{"id":6752,"date":"2025-11-28T12:38:43","date_gmt":"2025-11-28T12:38:43","guid":{"rendered":"https:\/\/fintech.global\/globalregtechsummit\/?p=6752"},"modified":"2025-11-28T12:39:29","modified_gmt":"2025-11-28T12:39:29","slug":"how-behavox-is-helping-its-clients-optimally-leverage-their-data","status":"publish","type":"post","link":"https:\/\/fintech.global\/globalregtechsummit\/how-behavox-is-helping-its-clients-optimally-leverage-their-data\/","title":{"rendered":"How Behavox is helping its clients optimally leverage their data"},"content":{"rendered":"

Founded in 2014, Behavox is an AI company that transforms structured and unstructured corporate data into insights that safeguard and enhance businesses. The firm\u2019s technology and industry-specific LLM enables users to ask and answer questions without becoming domain experts, technologists, or data scientists.<\/em><\/strong><\/p>

 <\/strong>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 \u2018mountains\u2019 of communications and transactions that are being stored or archived for compliance \u2013 but they\u2019re not leveraged actively for risk detection or business intelligence.<\/p>

This is where Behavox comes in. \u201cBehavox set out to transform that type of data into actionable AI driven insights that help protect firms from misconduct while unlocking measurable performance gains..\u201d<\/p>

Transforming insights<\/strong><\/p>

To turn data into AI insights for business protection, Kurji believes the way to do this comes through building explainable, trustworthy AI \u2013 with explainability being a very important aspect.<\/p>

She said, \u201cIt\u2019s 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\u2019re operationally pragmatic. They\u2019re easy to adopt, they\u2019re fast to deploy and they\u2019re seamlessly integrated into your workflows.\u201d<\/p>

Kurji added that protecting businesses would really mean addressing risk reduction, managing compliance and misconduct.<\/p>

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.<\/p>

\u201cDefine your risk categories, whether that\u2019s insider trading, harassment, bribery or collusion, and then train on domain-specific data,\u201d she said. \u201cSo 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.\u201d<\/p>

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\u2019s unknown why something has been flagged.<\/p>

\u201cLastly, 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.<\/p>

Minimising false positives<\/strong><\/p>

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.<\/p>

\u201cI would say use contextual understanding instead of keyword triggers,\u201d she began. This approach, she explained, is fundamental. \u201cFor us, that\u2019s really important. We move very far away from just lexicon to make sure that contextual understanding is really important.\u201d<\/p>

She then detailed a multi-model strategy as the next step. The key is to \u201capply and assemble your models,\u201d using semantic, behavioural, and anomaly detection to cross-check those results.<\/p>

Kurji also pointed to the necessity of continuous learning. \u201cThen implement feedback loops,\u201d she added. This mechanism functions similar to a human in the loop, creating a cycle where analyst case outcomes are retraining the system.<\/p>

\u201cAnd then I would say, probably balance by tuning the risk thresholds per client, per business line, per region,\u201d she added.<\/p>

It is those four things together, Kurji stated, that would really minimize the false positives within that AI surveillance while still keeping detection strong.<\/p>

 <\/strong>Trustworthy AI<\/strong><\/p>

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?<\/p>

Kurji identifies three key pillars for ensuring trustworthiness and compliance. \u201cFirst would be transparency \u2013 models have to be explainable and auditable.\u201d She emphasised that explainability is incredibly important, allowing regulators and compliance teams to understand how the AI reaches its conclusions.<\/p>

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. \u201cWith new rules coming out, like the EU AI Act, aligning with those governance frameworks is important.\u201d<\/p>

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.<\/p>

Finally, Kurji highlights data lineage. \u201cOutputs are supported by audit trails and data lineage designed to enable traceability,\u201d she states. This traceability ensures that any AI-generated insight can be verified and audited, providing the accountability that regulators demand.<\/p>

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.<\/p>

Meeting standards<\/strong><\/p>

 <\/strong>A crucial aspect for any data archive is that it meets the applicable regulatory record-keeping requirements. What ensures this meets standards?<\/p>

Kurji acknowledges it\u2019s a comprehensive list but breaks it down systematically. \u201cFor starters, I would say immutable storage,\u201d she begins. \u201cSo 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. \u201cEnsuring least privilege principles, being able to be very specific with that audit trails\u201d are essential components, she notes.<\/p>

Visibility into data handling is another key requirement, Kurji outlines, wit this audit trail ensures that organizations can demonstrate compliance when questioned by regulators.<\/p>

Finally, Kurji points to multi-jurisdiction compliance as a critical consideration, and the importance of having flexible retention aligned to local laws.<\/p>

Driving revenue<\/strong><\/p>

Kurji sees significant revenue potential in AI-driven analysis. \u201cFirst, 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,\u201d she explains. This dual-lens approach allows firms to spot revenue opportunities while simultaneously managing compliance risks.<\/p>

Additionally, the key for Kurji is also providing actionable intelligence to the front office. \u201cProvide front office product and periods so instant answers on policy, trade checks to really accelerate deal flows,\u201d Kurji notes. This real-time insight enables faster, more informed decision-making that can close deals more efficiently.<\/p>

She emphasised that compliance must remain central, and that compliance guardrails must always be embedded.<\/p>

\u201cCompliant actions, they really should be frictionless, while risky actions are proposed or flagged,\u201d she said. This approach ensures that revenue-generating activities flow smoothly when they\u2019re compliant, while potentially problematic transactions receive immediate scrutiny.<\/p>

Staying adaptable<\/strong><\/p>

A million-pound question for many modern firms in 2025 is how do they keep AI tools adaptable to changing regulations.<\/p>

Here, Kurji outlines a systematic approach built on specialized expertise and architectural flexibility. \u201cThere\u2019s 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,\u201d she explains. This dedicated team monitors the regulatory landscape to stay ahead of changes.<\/p>

The technical foundation is equally important. \u201cI would say that it probably focuses on making sure that there\u2019s modular architecture so they\u2019re updateable to update risk taxonomies without really retraining the full model,\u201d Kurji notes. This modularity allows the system to adapt to new regulatory requirements without requiring complete rebuilds.<\/p>

Kurji emphasizes the importance of maintaining focus on core capabilities. \u201cThen these groups focus on continuously monitoring regulatory updates, mapping to new rules, to AI logic, and having strong partnerships with regulators,\u201d she says. This ongoing dialogue ensures that the AI tools evolve in alignment with regulatory expectations.<\/p>

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.<\/p>

\u201cThen customer configurability is a big one, so clients can adjust their rules to their jurisdiction,\u201d Kurji concludes. This flexibility allows individual organizations to tailor the AI tools to their specific regulatory context and risk appetite as requirements shift.<\/p>

Future plans<\/strong><\/p>

As Behavox looks toward the future, what is on their horizon? For the company, 2026 will be a big step forward.<\/p>

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.<\/p>

Kurji concluded, \u201cThe ultimate vision is a single AI platform that protects and empowers businesses and gives leadership both risk confidence and strategic advantage. It\u2019s about having that AI ecosystem and being able to have consolidated tech stacks and being able to get everything under one roof.<\/p>

\u201cThe focus is to be an all-encompassing solution, whether that is for archiving or policy management, compliance or insider threat and trade surveillance.\u201d<\/p>

Keep up with all the latest RegTech news here<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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