ML6 uses predictive analytics to help organizations like yours forecast future outcomes, which is essential in a volatile economic environment. This can be applied to tasks such as forecasting liquidity needs, optimizing capital allocation, and predicting market trends, enabling better strategic planning and resource allocation. A case study with a large e-commerce company, where ML6's AI-driven sales forecasting system led to a €10 million euro increase in top-line growth, demonstrates the direct business impact of this capability.

Many traditional institutions are burdened by legacy IT infrastructures that slow down processes and hinder innovation. ML6 can use large language models (LLMs) and agentic workflows to automate the migration of legacy codebases to modern infrastructure. This can enable banks to modernize their critical systems more quickly, efficiently, and cheaply than with traditional methods.

Financial institutions are often hindered by data trapped in silos across various departments, including finance, customer support, and IT.
Unum, our Enterprise Superintelligence, is designed to unify all enterprise data into a "single, real-time platform," breaking down silos to create a comprehensive knowledge base. This enables a holistic, real-time view of the organization's operations, overcoming the "human-made ceiling" that traditionally limits growth and innovation.

ML6's work with Randstad demonstrates how AI can boost sales effectiveness. By developing a scalable AI solution, ML6 enabled Randstad's sales consultants to focus on contacting companies with real potential, resulting in an 81% increase in the sales hit rate. This capability is directly transferable to FSI firms looking to enhance client acquisition and cross-selling in a competitive market. 

The traditional SDLC is often reactive, with development teams only addressing issues after they are reported. Unum proactively identifies and resolves problems before they become critical.

• The system constantly scans an organization's operations and digital communications to detect bottlenecks, such as a missing approval in a workflow or an unanswered email.
• It then deploys autonomous AI agents to eliminate this friction and automate repetitive work.
• This moves problem-solving from reactive ("sales are down") to proactive ("the system sees where the issue is and helps build the solution").

The "autonomous financial data engineer" is an example of an AI agent that Unum can deploy.

• This agent can generate a deterministic data pipeline for a given task, acting as a "universal data translator".
• It combines the generalizability of GenAI models with a focus on ensuring deterministic and error-free processing. This capability significantly accelerates data-related tasks in the SDLC.

Unum can provide a unified view of an organization's data, which helps align IT activities with strategic goals.

• It can analyze data to provide automated, data-driven recommendations on where resources would have the highest impact.
• For two leading Dutch banks, ML6 is investigating how ESI can improve strategic reporting and help the IT team ensure their work aligns with the bank's strategy. This streamlines the process of strategic execution and resource allocation.

Unum, our Enterprise Superintelligence, can help with detecting and proactively managing Common Vulnerabilities and Exposures (CVEs) and their reporting by acting as an "autonomous brain" for an organization's security posture. It proactively identifies and addresses cybersecurity risks, moving beyond a reactive, human-driven approach to a dynamic and automated one.

Unum can be configured to act as a system-wide cognitive layer. It can constantly scan an organization's entire IT ecosystem, including legacy systems and third-party vendor platforms, for known vulnerabilities by cross-referencing against CVE databases and other threat intelligence feeds. Instead of waiting for a security report or a manual audit, Unum proactively identifies emerging issues and opportunities before they become critical.

When Unum detects a potential CVE, it doesn't just flag it; it can also analyze the context of the vulnerability within the specific environment. By understanding the interconnectedness of systems and data, it can assess the potential impact of the CVE and deploy autonomous agents on the fly to prioritize the most critical risks. This capability moves beyond static risk analysis to a dynamic and intelligent allocation of resources based on where they can create the most value.

Once a CVE is identified and prioritized, Unum can deploy an "army of AI agents" to help with the remediation process. This could involve drafting a plan, proposing the next steps, and in some cases, automating the deployment of patches or security updates. For example, Unum could automate the process of writing an integration to connect to a legacy system to push out a patch. This significantly reduces the time from detection to resolution, which is critical in preventing an exploit.

The regulatory burden on the FSI is continuously expanding and fragmented. Unum can help automate the reporting of CVEs and other security incidents by integrating with internal systems and regulatory frameworks. Since Unum has a built-in governance layer, it can ensure that all reporting is transparent, auditable, and aligned with legal and enterprise compliance requirements. This eliminates the risk of human error in compliance and reporting, which can be overwhelming due to the sheer volume of data.

A significant portion of security breaches are caused by third-party vendors. Unum can continuously monitor the entire third-party ecosystem for vulnerabilities, a task that is too complex for manual oversight alone. Because Unum is deployed securely on the client's premises or cloud, it accesses only what is authorized and ensures that no data ever leaves the client's infrastructure. This mitigates the risk of unintended exposure and addresses the critical security challenge posed by third parties.