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Insights from our recent webinar "Beyond Data Management: Accelerating Investment Opportunities in a Changing Market"

The growing mountain of data confronting investment teams has made traditional research methods increasingly untenable. During our recent webinar with seasoned investment leaders, several critical limitations of conventional approaches were highlighted:

"The workload is clearly only getting larger. The volume of data is growing constantly, and it becomes increasingly implausible for human capital to keep on top of the sheer weight of data coming in its direction," noted one senior investment partner. This volume increases the likelihood of error and oversight while creating significant time constraints.

Global investing requires analyzing information across multiple languages, creating a substantial challenge for teams without multilingual capabilities. As one fund manager explained, "We can use data science to help significantly navigate parts of the market safely, in a way that we wouldn't be able to otherwise."

Research analysts often work differently, making standardization difficult. "Consistency of approach is less well curated than one might like as a senior leader," observed one panelist, pointing to the difficulty of ensuring quality control across large investment teams.

Traditional screening methods frequently miss companies whose AI involvement isn't reflected in standard metrics, particularly when qualitative information is buried in regulatory filings, earnings calls, and press releases.

The solution? AI-powered research tools that complement human analysis rather than replace it. As one panelist put it, "It's about allowing humans to have the burden lifted from them, to concentrate and spend more of their time doing the things that humans still do really well – creativity tasks, critical thinking – not turning pages in annual reports looking for a needle in a haystack."

For investment teams evaluating their research processes, the webinar made clear that AI implementation isn't just about automation – it's about enabling humans to focus on higher-value activities while maintaining consistent coverage across global markets.

 for more insights from our expert panel.

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Discover why traditional investment research struggles with data overload, language barriers, and consistency challenges, and how AI solutions are enabling analysts to focus on high-value activities.

 AI Investment Research: Why Traditional Methods Fall Short | Orbit

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Why Traditional Investment Research Falls Short in the AI Era

In the ever-evolving landscape of financial services, the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) has emerged as a game-changer, especially in the realm of financial data generation such as:

The accuracy in which AI, particularly LLM’s can extract data points from unstructured sources, such as Exchange filings and Disclosures, is reshaping how FinTech’s and Data Vendors approach Data production and Operations, providing a potent catalyst for

Some key operational benefits that the integration of AI and LLM’s can deliver include:

1. Complementing Internal Operational Processes

Financial institutions rely heavily on data vendors for comprehensive and timely information. Integrating AI and LLMs into existing operational processes enhances the capabilities of these vendors. By automating data extraction and analysis, these models help eliminate manual errors, reduce processing times, and ensure a more consistent and reliable flow of information. This strategy can open the door for data vendors to extend the scope and simplify the integration of their data sets without additional large capital investment in data operations.

ESG factors have become integral to investment decision-making, reflecting a growing awareness of sustainability and responsible investing. AI and LLMs contribute significantly by automating the extraction and analysis of ESG-related information from diverse sources, including news articles, social media, and official company disclosures. This process not only improves the accuracy of ESG data but also significantly improves speed-to-market for updates data points within days of a company material ESG disclosures.

One of the primary advantages of leveraging AI and LLMs within data generation operations is the acceleration of speed to market for new product offerings. These models can quickly analyze, extract and maintain datasets, assuming the models can be exposed to the official underlying sources of data. It is worth noting that, in our view, there will always be a need for human involvement in the data generation process, especially for performing delta checks where business rules have been triggered. Overall the combination of AI and human within data operations can deliver the speed-to-market, which is critical in an industry where timely information can make the difference between seizing market opportunities and missing out.

Market trends affirm the growing importance of AI and LLMs in financial services. According to a report by EY in October 2023*, Wealth and Asset Manager senior executives rated ‘Data Ingestion to drive alpha generation’ as the top area in which GenAI could have the greatest impact on their organization. The report emphasizes the positive impact on data accuracy and the ability to uncover previously inaccessible insights, aligning with the themes discussed in this blog.

In conclusion, the integration of AI and Large Language Models highlights how positive an impact GenAI can have on data generation within financial services – detailed thought needs to go into how these GenAI models are deployed, as well as the controls and QC processes that are built around them. By enhancing accuracy, increasing scope, and accelerating speed to market, these technologies could empower services providers to stay ahead in a dynamic and competitive landscape.

As the industry continues to embrace this transformation, the power of GenAI models could prove to be a disruptive force within financial data generation.

