Data Modernization Services for Financial Institutions

Turn siloed financial data into governed, AI-ready platforms. DataArt modernizes data ecosystems for banks, insurers, and asset managers, end to end.

Why Modern Data Platforms Matter

Trusted data access with governance built in

Unify fragmented data and standardize definitions, lineage, and access controls. Give teams reliable self-service data while meeting audit, privacy, and regulatory expectations.

Connected ecosystems and faster intelligence

Integrate front-, middle-, and back-office systems to improve reporting, analytics, and decision cycles. Reduce manual reconciliation and accelerate delivery of new data products.

AI-ready architecture without new risk

Modernize pipelines, data models, and platform foundations so AI assistants, automation, and advanced analytics can run on consistent, secured, well-governed data.

Operational efficiency with measurable cost control

Move from “keep it running” to a managed platform operating model. Apply FinOps and data platform governance to improve reliability and reduce total cost of ownership.

5 Pillars That Guide Our Data & AI Strategy Mentioned by Gartner®

We believe the Gartner^® report reinforces several pillars of DataArt’s long-term strategy – pillars we have already begun scaling across our global organization.

Industry-specific delivery

Finance use cases demand domain-aware data models, controls, and workflows.

How we deliver: build solutions grounded in finance operating reality: risk, reporting, surveillance, underwriting, and customer data.

Outcome-aligned engagement models

Modernization succeeds when scope and value are explicit.

How we deliver: define measurable outcomes early (time-to-insight, platform stability, reporting latency, cost-to-serve), then deliver to those targets.

Reusable assets to accelerate delivery

Standard patterns reduce delivery risk and speed up platform implementation.

How we deliver: apply reusable accelerators, reference architectures, and automated delivery components where they fit.

Governed AI adoption and compliance

AI adoption increases the need for auditability, access control, and repeatable controls.

How we deliver: design governance, observability, and control planes that support regulated AI use.

Continuous investment in data and AI capability

Sustained progress requires ongoing enablement, not a one-time platform build.

How we deliver: invest in data and AI capabilities, delivery methods, and talent development.

How Finance Teams Use Platform

Asset Management

Portfolio and performance analytics on unified, governed datasets
ESG and alternative data ingestion and modelling
Research platforms and workflow automation
Faster client and regulatory reporting

Capital Markets

Market and reference data pipelines built for scale and latency needs
Trade surveillance and regulatory analytics foundations
Quant research enablement through unified data environments
Cloud data infrastructure modernization

Insurance

Claims analytics and operational dashboards
Underwriting data modernization and automation-ready pipelines
Data quality controls and governance automation
Actuarial modelling support through standardized datasets

Banking and Payments

Fraud, AML, KYC, and credit analytics foundations
Customer insights with privacy-safe data access patterns
Payments risk intelligence and monitoring
Regulatory reporting data foundations

Proof in Practice

Choose DataArt as your trusted partner for progress.

DataArt's flexible support model allows us to effectively scale our in-house teams during high-demand projects, helping us maintain the high level of service our clients expect. Their ability to source niche expertise on a short-term basis has proven invaluable, giving us access to specialist knowledge precisely when we need it, without the overhead of full-time resourcing. This on-demand capability helps us move faster and stay focused on delivering impactful outcomes.

Stuart Buckell, CEO and Founder at Buckhill

Our Finance Clients

Book a Finance Data Consultation

Gartner, AI Vendor Race: How to Evolve Your Pricing Model for AI Services, Danny Ryan, Robert Brown, 13 October 2025.

Gartner is a trademark of Gartner, Inc. and/or its affiliates.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.