Custom Machine Learning Models for FinTech & Asset Management

Hexaview develops custom models for use cases such as risk scoring, fraud detection, portfolio analytics, forecasting, client segmentation, reporting automation, reconciliation support, and operational decisioning. Models can include supervised, unsupervised, and NLP-based approaches, depending on the business objective, data quality, and compliance requirements. Each solution is designed for production use, not just experimentation.

Security and governance are built into the delivery process. Hexaview operates with SOC 2 Type 2 certification and ISO-aligned information security practices, which helps support secure handling of sensitive financial data. Projects typically include access controls, audit-friendly workflows, environment separation, documentation standards, and model governance considerations so firms can manage risk while advancing AI initiatives.

Yes. Many financial organizations operate across legacy platforms, siloed datasets, and mixed cloud environments. Hexaview helps modernize data pipelines, connect APIs, and structure usable datasets for model training and deployment. This often includes integrating front-, middle-, and back-office systems so machine learning models can draw from more complete, reliable information and deliver stronger business outcomes.

Yes. Through AI Labs and consulting engagements, Hexaview can rapidly prototype model concepts, validate assumptions, and test feasibility before a larger rollout. A proof of concept typically focuses on one defined use case, measurable success criteria, and a controlled dataset. This approach helps firms reduce implementation risk, confirm business value, and prioritize the right production roadmap.

Timelines vary by use case, data readiness, integration complexity, and governance requirements. A focused proof of concept may take a few weeks, while a production-grade model with platform integration, monitoring, and workflow automation can take several months. Hexaview typically begins with discovery, data assessment, and scope definition so delivery milestones are realistic and aligned with business priorities.

Yes. Hexaview supports platform implementation and cloud-based deployment strategies that fit existing enterprise environments. As an AWS Select Tier Service Partner, the team can help design scalable infrastructure for training, inference, monitoring, and data orchestration. Deployments are planned to align with operational workflows, security controls, and the performance expectations of financial services organizations.

Off-the-shelf tools can be useful for generic tasks, but financial firms often need models tailored to proprietary data, internal workflows, and regulatory expectations. Custom development allows tighter control over features, explainability, governance, and integration. It also improves fit for specialized use cases such as portfolio analytics, investor reporting, or operational automation where generic tools may fall short.

Hexaview works with a range of financial organizations including wealth management firms, asset managers, hedge funds, fund administrators, neo banks, and broader banking and financial services companies. The team also supports enterprise clients and startups that need secure AI engineering and data science capabilities. This industry focus helps accelerate delivery for firms with complex financial workflows and data environments.