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Financial firms rely on monitoring, archiving and recordkeeping tools to meet regulatory obligations, protect clients and safeguard their businesses. In pursuit of efficiency, many firms adopted early, third-party artificial intelligence-backed regulatory technology (regtech) solutions to replace their manual processes.
While these applications showed promise, they have limitations. As regulatory scrutiny intensifies and communication volumes continue to rise, it is apparent that many older AI-backed regtech tools add friction instead of reducing it.
This paradox is the catalyst for a newer generation of closed AI platforms that narrow many of the gaps left by open AI solutions. These systems are also AI-backed, but they differ in architecture and intent, emphasizing contextual understanding, internal data control and clearer auditability.
Next-Gen AI-Powered Archiving Platforms vs. Legacy Solutions
Legacy AI regtech tools were largely built on open, third-party AI engines that relied on application programming interfaces (APIs) to access stored data. While this approach enables rapid deployment, routing data to and from external systems increases the odds of data leakage, process mismanagement and potential loss.
In one notable instance, OpenAI, ChatGPT's parent company, implemented an update that made previously stored records inaccessible. This left many firms without work records, hindering their ability to produce required documentation for regulatory reviews. This example underscores how disruptions to retained communications can create material compliance exposure for regulated firms.
The core problem was data governance: where data was housed and how much control firms had over their records.
Next-generation platforms are custom-built to operate within a firm's internal environment using internally hosted data lakes instead of third-party storage. Data is ingested directly and analyzed against firm-specific governance policies. This structure reduces vulnerability and improves breach visibility while also providing firms direct oversight of record capture, review and retention.
How Legacy AI Tools Fall Short
Beyond architectural concerns, many open, third-party AI tools struggle to properly evaluate communications. Systems that rely on keyword scanning without sufficient context can generate false alerts while still missing genuine risk.
For example, a WhatsApp message stating, “I guarantee I’ll call you tomorrow,” may be flagged as a performance claim, prompting unnecessary review and documentation. At the same time, a multi-message exchange implying a strategy “should easily outperform last year” may pass through unflagged because messages are evaluated individually rather than as part of a broader conversation thread.
Over time, inflated alert volumes can burden compliance teams, extend review cycles and increase the likelihood that meaningful vulnerabilities will become buried among benign communications.
What might have started as an efficiency tool can instead create operational drag, diverting resources toward low-value review work.
What Next-Gen AI Platforms Do Differently
Next-generation AI platforms are also AI-backed, but they are designed with context and control as foundational principles. Rather than relying on generalized third-party models, they ingest data directly into controlled AI environments configured with firm-specific prompts and guardrails. These systems evaluate full communication threads and intent, allowing them to distinguish between casual language and genuine regulatory risk.
The impact of this design shift can be significant, as this real-life example from an Archive Intel client shows:
A large broker-dealer reviewing 1.5 million emails saw roughly 150,000 messages flagged by a legacy system. When those same messages were evaluated using contextual AI, fewer than 150 required escalation.
In addition to improved accuracy, modern platforms support unified capture across email, text, social media and messaging applications. They also accommodate bring-your-own-device (BYOD) policies through selective auditing on personal devices, helping firms balance compliance requirements with employee privacy. This can prove valuable, even for firms that maintain their employees do not conduct business via personal devices, only for a regulator to ask them to back that claim. This, too, was an actual occurrence at a financial firm. The impromptu audit did not end in their favor.
Fortunately, selective auditing, such as whitelisting personal contacts on a device used dually for business and private affairs, is easy to implement with newer, closed-architecture regtech systems. For some firms, this capability alone may warrant a closer evaluation.
Practical Questions for Evaluating AI-Backed Compliance Tools
Firms seeking answers can ask the following questions to assess their current communications archiving system or vet potential alternatives:
- Does the platform evaluate entire conversations instead of isolated messages?
- What's the false positive rate, and how much time is spent reviewing alerts?
- How is communication data ingested and stored (internal data lake or external APIs)?
- Can the system capture email, text, social and messaging apps in one environment?
- Does it support selective auditing and contact whitelisting on personal devices?
- Can the platform provide a defensible audit trail during an examination?
- Are the vendor's AI governance, privacy and incident-response policies clearly documented?
Why Architecture Matters as Much as Intelligence
AI is vital for compliance, but not all systems effectively reduce risk or workload. As communications increase and regulations change, a gap is emerging between early-generation and modern tools.
While legacy platforms built on open AI models might have been improvements over the earliest compliance tools, continued innovation has resulted in materially better options.
Reassessing legacy systems through a modern lens can help firms identify where closed, context-aware platforms may offer a stronger foundation for communication governance, operational efficiency and regulatory confidence.
Open AI models helped kickstart automation in compliance. Closed platforms will likely make it sustainable.
Larry Shumbres is the founder and CEO of Archive Intel, an AI-powered communications compliance platform built for the financial services industry, helping financial institutions monitor, archive and review communications and marketing content with speed and precision.
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.
