The Multifaceted Challenges Faced by Financial Institutions
The deployment of AI/ML for fraud prevention and risk mitigation creates a set of different challenges for Financial Institutions (FIs):
- Model Development and Integrity: Crafting, serving, and monitoring models of production caliber demands diligence and expertise, as inaccuracies can lead to consequences such as financial losses or a poor experience for customers.
- Explainability and Model Governance: As the decisions engendered by ML models play a pivotal role in financial transactions, understanding their underlying mechanisms becomes vital. Transparent and interpretable models are fundamental for compliance and risk mitigation.
- Real-Time Decisioning: The obsolescence of batch processing has engendered the requirement for instantaneous processing to facilitate seamless user experiences. Today, Customers expect fast and smooth experiences.
Powerful ML-driven fraud prevention: Seldon and Effectiv AI
Effectiv and Seldon enable real-time decision-making for FIs
Effectiv AI is an end-to-end fraud and risk management platform, enabling risk teams to manage sophisticated real-time fraud and AML strategies by utilizing data and machine learning. Seldon is a deployment solution that helps teams serve, monitor, explain, and manage their ML models in production. Together, Seldon and Effectiv work hand in hand to enable financial institutions to adapt AI to prevent their institution and customers at scale by dramatically accelerating the development and deployment of real-time machine learning-driven fraud solutions.
Streamlining Model Development
Some of the most significant challenges FI’s face to effectively utilize ML for fraud prevention have traditionally revolved around data orchestration and data consistency during both training and serving phases. This also extends to the computation of stateful risk features in real-time, which is critical for precise and effective fraud detection.
Effectiv’s no-code approach to building robust data pipelines capable of orchestrating interactions with third-party vendors is key to computing thousands of fraud features within millisecond latency. Model and pipeline versioning is another important concept for maintaining data and model governance.
In terms of providing training-ready datasets for data scientists, FI’s should streamline the process of data preparation. It eliminates the time-consuming tasks of data cleaning and preprocessing, allowing data scientists to focus on the core task of model development and optimization.
Model Deployment and Management
Model deployment and management often pose significant challenges in the rapidly evolving field of machine learning. The use of Docker containers on separate servers for deploying models can increase complexity, especially when handling multiple use cases and models.
MLServer offers an effective solution to these challenges. As an open-source inference server, MLServer facilitates the creation of REST and gRPC endpoints on top of serialized model artifacts, thus simplifying the deployment process. MLFlow models can be Dockerized using MLServer and deployed conveniently.
To handle advanced requirements like inference graphs, A/B testing, and monitoring, Seldon Core comes into play. An open-source model orchestration framework, Seldon Core aids in deploying MLServer and Triton models at scale. It is built on Kubernetes, offering flexibility and seamless integration with other platforms.
For even more advanced deployments, Seldon Deploy Advanced, an enterprise-grade solution, facilitates easy model deployment by data scientists, incorporating best practices such as monitoring, logging, and alerts. This solution integrates with Effectiv, making it easier to serve models in a pre-integrated production pipeline. Importantly, Effectiv allows for easy model refreshing and updating, enabling adaptive decision-making without the need for additional engineering resources.
Model Adoption and Explainability
Model adoption and explainability have become significant focus areas in today’s machine-learning landscape. Companies and regulatory bodies often require deep governance and an understanding of the decision-making processes behind ML models before they go into production. Furthermore, frontline staff and reviewers require a clear comprehension of these processes to ensure effective operation. The adoption rate of ML solutions that lack this level of trust and understanding is significantly lower.
Seldon’s AIX (Artificial Intelligence eXplainability) solutions present an effective response to these issues. They provide a high level of model explainability, enhancing the understanding and thus, the adoption rate of ML models. This explainability is key in fostering trust and confidence in the models and the decisions they drive.
Effectiv’s adaptable case management systems allow frontline staff and reviewers to consume model explainability during their . This improves their comprehension of the ML-driven decision-making process and enables more informed and quicker decision-making.
Seldon Alibi SHAP explainer providing insights about a case in the Effectiv platform
A deep expertise in machine learning modeling ensures to crate models in a sound and auditable manner. This helps to build trust, facilitate understanding, and ultimately, increase the adoption rate of these models.
Envisioning the Future: A Holistic Approach
Looking ahead, the vision is to transcend the realm of fraud detection and consolidate all risk and fraud-related aspects into a single, comprehensive system. This approach necessitates more sophisticated model deployment, a challenge that combined solutions like Seldon and Effectiv can help solving.
In pursuit of a more advanced user experience, different functions within a financial institution will need to move closer together. Machine learning will need to power many decision frameworks in real-time which creates a need to consume data collected from the entire user journey.
For this, a 360-degree view of customer data paired with advanced model serving is key. By integrating data from various customer touchpoints, such as onboarding, payments, account change events, and underwriting, we can generate richer data sets. These comprehensive data sets are the foundation for improving machine learning models, leading to better prediction and detection of risk and fraud.
Ultimately, this holistic approach not only enhances the accuracy and efficiency of our risk and fraud management systems but also significantly improves the customer experience. By ensuring a safer environment for transactions, we build trust and loyalty with our customers, thereby contributing to the overall success of the business.
Closing Thoughts
Seldon and Effectiv AI are changing the game in fraud prevention. By joining forces, they offer a powerful set of tools to help financial institutions fight back against fraud. By creating a safer and more efficient environment for financial transactions, they’re helping to build trust and keep customers happy, paving the way for success of the financial institution of tomorrow.
