Mirai AI

Mirai AI embeds intelligence directly into Treasury, ALM, and Risk workflows. It covers three core capabilities: Mirai AI Agent, a natural language companion that makes ALM and regulatory knowledge accessible across teams; Mirai AI Modeling, which automates behavioral modeling using deep historical data and continuous self-calibration; and Mirai MCP Server, which allows banks to integrate Mirai's domain expertise directly into their own LLM and workflows.

Mirai AI Agent is an end-to-end expert companion embedded in Treasury, ALM, and regulatory workflows. It makes platform, regulatory, and data knowledge accessible so users do not need to be experts to get accurate, actionable answers. Teams can query ALM data in natural language, explain metric variations, analyze data quality issues, and generate executive-level insights across NIM, LCR, and other key indicators. It reduces onboarding time, accelerates decision-making, and ensures no relevant information is missed across quant, treasury, and regulatory functions.

Mirai AI Modeling applies machine learning techniques, including Neural Networks, Gradient Boosted Trees, and Random Forests, to model deposit behavior, prepayment rates, and customer responses to interest rate changes. Models are trained on deep historical transaction-level data, enabling granular pattern detection and continuously evolving behavioral insights through self-calibration. A practical application is the automated classification of core and non-core deposits, replacing manual processes with decision tree algorithms trained on the bank's own data. All models are explainable and auditable, meeting regulatory transparency requirements.

Yes. The Mirai MCP Server (Model Context Protocol) allows banks to connect their own LLM and enrich it with Mirai's domain expertise in ALM, liquidity, regulatory reporting, and financial intelligence. The result is a context-aware intelligence layer that integrates Mirai's knowledge directly into an institution's existing workflows and systems.

Mirai uses supervised machine learning to automatically classify deposits as core or non-core based on historical behavior patterns, rate sensitivity, and account characteristics. The classification updates dynamically through continuous self-calibration as new data arrives, replacing manual, spreadsheet-based processes used in legacy systems.

Yes. Mirai follows an "explainable-first" approach. Every Mirai AI Modeling output traces back to the factors that drove the result. Mirai allows banks to generate the explainability outputs their governance requires, including feature importance rankings, confidence intervals, or any custom validation metric used in model risk management. This meets regulatory expectations for model governance, validation, and audit in banking.

Mirai AI embeds intelligence natively into Treasury, ALM, and Risk workflows through the Mirai AI Agent and Mirai AI Modeling. It automates operational queries, explains data variations, and delivers actionable guidance exactly where teams need it, supporting faster and more confident decision-making across treasury, risk, and regulatory functions.

Static models and manual classification processes are slow to adapt to shifting customer behavior or market conditions, creating gaps in exposure detection. Mirai AI addresses this through Mirai AI Modeling, which uses deep historical transaction-level data, continuous self-calibration, and dynamic behavioral modeling to capture emerging exposures as they develop rather than after the fact.

Regulated banks cannot act on outputs they cannot justify to auditors, regulators, or senior management. Mirai AI is built on an explainable-first architecture, meaning every recommendation or insight generated by Mirai AI Agent and any output from Mirai AI Modeling comes with transparent, traceable reasoning. This makes it possible to validate decisions, maintain governance standards, and build institutional trust in Mirai AI-driven analysis.

Mirai AI is purpose-built for regulated banking environments. Its explainable-first architecture, secure cloud infrastructure, and deep domain expertise across ALM, liquidity, FTP, and regulatory compliance address the governance, auditability, and control requirements that regulated treasury teams operate under.

Mirai AI delivers an ALM analytics environment through Mirai AI Agent and Mirai AI Modeling. Teams can analyze NIM projections, LCR ratio drivers, deposit behavior, and funding structures using natural language queries via Mirai AI Agent, while Mirai AI Modeling provides granular behavioral insight fueled by deep historical data and continuous self-calibration.