Conversational analytics for banks, asset managers, insurers, and fintech operators — turning portfolio, risk, ledger, and customer data into the answers your CFO, CRO, and audit committee need with the lineage attached.
Banks, asset managers, and insurers have invested heavily in data warehouses, risk engines, and BI. Yet front-line decisions still wait on a queue of analysts to translate questions into queries. Regulators ask for evidence, not narratives.
Portfolio managers want to see exposure by sector, by tenor, by counterparty — sliced today's way, not yesterday's. The data team's backlog stretches into next quarter.
The CRO sees the daily VaR. Drilling into what changed since yesterday — by desk, by book, by underlying — still requires an analyst's morning.
A single 30-day data request from a regulator can consume two analysts for a month. The data exists. Getting it into the right shape, with lineage, doesn't.
Transaction monitoring catches what its rules anticipated. The patterns regulators are now asking about — beneficial ownership, cross-channel flows — aren't in the rule set.
Relationship managers know which clients drive revenue. The system can compute true contribution by client by product by channel — but it takes an analyst, every quarter.
When external auditors ask how a number was derived, the answer requires re-tracing the pipeline. Lineage isn't always queryable.
Data Dialogix sits over your existing core banking, treasury, risk, ledger, and customer systems. Every conversational answer is generated with the underlying SQL, source-system citations, and lineage attached. Your front office, risk, finance, and compliance teams can ask questions in plain language — and the answer is regulator-defensible because the path from source to answer is fully traceable.
For financial services, that means we model the way the business actually runs: by desk, by book, by counterparty, by product, by entity, by jurisdiction. Not by GL account code, not by trade table partition. When a CRO asks "what changed in our IG corporate exposure overnight," the platform knows which desk, which positions, which underlying, and shows the delta in fifteen seconds — with the supporting trade detail one click away.
We deploy inside your network perimeter or in a SOC 2 Type II / ISO 27001 environment, with single sign-on, role-based access, and full audit logging from day one. No vendor sees your customer data, your positions, or your trades.
Sector, tenor, counterparty, geography — sliced live across the book.
VaR, sensitivities, stress results — drillable to position-level by desk and book.
Pre-formatted answers and lineage for Basel, CCAR, LCR, FR Y-9C, MIFIR, and similar regimes.
Conversational investigation of suspicious patterns, beneficial-ownership chains, cross-channel flows.
Net contribution by client, by product, by channel — refreshed live, not quarterly.
Real questions from portfolio, risk, finance, and compliance teams, answered with lineage in seconds.
Every metric below is computed live from source systems, available as a conversational query, and pinnable as an automated monitor with lineage attached for audit.
An anonymized engagement profile drawn from a typical regional bank treasury and risk function. Names and specifics generalized — directionally representative of what a six-month engagement looks like.
The starting point. The treasury team maintained a daily liquidity packet in Excel that took two analysts four hours every morning to produce. The CRO's market-risk view was a static PDF generated by an overnight batch. When the Fed inquiry came in mid-quarter, two analysts were redeployed for three weeks to reconcile a single dataset.
What we did. Connected the Murex market-risk warehouse, Fiserv core, the internal product ledger, and Snowflake under a single conversational layer. Modeled the bank's entity, desk, and book hierarchy. Built dedicated workspaces for treasury, market risk, and credit, each with role-bound data access. Linked every conversational answer to a re-runnable SQL artifact stored with lineage.
What changed. The daily liquidity packet went from four hours to fifteen minutes — fully sourced from live systems. The CRO began asking ad-hoc questions in the standup instead of placing tickets. When the next regulator inquiry arrived, the response was assembled in three business days, not three weeks, with lineage attached.
Six months later. The platform extended into credit risk and into the CFO's monthly close review. Three analyst FTEs were redirected from report production to modeling and forward-looking analysis work.
In financial services, ROI on conversational analytics shows up in three specific places. We instrument each so it's defensible to finance, internal audit, and the board.
External and internal audit requests shrink from weeks to days. For institutions facing multiple concurrent inquiries, this directly reduces analyst overtime and external counsel exposure.
Faster, more granular exposure intelligence enables tighter limit utilization and earlier hedging decisions. The economic value compounds at the desk and treasury level — typically tens of basis points per year on managed RWA.
Reporting-heavy analyst functions reclaim 30–50% of their time for higher-value modeling and decision support. The headcount doesn't shrink — the output ratio improves materially.
Native connectors for the systems banks, asset managers, and insurers already operate. No core replacement, no data egress to a vendor cloud unless you choose it.
Financial institutions operate under examination. The platform is designed to operate inside that posture from day one — not retrofitted to it.
Every reported figure has a queryable lineage. Internal audit and external auditors can trace from answer to source row.
Encryption at rest and in transit, least-privilege access, full audit logging.
Designed against the FFIEC IT Examination Handbook for third-party risk management.
EU and US residency options, row-level masking, and right-to-erasure workflows for cross-border operations.
Book a 30-minute working session with our team. Bring one operational question your current tools answer slowly — exposure, risk, profitability, or regulator-driven. We'll show you what conversational analytics with regulator-grade lineage looks like against your kind of data.
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