---
title: "How FSIs eliminate silos between clients, operations, and finance"
description: "Financial institutions often split portfolio, operations, actuarial, and finance work across separate systems, even when teams depend on the same portfolios, positions, liabilities, and transactions. Databricks presents a unified, governed platform combining Databricks SQL, Unity Catalog Metric Views, Lakebase, Genie, Databricks Apps, and Lakeflow Designer to connect analysis with operational action. A worked workflow follows an actuary's duration analysis, a portfolio manager's scenario modeling and mandate changes, operations' IBOR/ABOR reconciliation, and finance's approval of adjustments and ledger entries. Governance features including row-level security, column masking, RBAC, ABAC, and shared business semantics keep access and terminology consistent, while Lakebase records corrections in auditable, timestamped tables. The result described is a closed-loop process in which strategy, risk, and accounting use the same governed data rather than reconciling separate systems."
---

# How FSIs eliminate silos between clients, operations, and finance

[Databricks](https://yomu.fyi/company/databricks) · Anindita Mahapatra, Emily Piekarski, Shirly Wang · Sep 18, 2026

**Type:** Problem & solution

## Summary

Financial institutions often split portfolio, operations, actuarial, and finance work across separate systems, even when teams depend on the same portfolios, positions, liabilities, and transactions. Databricks presents a unified, governed platform combining Databricks SQL, Unity Catalog Metric Views, Lakebase, Genie, Databricks Apps, and Lakeflow Designer to connect analysis with operational action. A worked workflow follows an actuary's duration analysis, a portfolio manager's scenario modeling and mandate changes, operations' IBOR/ABOR reconciliation, and finance's approval of adjustments and ledger entries. Governance features including row-level security, column masking, RBAC, ABAC, and shared business semantics keep access and terminology consistent, while Lakebase records corrections in auditable, timestamped tables. The result described is a closed-loop process in which strategy, risk, and accounting use the same governed data rather than reconciling separate systems.

## Context

The scenario addresses fragmented workflows in financial institutions, where actuaries, portfolio managers, operations specialists, and finance teams use different systems while relying on shared portfolio, position, liability, and transaction data. It presents collaboration across these roles as the central challenge and frames a unified data platform as the way to reduce manual handoffs and reconcile fewer systems.

## Approach / What changed

The proposed workflow uses Genie for governed conversational analytics, Lakeflow Designer for visual data enrichment, Databricks Apps for interactive review and approvals, Lakebase for low-latency operational writes and auditable corrections, and Unity Catalog for access controls and shared terminology. AI agents support market-data retrieval and scenario analysis through an external MCP Server, while the workflow carries a duration-change request from actuarial analysis through portfolio changes, IBOR/ABOR reconciliation, adjustments, risk review, and ledger entries.

## Takeaways

- Genie lets business users ask portfolio and liability questions in plain language, while Lakeflow Designer enables visual enrichment of asset and liability data without waiting for engineering cycles.
- Unity Catalog applies row-level security, column masking, RBAC, and ABAC so different teams can query shared tables with independently governed access and consistent business terminology.
- Lakebase stores reconciliation corrections as auditable, timestamped records and supports operational checks, approvals, balance validations, and real-time writes between analytics and finance workflows.

## FAQ

### How does the workflow connect a duration analysis to accounting?

Sarah uses Genie and Lakeflow Designer to identify a duration mismatch and submit a mandate-change request. Dan translates it into portfolio changes, John reconciles IBOR and ABOR records, and Ben approves adjustments, generates ledger entries, and reviews risk.

### Where does AI assist, and who remains responsible for decisions?

AI agents retrieve market data through an external MCP Server, run scenario models, surface trade-offs, flag reconciliation mismatches, and propose corrections. The portfolio manager reviews outputs and adjusts parameters, while human users retain decision-making responsibility.

### How are corrections and access controls governed?

Unity Catalog provides row-level security, column masking, RBAC, and ABAC policies for shared data. Corrections are written to governed Lakebase tables with auditable, timestamped records.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Data Governance](https://yomu.fyi/topic/data-governance), [Lakehouse](https://yomu.fyi/topic/lakehouse)

- Source: [Databricks](https://www.databricks.com/blog/how-fsis-eliminate-silos-between-clients-operations-and-finance)
- Source URL: https://www.databricks.com/blog/how-fsis-eliminate-silos-between-clients-operations-and-finance
- Ingested by Yomu: 2026-09-17T14:06:22.976Z

[Read original post](https://www.databricks.com/blog/how-fsis-eliminate-silos-between-clients-operations-and-finance)