bmpro: Consulting & Advisory
bmpro is a consulting and advisory firm that helps Indian financial institutions establish defensible model risk management. It builds model inventories, conducts independent validations, and provides plain-language explanations of model-driven numbers for regulators and boards. The firm also offers an Agent Trust Framework that verifies AI agent architecture, data flows, and contractual terms through seven evidence-based checks.
- Regulatory & Compliance Technology
- Financial Technology
- Insurance Technology
- Software Only
- Data & Analytics
- Artificial Intelligence
Funding
Founders
Founder details are not available yet.
Product
Problem
India's financial institutions now run on models, yet few can explain why a model produced a specific number, where it could be wrong, or what that means for reported figures. Ind AS 109, Ind AS 117, and the coming risk-based capital regime for insurers demand defensible lines of sight from model to number to decision, but most organisations lack the documentation, independent challenge, and version control to provide them.
Solution
bmpro tailors solutions to each client's institution, regulation, and problem, delivering value through structured model risk management. For insurers, the firm builds model inventories, brings in independent validators to test assumptions rather than confirm them, and pushes for outputs explainable in plain language. For AI adoption, bmpro's Agent Trust Framework conducts seven verifiable checks covering architecture, reproducibility, data flow, access permissions, training clauses, ownership, and deletion, extending standard procurement questions into the model and access layer behind the interface.
Target Audience
Primary customers are Indian insurers preparing for Ind AS 117 and risk-based capital, as well as financial institutions and enterprises needing model validation, AI agent vetting, and defensible explanations of model-driven numbers for boards, auditors, and regulators.
Features
- Model inventory covering every model feeding a reported number, with an owner and last-checked date across actuarial, finance, investment, risk, and capital
- Independent validation by someone outside the model-building team, testing assumptions rather than confirming them
- Change logging that traces every model or input change back to the reported figures it touched, preventing unexplained profit swings
- Agent Trust Framework with seven checks: architecture identification, version reproducibility, data flow tracing, permission enforcement, no-training clause verification, ownership confirmation, and deletion evidence
- Plain-language model explanations covering what drives a number, what would move it, and what it doesn't account for
- One consolidated management and board view of what's changed, what's open, and what could go wrong, instead of inconsistent answers across stakeholders