- EXL’s expanded collaboration with Databricksannounced June 12, 2026, strengthens its EXLdata.ai platform by extending end-to-end data lineage and auditability across distributed environments, capabilities that regulated industries require before AI deployments can scale.
- EXL’s Document Fraud Solution cut fraud losses by $12.1 million for a named US fintech bank and reduced fraud investigation turnaround by 58%, achieved by combining machine learning with a team of over 200 fraud specialists.
- EXL REGULATORY REPORTING ASSIST.AIâ„¢ claims a 60-70% reduction in regulatory reporting errors for NAIC insurance compliance, an assertion enterprises should test against their own error baselines before factoring it into procurement decisions.
While much of the AI market conversation fixates on valuations and hype, EXL (NASDAQ: EXLS) has been building quietly in the opposite direction: embedding AI into the operational workflows of insurance, healthcare and banking clients where compliance failures and fraud carry real financial consequences. This week the company expanded its collaboration with Databricks to strengthen the data foundation underpinning its EXLdata.ai platform. The partnership is a useful lens into how enterprise AI services firms are differentiating, not on model capability, but on governance, auditability and measurable outcomes.
Data Modernisation Powers AI Readiness
A clean, well-governed data foundation is a prerequisite for enterprise AI that actually holds up in production. EXL’s data modernisation services address this by automating pipelines, enriching metadata and enforcing governance from the outset, converting fragmented legacy data into assets that AI models can use reliably. The expanded Databricks collaboration, announced on June 12, 2026, is central to this work: through EXLdata.ai, EXL helps clients operationalise data capabilities in production environments while maintaining the lineage, compliance controls and auditability that regulated sectors require.
Poor data quality remains a persistent obstacle to scaling AI in enterprise settings, and EXL’s proposition is that operationalising governance early shortens the distance between pilot and production. How widely that argument is landing with buyers is harder to verify from public material, but the Databricks partnership gives it a credible technical anchor.
Streamlining Operations with AI-Powered Workflows
EXL’s EXLerate.AI portfolio targets the process-heavy workflows where manual handling is both slow and error-prone. The platform combines intelligent document processing with agentic AI to extract and route content from structured and unstructured sources, reducing the volume of work that reaches human reviewers. EXL reports a 27% reduction in claims processing time for insurance clients and a 20% productivity gain in healthcare data workflows, according to the company. The solutions are designed to integrate with existing environments rather than requiring large-scale infrastructure replacement, which matters for enterprises wary of lengthy implementation cycles.
Detecting Fraud with Advanced AI Models
The clearest evidence of financial impact in EXL’s portfolio comes from its Document Fraud Solution. Deployed at a leading US fintech bank, the system uses machine learning to detect tampered documents, bank statements, invoices, payslips, by analysing metadata for signs of post-creation editing. Working alongside a team of over 200 fraud specialists, the solution delivered a $12.1 million reduction in fraud losses and a 58% improvement in fraud investigation turnaround time within a year, according to EXL. That combination of automated detection and specialist review is what separates it from purely rules-based fraud screening, which tends to flag high volumes of false positives and miss document-level manipulation.
Enhancing Regulatory Compliance and Risk Management
Regulatory reporting in financial services involves large volumes of structured data, tight deadlines and a low tolerance for error, conditions that suit AI-assisted review. EXL’s REGULATORY REPORTING ASSIST.AIâ„¢ is built specifically for this context, with a focus on NAIC insurance reporting. The platform automates review and analysis, and EXL claims it delivers a 30-40% improvement in handling time for controller and regulator queries, a 40-50% reduction in review timelines and a 60-70% reduction in errors, according to the company. Enterprises evaluating it should treat those figures as vendor-reported and test them against their own error baselines, but the direction of travel, shifting compliance from periodic sample review to continuous monitoring, reflects where the regulatory technology market is heading.
For context on the broader compliance deadlines that are shaping enterprise AI deployments in regulated sectors, see our coverage of the EU AI Act’s staggered compliance timeline.
Accelerating Generative AI Deployment at Scale
EXL’s generative AI infrastructure is built around a Centre of Excellence with, according to the company, over 1,500 generative AI specialists and 8,000 data engineers, supported by more than 50 plug-and-play AI accelerators designed for rapid deployment. The EXL Enterprise AI Platform, launched in October 2024, runs on NVIDIA AI Enterprise software and integrates NVIDIA NeMo and NIM microservices for model customisation and deployment. That architecture gives enterprise clients a path to customised AI capabilities without building from scratch, relevant for organisations in regulated industries where off-the-shelf model behaviour is rarely sufficient.
EXL’s position in the enterprise AI market rests on execution rather than model innovation: governance, workflow integration and domain-specific compliance tooling in industries where mistakes are costly. The Databricks partnership deepens the data foundation that makes that execution credible. Whether the reported performance figures hold across different client environments is a question due diligence will answer, but the operational focus is clear. For more coverage of AI policy and regulation, visit our AI Policy & Regulation section.