Orbit Financial Technology focuses on harnessing AI models to make sense and structure from millions of unstructured data sources – Contact Us to see how our platform is creating positive disruption in data production and operations as well as investment research and ESG due diligence.

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Transforming the Data Landscape

All you need to source, process and transform unstructured data into predictive indicators.Alternative datasets of global regulatory filings, corporate websites, China and digital assets. Bespoke data

The Impact of AI and Large Language Models on ESG and Financial Data Generation

A common misconception in the investment world is that firms can easily build their own AI research capabilities using widely available large language models. Our recent webinar with industry leaders revealed why this approach often falls short.

 "Many clients underestimate these challenges," explained our AI expert. "Implementing AI on a larger scale, such as analyzing thousands of companies or screening the entire market for interesting investment opportunities, presents a much bigger data engineering problem."

While building a proof of concept might be relatively straightforward, scaling it becomes exponentially more difficult. The panelists emphasized that data engineering "is not an area where you can simply throw in GPUs and servers and expect everything to work magically."

 Generic AI models can produce unreliable results when applied to specialized investment analysis. The solution isn't just about having a model but ensuring it receives the right information and follows structured logic.

"Most of the effort goes into curating documents and unstructured data stores," noted one speaker. This curation process – collecting, organizing, and preparing millions of documents in multiple languages – represents the hidden iceberg beneath the surface of successful AI implementation.

 Investment firms must weigh the cost and time required to build internal solutions against partnering with specialized providers. One fund manager shared that their payback time was "maybe four months, maybe less" when working with an established AI research partner.

For firms evaluating the build-versus-buy decision, the webinar highlighted that successful AI implementation requires three key components: high-quality data sources, powerful language models, and sophisticated systems to connect them – a combination that's challenging to develop internally without significant specialized resources.

 to hear our expert panel's detailed analysis.

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The Myth of the DIY AI Solution: Why Build vs. Buy Matters for Investment Research

“The pace of regulatory change, across global markets, is an on-going challenge for Asset Managers. With ISC’s regulatory expertise, combined with the Orbit GenAI and data gathering capability, we are delivering a unique proposition to deliver clarity and transparency across the global regulatory landscape – we look forward to working with ISC in this exciting partnership.”

“We are very excited to work in partnership with Orbit, their cutting-edge AI skills complement our deep experience in Regulatory Change and consultancy, together providing the perfect solutions for our Investment Management clients' Regulatory Horizon Scanning.”

Orbit Financial Technology utilises a comprehensive set of AI models to streamline unstructured data sourcing, cleansing and tagging at scale - empowering a multitude of Financial Services Use Cases.

Recognising the untapped potential of unstructured data across financial Services, Orbit has been focused on harnessing this power since 2015. We were aware of the complexity, significant manual effort, and costs associated with this task, so we set out with a straightforward aim - to help organisation's master unstructured data by harnessing AI and providing instant access to a depth of unique and reliable alternative data sets.

Our platform is designed to meet a wide range of use cases, out-the-box’, from GenAI powered investment research, quant-based data generation to ESG due diligence with full transparency to underlying sources.

For more information about Orbit, please visit  

Founded in 2005, ISC are a firm of talented consultants providing end to end change and transformation services to the Investment Management community. Our passion is to help Investment Managers maximise the efficiency and effectiveness of their business models - enabling them to deliver greater value to their clients.

ISC has innovative product and service offerings focused on - regulatory change, product, and transformation – that, when combined with our deep investment management knowledge, allows us to deliver transformative change programmes that enhance the capabilities of our clients and enables their Digital Transformation.

We continually strengthen our deep and broad knowledge of Investment Management whilst cultivating strong relationships with technology vendors and service providers to enhance our offering to our clients.

For more information about ISC, please visit 

Enquiries regarding this announcement should be addressed to: 
 

Orbit and ISC announce a strategic partnership

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January 2024: Investment Solutions Consultants (ISC) and Orbit Financial Technology are pleased to announce that they have entered into a strategic partnership, combining ISC’s extensive Regulatory change expertise with Orbit’s industry leading AI platform. The partnership aligns Orbit’s skills in working with unstructured data with ISC’s knowledge of regulatory change to create an innovative approach to Regulatory Horizon Scanning tailored to the individual investment manager.

Orbit Financial Technology and Investment Solutions Consultants announce a strategic partnership to provide Regulatory Horizon Scanning Services powered by Orbit AI to Investment Managers

Introducing our China A transcript app. We have over 5 years of historical data for all China A transcripts.

