This is the second post in a mini-series on The Three E’s of AI Success. In case you missed it, read the introduction here.
Across the financial services industry, firms often experiment with artificial intelligence as a bolt-on capability—adding a model here, testing a tool there, hoping AI will generate value around the edges of their business. This peripheral approach might lead to siloed improvements, but to realize AI’s full potential, companies must take a strategic approach to integrating it throughout their core systems, processes and culture.
At BetaNXT, we view AI not as a separate initiative, but as an accelerant to our “business as usual”—enhancing the full technology lifecycle from development to deployment and enabling every team to work faster and smarter. Fulfilling this vision means we are constantly anticipating our clients’ needs before they surface, to help them address existential issues like:
- How to meet advisors’ and investors’ growing expectations
- How to maximize data’s value while safeguarding privacy
- How to innovate wisely even before regulatory guidance becomes clear
Addressing all these issues and more with AI in the picture is a heavy lift for our clients, which is why we as their partner have only galvanized our commitment to offloading their burden as much as possible. We believe AI success (and success for our clients in general) begins with anticipating needs and embedding the necessary solutions and processes upfront.
In practical terms, these are the pillars we have long embedded in our business and solutions—and now we’re dialing up each one’s intensity and evolving our protocols to enable AI success:
Strong data governance
Transparency and explainability
Security and privacy
Responsibility
Let’s examine each of these further.
Strong Data Governance
When it comes to data governance, we take an approach we call “shift left.” Our framework not only pulls governance all the way back to the point of data creation but also assigns it a more strategic and central role in business outcomes (and AI success). Essentially, we have transformed the meaning of governance, from compliance-centric, after-the-fact oversight toward an enablement-driven, at-the-source engine. Our clients benefit because we are offloading much of their burden while adding significant new value as well. With the introduction of AI—particularly generative AI, intelligent automation and predictive analytics—this governance approach becomes even more critical.
We have built our infrastructure, centered around DataXChange, to make data more knowable, searchable, usable—and ultimately more valuable for all users and stakeholders. We have created a context-rich, well-linked platform where AI can thrive, thanks to rich metadata and robust mapping of datasets and the relationships between them.
User-friendly tools, including a cloud-native governance hub, comprehensive data catalog and visual knowledge graph, provide the clarity and context that AI requires.
Beyond the technology of operationalizing governance, having an engaged human governance committee is just as important. Our cross-functional team includes subject matter experts from compliance, data science, product, and IT who together oversee model design, testing and validation, and ongoing product development lifecycle management. Activating AI effectively and responsibly is an enterprise priority and a core pillar of our day-to-day work.
At a granular level, BetaNXT is committed to the principle of human-in-the-loop (HITL) for every AI initiative, ensuring that expert oversight complements automation. A HITL approach safeguards governance, enhances decision-making, and reinforces the trust and accountability that technology alone cannot provide.
Transparency and Explainability
Transparency means different things to different stakeholders. Regulators want to see AI model logic, data sources and governance frameworks; advisors and investors want plain-language explanations of recommendations; and internal teams need to understand model design, assumptions, metrics and decisions. Answering this broad range of needs starts with centralizing all data, both internal and external, so it can be governed holistically as a single source of truth—before it feeds into AI applications.
At BetaNXT, our DataXChange platform has turbocharged our ability to understand, share and distribute data with maximum transparency and explainability. Especially in the absence of clear regulatory guidance on AI, it’s wise to prioritize documentation and detail proactively now to avoid potential liabilities later. Audit trails are nothing new for BetaNXT or our clients, but today we need to incorporate AI-specific dimensions and documentation.
In addition to the lineage, change history and business context our metadata frameworks already address, we have now expanded to new aspects such as model provenance, prompt history and evaluation metrics. This creates transparency not just for the data, but for the AI systems driven by that data.
On a more macro level, it’s crucial to test models regularly and document accuracy, issues and any adjustments made (and by whom).
We have also implemented continuous monitoring to track data quality and anomalies, confidence levels and outlier decisions. In the era of AI, when in doubt, document it.
Security and Privacy
AI amplifies both the value and the risk associated with data for us and our clients. With sensitive financial information passing through AI-enabled systems—workflow automation, portfolio intelligence, client analytics—we have embraced a security-first, privacy-by-design architecture.
We apply the same rigor used for our core brokerage and clearing systems—such as Zero-Trust access, end-to-end encryption and a multi-layered security framework—across multiple dimensions, including network security, identity and access management, data protection, operational security, compliance and application security.
One of our key principles that has become more critical with the advent of AI is data minimization, which means having controls in place to ensure AI models never receive more data than they strictly require.
For example, we automatically detect sensitive fields (e.g., Social Security numbers) and redact PII when it is not necessary for a specific use case. Even as we strive for explainability, we are also careful not to reveal private details and to always protect client data throughout the process.
Particularly as the investment ecosystem becomes more interconnected across third-party partners, we are ensuring our high standards for security and privacy remain in place, from the way we secure our data pipelines and integrations to the language we use in our contracts.
Responsibility
As an overlay to all the aspects mentioned above, embedding a culture of responsible AI adoption and ethical innovation is a top priority for our enterprise. This manifests in many ways, from our choice of AI models to our cultural evolution.
We have intentionally embraced a “constitutional AI” model: a training method where the model follows a set of ethical principles, or a “constitution,” to make itself more helpful, honest and harmless without relying solely on human feedback.
Our AI-powered technology follows a more ethical path instead of one that prizes performance at all costs.
We purposely provision our AI models with read-only access to source data—this level of abstraction offers the ability to safely develop innovative new capabilities without the exposure and risk of inadvertent modification or corruption. This will also help protect us and our clients from a regulatory standpoint.
Lastly, and most importantly, we are evolving our company culture from top to bottom to seamlessly adopt AI in a smart, responsible way. Our AI steering committee is detailing clear policies for acceptable use, transparency, data protection and other essential topics, and implementing an AI literacy education program across the company. AI is a part of daily life now for our teams and our clients, and we are focused on innovating responsibly.
Stay Tuned for More
Look out soon for the next post in this series, where we dive a little deeper on our second E for AI success: Efficient.
