Financial services organizations are drowning in data. From emails and Bloomberg chats to WhatsApp messages and calls, the need to review communications data to detect potential misconduct and financial crime by employees and third parties is a mandated regulatory requirement for compliance and risk teams in 2025. Elastic's Financial Services Summit tackles this pressing challenge: how to monitor and analyze the vast web of digital communications that run across a financial services firm effectively and time efficiently.
fingerprint, whose clients include large asset management firms to boutique hedge funds, partners with Elastic to address a critical gap: While firms process many millions of monthly emails, compliance teams can manually review only 2%–3%. How can they achieve actionable insight and management information across this vast and varied pool of data?
The compliance crisis: More data, fewer hands
Fingerprint's CEO and Founder James Hogbin explains that traditional manual oversight cannot provide enough cover across this tsunami of communications data. With Elastic's AVP Massimo Merlo and Fingerprint's Head of Marketing Brielle Hewitt, the discussion is about solving this challenge. With regulators globally demanding ever-better oversight, showing “zero tolerance” with fines dished out to the tune of $2.1 billion to financial services firms lacking appropriate systems, controls, and oversight across their data, the issue is urgent.
This reality is one driver of Gartner’s 2024 forecast that by 2027, legal risk and compliance functions will double investment in compliance technology. This spending surge reflects mounting pressure on financial services to adopt automation driven, AI-powered tools capable of navigating increasingly complex regulatory environments. Key priorities, according to Gartner, are automated risk detection systems that analyze transactional patterns in real time, AI-driven audit workflows to reduce manual oversight, and explainable AI (XAI) frameworks to ensure regulatory transparency. Gartner emphasizes that institutions prioritizing unified data architectures will likely outpace peers in mitigating risks like financial crime and operational non-compliance.
Customer spotlight: FICO
FICO uses Elastic to power advanced analytics and decision-making tools that monitor financial, credit, and risk-related data. With Elastic and Kibana at the core of its analytics platform, FICO processes unstructured datasets to detect risk patterns and predict outcomes like fraud, credit exposure, and compliance breaches. The solution integrates 16 open source tools, including Elastic, to power text analysis, sentiment detection, and predictive modeling at scale — bringing real-time insight to financial institutions around the world. Read the full story.
Unifying data and scaling oversight with explainable AI
Companies can achieve 100% oversight while reducing the burden on compliance teams with a unified data approach and explainable AI. “Automation is key. There's too much information out there. You have to detect external actors, employee behavior, all sorts of things ... A human cannot manage it alone," Hogbin explains. "Financial crime and misconduct nowadays are far more sophisticated in complex digital ecosystems and rarely discoverable by a single action or event," says Merlo. "From day one our focus has been on creating a search platform that prioritizes speed, scale, and relevance."
Elastic’s Search AI Platform unifies fragmented data sources and provides sophisticated analysis. For compliance teams, this means automating repetitive tasks and focusing resources on in-depth investigations. For Fingerprint, this has brought significant benefits: Starting with one client, Fingerprint scaled to 150 clients processing 80 million monthly messages on a single Elastic cluster "without missing a beat," as Hogbin notes. Elastic's efficient data tiering and new Elasticsearch logsDB index mode reduce storage costs by up to 65% while keeping data instantly accessible. The platform's value extends beyond compliance — one asset manager using Elastic for compliance monitoring discovered they could save "a basis point, a basis point and a half" by analyzing which communication channels secured better pricing.
Hewitt confirms that financial institutions implementing AI and automation in compliance workflows see benefits including increased oversight coverage from 2%–3% to 100%, significant time savings, more precise risk identification, improved behavioral analysis capabilities, and enhanced productivity for traditionally small compliance teams. “They're saving 80% of their time. And normally compliance and risk people are very busy people. They've got a lot of expectations on them, lots of pressure," she says. “The ability for very small teams to do so much in a limited amount of time improves their productivity.”
Building future-proof risk detection frameworks
Merlo's closing advice focuses on fundamentals: Build a strong data foundation by unifying fragmented sources and ensuring data accessibility. Then, leverage automation and AI to manage scale and complexity, but always align these tools with human expertise. And remember, an open architecture is crucial to future-proof your strategy as regulations and technologies evolve.
Watch the full session: Real-time risk detection in action
Regulatory risk, fraud, and data complexity aren’t going away — but financial services teams can take control with the right foundation.
Watch the webinar to hear how Fingerprint and Elastic are redefining compliance through AI, automation, and unified observability.
See how Fingerprint is driving real-world results with Elastic, explore the customer success story.
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