Use a range of parameters to provide accurate search results:

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Results are then displayed in list or block format:

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Open an individual result to see the full translated transcript with all relevant associated data and a dynamic event detector with stock position graph:

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Contact us and book a demo, if you’d like to know more about this or the wide range of problem-solving applications on offer through the Orbit Vision platform.

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China A Transcript App

All you need to source, process and transform unstructured data into predictive indicators. Alternative datasets of global regulatory filings, corporate websites, China and digital assets. Bespoke data

China A Transcript App – Now available

In the ever-evolving landscape of investment research, the integration of Generative AI (GenAI) and Large Language Models (LLMs) heralds a new era of transformative possibilities. The key areas that require thought and focus across the financial services landscape is to ensure that these models are utilized in the right way, are being fed with tokenized data (not just throwing a PDF to ChatGPT) to ensure accuracy and alleviate hallucinations, as well as having a clearly defined set of tasks and outcomes to be performed by GenAI.

Picture this: it's 2023, and Generative AI has emerged as a game-changer, akin to the transformative impact of smartphones in the past. Companies like NVIDIA, OpenAI, Meta, and Microsoft lead the charge, marking significant milestones in market valuation. The introduction of GenAI into the financial services industry, particularly investment research, represents a paradigm shift towards a data-centric approach and strategic utilization of unstructured data.

The key to unlocking the power of GenAI and LLMs lies in their ability to streamline tasks, leverage unstructured data sources, and enhance the speed of delivering investment opportunities to market. While GenAI isn't a standalone solution, it acts as a facilitator, providing structured information for more efficient analysis. Analysts, once mired in manual data gathering, now have the opportunity to become architects of sophisticated strategies, refining models, and curating data sources for valuable insights.

But before the power of GenAI and LLM’s can be realized, significant thought needs to go into the underlying data that is feeding these models. Before fully harnessing GenAI and LLMs, two critical considerations come into play: the accuracy and reliability of underlying data and defining specific outcomes. The quality of input data is paramount, requiring meticulous attention to ensure reliability and accuracy. Converting unstructured data into a semi-structured, tagged state is essential for enhancing accuracy and extracting valuable insights efficiently.

The integration of LLMs into the investment research landscape has further expanded possibilities, particularly in analyzing unstructured data sources like news sentiment, corporate filings, and regulatory documents. Imagine automating the analysis of vast amounts of textual data in real-time, providing accurate and up-to-the-minute insights into market sentiment. LLMs excel in navigating regulatory documents, extracting pertinent information, and accelerating project timelines, empowering financial professionals with actionable insights.

Personalization is another hallmark of the GenAI and LLMs revolution. Analysts can now configure models to execute multi-stage screening tasks autonomously, tailored to their specific objectives. This level of personalization not only enhances efficiency but also elevates the quality of decision-making, ensuring no potential opportunity goes unnoticed.

Looking ahead, the journey towards fully automated investment research is just beginning. While the industry may currently be around the test phase in research automation, the roadmap laid out by GenAI promises a future marked by innovation, customization, and scalability.

As analysts embrace their roles as architects of sophisticated strategies, the landscape of investment research is set to undergo a significant overhaul, unlocking the full potential of GenAI and LLMs to create a future where financial analysis is accurate, personalized, and efficiently navigated.

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Unlocking the Potential

Unlocking the Potential: How GenAI and LLMs Can Revolutionize Investment Research

Unlocking the potential: how GenAI and LLMs can revolutionise investment research

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Harnessing GenAI

Orbit - Whitepaper 

In the dynamic landscape of sustainable investing, where environmental, social, and governance (ESG) factors are gaining paramount importance, the need for data transparency has never been more critical. As asset managers strive to align their portfolios with ethical and sustainable practices, the demand for reliable, traceable data becomes the bedrock of responsible investment decisions.

One key challenge in the realm of sustainable investing is the diversity and complexity of data points involved—emissions data, water usage, social impact metrics—the list goes on. To navigate this maze successfully, asset managers will need to rely on data vendors to provide a clear audit trail and data lineage. This ensures that every reported data point can be meticulously traced back to its official underlying source. Such transparency is not merely a bureaucratic formality; it is the linchpin for robust due diligence.

