern platforms supporting the complex tasks of FCRM, KYC, AML, or MRM.
Working with our customers and partners, the Exprentis team has made significant and recognizable contributions to the development of regulatory technology and best practices.
Work with us. You’ll be in good company.
Would your models pass a regulatory exam? Do they satisfy your risk tolerance? We apply statistical inference and unsupervised machine learning methods to align regulatory models with your company’s risk policies. Tune your models for efficient operations, and for your peace of mind.
How sure are you that your name screening models don’t miss sanctioned entities? False hits are visible, so tuning methods can be easily applied. We also developed specialized tests to identify those near-misses. since they pose reputational risk and potential exposure to significant fines.
Are your alert investigations time-consuming due to party relationship checks? Detection and risk models emphasize counts and amounts as primary decision factors, but the needs are bigger. We use graph and unstructured data technologies to augment investigations with better relationship risk assessments.
Do you understand and accept the risk emerging from all of those analytical components? Regulators demand that you understand all model risks and stay in full control of model operations. We use the modern Enterprise Knowledge Graph technologies and advanced quant methods to control model risks.
Maximize Compliance. Minimize Risk.
Exprentis continues delivery of its core analytical services with a steady pace, and with a sharp focus on the quality and timeliness of contracted projects.
As experienced in 2018, the 2019 primary demand for analytical services will continue to be related to customer risk and financial crime risk scoring models. The name matching models for sanction screening are also catching up with the model risk management requirements creating needs for advanced analytical services. Behavior detection models seem to be entering a stability phase where ongoing model monitoring and tuning play a key role. Read more
Exprentis team members actively participate in industry events leading the best practices and new compliance technologies.
The Exprentis technology exhibits take place at the annual ABA/ABA Financial Crime Enforcement conferences, and at the ICBA National Conventions. Exprentis introduced its AI/ML-based Resolvian technology conducting demos and pilots. Exprentis is a member of the Enterprise Data Management (EDM) Council and participates in development efforts of the Financial Industry Business Ontology (FIBO). The Resolvian technology presentations were given to OCC and CFTC. Read more
The Exprentis team’s experience and its broad knowledge of FCRM is captured into Resolvian operational knowledge bases.
Resolvian is a generic problem resolution engine that supports evidence-based reasoning and machine learning. Resolvian uses AI, knowledge graphs, and semantic inference integrated with ML computational and predictive algorithms. Resolvian makes deductive and predictive decisions about gathered evidence. Resolvian can be configured as specialized intelligent assistants that can help with financial crime investigations and model risk management. Read more