Founded in 2014, Behavox is an AI company that transforms structured and unstructured corporate data into insights that safeguard and enhance businesses. The firm’s technology and industry-specific LLM enables users to ask and answer questions without becoming domain experts, technologists, or data scientists.
According to Fahreen Kurji, Chief Customer Intelligence Officer at Behavox, the company was founded on the belief that data itself is the most underutilized asset in businesses. Specifically within the financial services space, Kurji emphasised that there are ‘mountains’ of communications and transactions that are being stored or archived for compliance – but they’re not leveraged actively for risk detection or business intelligence.
This is where Behavox comes in. “Behavox set out to transform that type of data into actionable AI driven insights that help protect firms from misconduct while unlocking measurable performance gains..”
Transforming insights
To turn data into AI insights for business protection, Kurji believes the way to do this comes through building explainable, trustworthy AI – with explainability being a very important aspect.
She said, “It’s about building explainable, trustworthy AI that empowers compliance and risk front-office teams, and then support comes from ensuring our solutions are not just technically advanced but that they’re operationally pragmatic. They’re easy to adopt, they’re fast to deploy and they’re seamlessly integrated into your workflows.”
Kurji added that protecting businesses would really mean addressing risk reduction, managing compliance and misconduct.
A vital aspect of building any system and product in the RegTech space is being able to create an AI system that can spot misconduct. On the question of how such a system is designed, Kurji believes it is key to start first with a taxonomy.
“Define your risk categories, whether that’s insider trading, harassment, bribery or collusion, and then train on domain-specific data,” she said. “So this involves trader-slang multi-language, contextual risk signals in multi-language, and you really need a native speaker more than anything else, then have multi-model input.”
For Kurji, this also includes capturing email, voice chat and collaboration platforms, and then ingesting all structured and unstructured data. Additionally, she calls on models to be built that allow compliance teams to see why something is flagged, versus a model where it’s unknown why something has been flagged.
“Lastly, I would say human in the loop is very important. Analysts need to refine AI judgments over time, so you need to have that human loop element.
Minimising false positives
Addressing the critical challenge of balancing accuracy with comprehensive coverage in AI surveillance, Kurji was asked how she recommends minimizing false positives while keeping detection strong.
“I would say use contextual understanding instead of keyword triggers,” she began. This approach, she explained, is fundamental. “For us, that’s really important. We move very far away from just lexicon to make sure that contextual understanding is really important.”
She then detailed a multi-model strategy as the next step. The key is to “apply and assemble your models,” using semantic, behavioural, and anomaly detection to cross-check those results.
Kurji also pointed to the necessity of continuous learning. “Then implement feedback loops,” she added. This mechanism functions similar to a human in the loop, creating a cycle where analyst case outcomes are retraining the system.
“And then I would say, probably balance by tuning the risk thresholds per client, per business line, per region,” she added.
It is those four things together, Kurji stated, that would really minimize the false positives within that AI surveillance while still keeping detection strong.
Trustworthy AI
In an age where AI insights are exploding and becoming not only a desire but a necessity, how can firms ensure their AI-generated insights are trustworthy and regulatory-compliant?
Kurji identifies three key pillars for ensuring trustworthiness and compliance. “First would be transparency – models have to be explainable and auditable.” She emphasised that explainability is incredibly important, allowing regulators and compliance teams to understand how the AI reaches its conclusions.
The second pillar is establishing a robust governance framework. Kurji stresses the importance of aligning with regulatory expectations from bodies like the FCA, SEC, and MAS. “With new rules coming out, like the EU AI Act, aligning with those governance frameworks is important.”
Also key here is independent validation, such as third-party audits, consultants, vetting models and customer monitors. For Kurji, this independent validation is very important.
Finally, Kurji highlights data lineage. “Outputs are supported by audit trails and data lineage designed to enable traceability,” she states. This traceability ensures that any AI-generated insight can be verified and audited, providing the accountability that regulators demand.