Investment Management & Company Interactions

Looking at this from a company perspective, in a 2023 Willis Tower Watson Risk Manager Survey*, 74% of respondents stated that improving their ‘ESG Score’ is a core focus for their business, however, the report indicates that the magnitude of frameworks and ESG data relevance to their industry is making this task ever more complex.

From a stewardship standpoint, transparency is paramount for monitoring how companies are progressing towards their sustainable goals. It's not enough for companies to claim adherence to ESG principles; investors, regulators, and the public demand tangible evidence. Transparent data empowers asset managers to scrutinize the journey of each company in their portfolio, fostering accountability and trust.

Thus, there is a specific need for closer engagement between investment managers and underlying companies, however, this engagement needs to be supported with a scalable, AI supported process to drill into ESG disclosures in order to clearly discuss progress towards defined ESG goals.

For asset managers to truly meet regulations and deliver sustainable products to the market, data must be not only accurate but also cost-effective and consumption-based. This means having the flexibility to procure only the data that is pertinent to their investment strategies. In an era where resource efficiency is synonymous with sustainability, it is crucial that asset managers can access the information they need without unnecessary data license fees for a universe of data they may not actually need.

Consumption-based data models allow asset managers to be discerning in their choices, ensuring that they only pay and extract what they need, in line with their investment universe. This not only aligns with the principles of sustainability but also makes economic sense. It empowers asset managers to tailor their data procurement to the specific needs of their portfolios, avoiding unnecessary costs associated with surplus or irrelevant information.

In conclusion, the journey toward sustainable investing is intrinsically linked to the transparency and reliability of the data that guides it. From emissions data to social impact metrics, every piece of information must be traceable and accountable. With the rise of LLMs, the importance of cost-effective, consumption-based data models cannot be overstated.

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Navigating the Data Maze

In the dynamic landscape of sustainable investing, where environmental, social, and
governance (ESG) factors are gaining paramount importance, the need for data transparency has never been more critical.

Navigating the Data Maze: The Imperative of Transparency within ESG Data

Leverage the insightful power of unstructured data when selecting, monitoring and re-weighting your holdings for active ETFs. Orbit Insight is the go to tool for company filings, news and press releases covering 15+ global exchanges with results updated throughout the day.Attempting to digest the volume of data required to make informed and insightful decisions when selecting the stock holdings for an ETF or any fund for that matter is impossible without an advanced tool of some sort. Orbit Insight covers all public company filings, news and press releases and enables you to monitor for all listed companies ongoing with up to 3 months historical data as standard and up to 5 years on request.The tool allows you to save and manage themes and their associated concepts as well as creating and managing portfolios and setting up alerts to highlight filings, news and press releases for companies that are directly relevant to you.

View these and a raft of additional features in action here:

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Calling all active ETF Issuers

Calling all active ETF Issuers, fund managers and analyst

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The numbers are staggering. In 2024 alone, global regulatory fines for ESG-related violations exceeded $15 billion, with individual penalties reaching hundreds of millions of dollars. DWS paid $25 million to settle greenwashing charges. BNY Mellon faced $1.5 million in fines. Goldman Sachs settled for $4 million. And these represent just the tip of the iceberg—for every case that makes headlines, dozens more companies quietly revise their sustainability claims under regulatory pressure.

The message from regulators is crystal clear: the era of unchecked green claims is over. Yet as financial institutions scramble to ensure compliance, they face an almost impossible challenge. How can you verify that thousands of sustainability claims across hundreds of portfolio companies are accurate, consistent, and defensible?

The Hidden Complexity of Greenwashing Detection

Greenwashing rarely announces itself boldly. Instead, it hides in the gaps between what companies say and what they do, in the subtle evolution of language across reports, and in the disconnects between different disclosures. Consider these real patterns that signal potential greenwashing:

A company's sustainability report prominently features its renewable energy commitments, while buried on page 147 of its 10-K, the company's fossil fuel dependencies increase by 30%. Their CEO speaks passionately about carbon neutrality during earnings calls, but risk factors include language about "challenges in meeting environmental targets." Their marketing materials showcase green initiatives, yet supplier contracts reveal no sustainability requirements.

These disparities aren't necessarily intentional deception. Often, they result from organizational silos where marketing, investor relations, and operations tell different stories. But intent doesn't matter to regulators or investors demanding authentic sustainability leadership.

The Transformation Through Intelligent Transparency

This is where artificial intelligence is revolutionizing sustainable finance—not by making bold green claims easier, but by making them verifiable. Modern AI systems can analyze millions of documents across companies, time periods, and disclosure types to identify patterns invisible to human review.

