Persistent Systems has achieved Databricks’ Brickbuilder Specialization for Banking, Financial Services and Insurance, expanding its positioning around governed data, AI and modernization projects for financial institutions.

Persistent Systems has achieved the Databricks Brickbuilder Specialization for Banking, Financial Services and Insurance (BFSI), a recognition that strengthens the technology services company’s focus on helping financial institutions modernize data infrastructure and deploy artificial intelligence across regulated workflows.

The specialization builds on Persistent’s work with the Databricks Data Intelligence Platform and comes as banks, insurers and other financial-services organizations face growing pressure to turn fragmented data estates into systems that can support analytics and AI at scale.

For financial institutions, the challenge extends beyond moving data into the cloud. Transactional, customer, risk and compliance information is often distributed across legacy systems, making it difficult to establish consistent governance and provide AI applications with reliable data. Regulatory requirements add another layer of complexity, particularly when AI is introduced into decision-making processes.

Persistent says its Databricks practice addresses these challenges by creating a governed, AI-ready data layer. The company’s approach incorporates technologies including Delta Lake, Unity Catalog and Mosaic AI to support data management, model development and AI application workflows.

The combination is designed to give financial institutions a common environment in which data products, machine-learning models and AI agents can be developed and monitored while remaining subject to institutional security and governance requirements.

One example is Persistent’s Merchant Risk Management and Fraud Detection solution, which is built on Databricks and uses agentic AI. The company says the solution is intended to move financial institutions beyond reactive fraud controls toward predictive merchant-risk management.

Fraud detection is an area where the combination of data engineering and AI can be particularly relevant. Financial institutions need to correlate transaction behavior, merchant information and other signals quickly, while also maintaining processes for explaining and monitoring automated decisions.

Persistent is also developing generative AI agents for operations, analytics and compliance. The company says these agents are grounded in institutional data and supported by evaluation and monitoring mechanisms designed to address accuracy, explainability and alignment with financial-services workflows.

The specialization also deepens an existing relationship between Persistent and Databricks. Persistent is a Databricks Global Systems Integrator partner at the Silver Tier and says it has more than 1,000 Databricks certifications and more than 10 accelerators on the platform.

The companies are also working together on financial-services implementations. Persistent says it helped a leading European bank modernize risk-data infrastructure and customer-data frameworks using Databricks, providing faster access to governed information and supporting regulatory-readiness initiatives.

In another customer engagement, Persistent says it helped one of Japan’s largest financial-services organizations establish a governed data-management framework on Databricks. According to the company, the project improved oversight while reducing cloud costs.

Those examples illustrate why data modernization remains an important component of enterprise AI adoption in financial services. Banks cannot simply deploy a large language model and expect it to produce reliable results from disconnected or poorly governed information. AI systems require access to relevant data, controls around that data and processes for evaluating the outputs they generate.

Databricks has increasingly positioned its Data Intelligence Platform around this convergence of data engineering, analytics, machine learning and generative AI. For systems integrators such as Persistent, industry-specific expertise can provide a route into projects where technology implementation must be combined with knowledge of regulatory processes, financial products and operational workflows.

Barath Narayanan, Global BFSI and Europe Geo Head at Persistent, said financial institutions hold large volumes of data across risk, transaction and customer systems, much of it fragmented by legacy architecture. He said the opportunity involves using that data for decision-making while maintaining the transparency and control expected in financial services.

Josh Meyer, Global Head of Brickbuilder and Industry GTM at Databricks, described Persistent’s BFSI specialization as recognition of its combination of industry expertise and technical capabilities.

The development reflects a broader change in enterprise financial technology. AI projects are increasingly moving from experimentation toward production environments, where organizations need repeatable infrastructure for data governance, model evaluation, security and ongoing monitoring.

For financial institutions, that shift can make the underlying data platform as important as the AI application itself. Fraud detection, risk management, customer intelligence and compliance automation all depend on the ability to access trustworthy information within controlled environments.

Persistent’s Databricks specialization therefore gives the company another platform around which to package its BFSI modernization capabilities. The practical test will be whether those implementations can translate fragmented financial data into measurable improvements while meeting the governance and regulatory requirements that come with deploying AI in financial services.

Market Landscape

Financial institutions are increasingly combining cloud data platforms, machine learning and generative AI as they modernize legacy technology estates. Data governance is becoming particularly important as AI moves from experimental deployments into operational workflows involving risk, fraud, compliance and customer information.

Databricks competes in this market with major cloud and data-platform providers, while systems integrators such as Persistent provide implementation, modernization and industry-specific services around those platforms.

The BFSI segment presents additional requirements because data architectures must support security, regulatory controls, auditability and responsible AI practices alongside performance and scalability.

Top Insights

• Persistent has achieved Databricks’ BFSI Brickbuilder Specialization, strengthening its focus on data and AI modernization for financial institutions.
• The company’s approach combines Databricks technologies including Delta Lake, Unity Catalog and Mosaic AI with BFSI implementation expertise.
• Persistent is applying agentic AI to merchant risk and fraud detection as financial institutions seek more predictive risk-management capabilities.
• The partnership includes customer projects involving risk-data modernization, governed customer data and cloud cost management.
• Data governance, explainability and AI monitoring remain central requirements as financial institutions move AI applications into production.

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