Together, these four elements, transparency, governance frameworks, independent validation, and data lineage, create a foundation for AI systems that are both trustworthy and compliant with evolving regulatory requirements.
Meeting standards
A crucial aspect for any data archive is that it meets the applicable regulatory record-keeping requirements. What ensures this meets standards?
Kurji acknowledges it’s a comprehensive list but breaks it down systematically. “For starters, I would say immutable storage,” she begins. “So being compliant is really at the top of that data subject. Retrieval capabilities are equally critical. Kurji explains the need for robust redaction, deletion on request, and especially, the ability to have requests from legal hold and granular access control. “Ensuring least privilege principles, being able to be very specific with that audit trails” are essential components, she notes.
Visibility into data handling is another key requirement, Kurji outlines, wit this audit trail ensures that organizations can demonstrate compliance when questioned by regulators.
Finally, Kurji points to multi-jurisdiction compliance as a critical consideration, and the importance of having flexible retention aligned to local laws.
Driving revenue
Kurji sees significant revenue potential in AI-driven analysis. “First, identifying cross-selling and upselling opportunities by really analysing client behaviour patterns to proactively detecting abnormal transaction flows that may indicate both risk and opportunity,” she explains. This dual-lens approach allows firms to spot revenue opportunities while simultaneously managing compliance risks.
Additionally, the key for Kurji is also providing actionable intelligence to the front office. “Provide front office product and periods so instant answers on policy, trade checks to really accelerate deal flows,” Kurji notes. This real-time insight enables faster, more informed decision-making that can close deals more efficiently.
She emphasised that compliance must remain central, and that compliance guardrails must always be embedded.
“Compliant actions, they really should be frictionless, while risky actions are proposed or flagged,” she said. This approach ensures that revenue-generating activities flow smoothly when they’re compliant, while potentially problematic transactions receive immediate scrutiny.
Staying adaptable
A million-pound question for many modern firms in 2025 is how do they keep AI tools adaptable to changing regulations.
Here, Kurji outlines a systematic approach built on specialized expertise and architectural flexibility. “There’s a system for this, and we have a team of SMEs and regulatory individuals who used to be ex BCG or regulators who do help us with this,” she explains. This dedicated team monitors the regulatory landscape to stay ahead of changes.
The technical foundation is equally important. “I would say that it probably focuses on making sure that there’s modular architecture so they’re updateable to update risk taxonomies without really retraining the full model,” Kurji notes. This modularity allows the system to adapt to new regulatory requirements without requiring complete rebuilds.
Kurji emphasizes the importance of maintaining focus on core capabilities. “Then these groups focus on continuously monitoring regulatory updates, mapping to new rules, to AI logic, and having strong partnerships with regulators,” she says. This ongoing dialogue ensures that the AI tools evolve in alignment with regulatory expectations.
Other areas of key importance are transparency and client empowerment, and Kurji stresses the importance of being aware of what is coming down the line.
“Then customer configurability is a big one, so clients can adjust their rules to their jurisdiction,” Kurji concludes. This flexibility allows individual organizations to tailor the AI tools to their specific regulatory context and risk appetite as requirements shift.
Future plans
As Behavox looks toward the future, what is on their horizon? For the company, 2026 will be a big step forward.
Behavox plans to launch a trade surveillance platform in the new year, and also has in its plans to launch sixteen products. Additionally, the company aims to expand from compliance-first AI to enterprise-wide AI insights and expand industry engagement with regulators and cloud consultancies to help position Behavox as a trusted standard in financial-services AI and advancing benchmarks for AI and compliance.
Kurji concluded, “The ultimate vision is a single AI platform that protects and empowers businesses and gives leadership both risk confidence and strategic advantage. It’s about having that AI ecosystem and being able to have consolidated tech stacks and being able to get everything under one roof.
“The focus is to be an all-encompassing solution, whether that is for archiving or policy management, compliance or insider threat and trade surveillance.”