Consider how AI-powered transparency works in practice. When a fund claims to follow strict ESG criteria, AI systems can systematically verify these claims by:

: Analyzing every sustainability claim against operational disclosures, financial filings, and third-party reports. If a company claims 50% renewable energy usage, AI traces this through utility contracts, facility reports, and regional energy mix data.

: Tracking how sustainability language evolves over time. Sudden changes in terminology, quietly dropped targets, or shifting baselines often signal problems before they become scandals.

: Following sustainability claims through complex value chains. A fashion brand's ethical sourcing claims can be verified against supplier disclosures, shipping documents, and regional labor reports.

: Comparing claims against industry peers to identify outliers. When one company claims dramatically better performance using similar operations, AI flags for deeper investigation.

The Competitive Advantage of Authentic Transparency

Forward-thinking institutions are discovering that AI-powered transparency provides competitive advantages beyond mere compliance:

: By identifying potential greenwashing early, firms can engage with portfolio companies to address issues before they become regulatory problems. One European asset manager using AI-powered analysis identified and remediated sustainability claim issues in 15% of their portfolio companies, avoiding potential fines and reputational damage.

: During investment evaluation, AI can assess the authenticity of sustainability claims in hours rather than weeks. This speed enables better investment decisions and stronger negotiating positions.

: In a market where 76% of institutional investors cite greenwashing concerns as a barrier to ESG investing, demonstrable verification capabilities become a powerful differentiator. Firms that can prove their claims with AI-backed analysis win mandates from increasingly skeptical allocators.

: When regulators come calling—and they will—firms with systematic verification processes and complete audit trails face far smoother examinations. The ability to show exactly how every claim was verified transforms regulatory reviews from adversarial investigations into collaborative discussions.

The path to AI-powered transparency requires thoughtful implementation. Based on successful deployments across leading institutions, key considerations include:

: Focus initial efforts on the most significant sustainability assertions—those featured in marketing materials, fund documents, and regulatory filings. Perfect verification of every claim isn't the goal; systematic verification of material claims is.

: AI shouldn't create new bureaucracy. The most successful implementations embed verification into existing due diligence, monitoring, and reporting processes. Analysts receive alerts about potential issues, not additional reports to review.

: Anti-greenwashing isn't a one-time exercise. As regulations evolve and sophistication increases, verification processes must adapt. AI systems that learn from each analysis become more effective over time.

: AI excels at identifying potential issues, but human judgment remains essential for interpretation and action. The goal is augmented intelligence, not the replacement of expertise.

With the UK's Sustainable Disclosure Requirements (SDR) now entirely in force, the stakes have never been higher. The FCA's anti-greenwashing rule applies to all regulated firms, with enforcement actions expected to ramp up significantly. Firms must ensure that sustainability-related claims are fair, transparent, and not misleading—a standard that's impossible to meet without systematic verification.

The SDR's detailed requirements for sustainability labels, disclosure templates, and naming conventions create multiple opportunities for inadvertent non-compliance. AI-powered analysis helps firms navigate these requirements by ensuring consistency across all communications and identifying potential issues before they become violations.

The anti-greenwashing revolution represents a fundamental shift in sustainable finance. Moving forward, unverified green claims will become as unacceptable as unaudited financial statements. The firms that thrive will be those that embrace radical transparency, leveraging AI to verify every claim and build unshakeable stakeholder trust.

This transformation extends beyond compliance. As AI makes greenwashing detection routine, authentic sustainability leaders will differentiate themselves through verified impact. The conversation will shift from "are these claims true?" to "what real change are we creating?"—a shift that benefits everyone committed to genuine sustainable finance.

The technology exists today to verify sustainability claims at scale, across languages, and throughout complex corporate structures. The question isn't whether your firm will adopt AI-powered transparency, but how quickly you can implement it before competitors or regulators force your hand.

As we approach Reset Connect London 2025, the sustainable finance community stands at a crossroads. Will we continue to struggle with manual verification processes that inevitably lead to failure? Or will we embrace AI-powered transparency that transforms compliance burdens into competitive advantages? The choice seems clear, and the time to act is now.

The era of unchecked green claims is over. Yet verifying thousands of sustainability claims across portfolios seems impossible. The solution? AI that systematically verifies every claim across millions of documents.

The Anti-Greenwashing Revolution: How AI-Powered Transparency is Reshaping Sustainable Finance

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In today's financial markets, where each investment decision (or its absence) can significantly impact fund performance, the quality of investment insights has never been more crucial. Large Language Models (LLMs) have emerged as powerful tools for processing vast financial data, from market reports to regulatory filings. However, a persistent challenge threatens the reliability of these systems: hallucinations, or the generation of plausible but factually incorrect information. For investment professionals, these AI-generated inaccuracies aren't just technical glitches – they're potential catalysts for significant financial losses.

Recent incidents in the financial sector have highlighted how AI hallucinations can lead to costly mistakes. When LLMs generate incorrect market analysis or misinterpret critical financial data, the consequences ripple through investment decisions, risk assessments, and compliance processes. Investment managers have reported cases where AI systems provided outdated interest rate information or generated fictional market trends, leading to suboptimal portfolio allocations and missed opportunities.

While many technology providers focus on refining model architectures, Orbit’s analysis reveals that the root cause often lies in input data quality and the procedures to control LLM over input data. Consider how investment decisions are made: analysts rely on a complex web of information sources – earnings call transcripts, market data, economic indicators, news, and exchange filings. When this data enters an AI system in an inconsistent, outdated, or poorly structured format, even the most sophisticated LLM will struggle to generate accurate insights.

The financial industry faces unique challenges in this regard. The sheer volume of financial data generated daily, combined with the need for real-time analysis, creates an environment where data quality becomes paramount. MiFID II and other regulatory frameworks demand not just accuracy but also traceability in investment decisions. Poor quality data leading to AI hallucinations can, therefore, create both immediate financial risks and potential compliance issues.

This is where Orbit's approach makes a crucial difference for investment professionals. Our platform explicitly addresses these challenges by pre-processing and storing all financial documents in an LLM-ready format. Think of it as having a team of expert analysts who meticulously verify, standardise, and index 

 before it reaches your decision-making process.


For portfolio managers and investment analysts, this means:

- More reliable market insights derived from clean, well-structured data

- Reduced risk of making investment decisions based on hallucinated information

- Faster analysis of market opportunities without compromising accuracy

- Enhanced compliance with regulatory requirements through traceable data lineage

The impact on investment operations is significant. One global asset manager using our platform reported a 60% reduction in time spent validating AI-generated market analysis, while another noted improved accuracy in their automated financial report processing. These improvements translate directly to more efficient investment processes and better-informed decisions.


As the financial industry continues to embrace AI for investment analysis, we've discovered that reducing hallucinations requires a comprehensive approach beyond simply developing more sophisticated models. Our experience has revealed three critical factors that work in concert to deliver reliable AI-powered investment insights:

First, trustable, high-quality input data serves as the foundation. Just as investment professionals wouldn't make decisions based on unverified information, AI systems require meticulously vetted, standardised financial data. This means ensuring that market data, company filings, and numerical indicators are accurate, up-to-date, and properly contextualised before they enter any AI pipeline.

Second, superior PDF processing capabilities are essential. Financial documents often arrive in complex PDF formats, containing crucial information in tables, footnotes, and structured sections. Advanced PDF processing ensures that every piece of valuable data is accurately extracted and preserved, maintaining the integrity of financial information from source to analysis.

Third, enhanced LLM control mechanisms provide the necessary governance layer. By implementing robust control systems, we can guide LLMs to focus on relevant financial information, respect data hierarchies, and maintain consistency in their analytical outputs. This controlled environment significantly reduces the risk of hallucinations while increasing the reliability of AI-generated investment insights.


Orbit’s success in eliminating AI hallucinations demonstrates that the future of investment analysis depends not just on model sophistication but on the holistic management of the entire data pipeline. As markets become more complex and data volumes grow, this comprehensive approach will become increasingly crucial for maintaining competitive advantage in the investment landscape.

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Discover how hallucinations in AI systems can lead to costly financial mistakes and how Orbit's approach ensures accurate, reliable investment insights by improving data quality and LLM control.

AI Hallucinations in Financial Insights - Orbit

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Garbage In – Garbage Out: The hallucination frustration

There is no doubt that financial industry is more and more aware of the value of alternative data. The number of alternative data platforms and data vendors is rapidly increasing. More and more companies are setting up a ‘Chief of Data’ role and data science teams to experience use cases on alternative data. Investment also increases, when for example Nasdaq acquired Quandl to advance the use of alternative data.

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