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Oct 8 2024

Why GenAI Won’t Change the Role of Data Professionals

The recent rise of GenAI has sparked numerous discussions across industries, with many predicting revolutionary changes across a broad range of professional landscapes. While the processes data professionals use and the volume of work they can sustain will change because of GenAI, it will not fundamentally change their roles. Instead, it will enhance their abilities, […]

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Data vs. AI Literacy: Why Both Are Key When Driving Innovation and Transformation

I have written before about the 5Ws of data and how important metadata – data about data – really is. This knowledge helps connect and contextualize data in ways that previously would take hours of knowledge and information mining. We have the tools now to automate this process and display it in a knowledge model of the data, […]

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Unstructured Data Hinders Safe GenAI Deployment

Enterprises are going all in on generative AI (GenAI), with the technology driving a massive 8% increase in worldwide IT spending this year, according to Gartner. But just because businesses are investing in GenAI doesn’t mean they’re broadly implementing it in actual production. Organizations are eager to wield the power of GenAI. However, deploying it safely […]

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Answering the Build vs. Buy Question for Generative AI

Building custom generative AI (GenAI) technology solutions is the best way to gain a competitive edge by leveraging GenAI tools and services tailored to your business. On the other hand, building GenAI models from scratch is a costly and complicated endeavor – which is why many businesses instead settle for a genAI strategy wherein they […]

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Streamlining Your Data Needs for Generative AI

Companies are investing heavily in AI projects as they see huge potential in generative AI. Consultancies have predicted opportunities to reduce costs and improve revenues through deploying generative AI – for example, McKinsey predicts that generative AI could add $2.6 to $4.4 trillion to global productivity. Yet at the same time, AI and analytics projects have historically […]

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Why Effective Data Management is Key to Meeting Rising GenAI Demands

OpenAI’s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, it’s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized. While the possibilities are […]

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Lost in Translation: Language Gap Holds Back GenAI in Life Sciences Industries

Across industries, organizations continue to seek out a range of use cases in which to deploy advanced intelligence. With the development of generative artificial intelligence (GenAI), various industries are leveraging the technology to process and analyze complex data, identify hidden patterns, automate repetitive tasks and generate creative content. The promise of GenAI is transformative, offering […]

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Running Generative AI in Production – What Issues Will You Find?

As your data projects evolve, you will face new challenges. For new technology like generative AI, some challenges may just be variations on traditional IT projects like considering availability or distributed computing deployment problems. However, generative AI projects are also going through what Donald Rumsfeld once called the “unknown unknowns” phase, where we are discovering […]

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How to Assess GenAI’s Impact on Your Business

Since the beginning of 2023, generative AI (GenAI) has quickly made a significant impact across an expanding range of industries and applications. In just over a year since its groundbreaking debut, there’s much to celebrate about GenAI – and even more to still uncover and understand. Today, 79% of employees report at least some exposure to AI, […]

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Generic LLMs vs. Domain-Specific LLMs: What’s the Difference?

Large language models (LLMs) are a special type of AI model that uses natural language processing (NLP) to understand and generate text similar to human language. They are a form of generative AI trained on textual data to produce textual content. ChatGPT stands out as a well-known example of generative AI. Trained on massive datasets, LLMs […]

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Beyond Generative AI and the Future of Innovation

The power of generative AI (GenAI) seems to have no limits. Every day, we see new barriers being broken and new use cases that no one thought possible. And yet, I can’t help but notice that most of these advances we’re hearing about revolve mostly around content creation. While remarkable in its own right, this begs the […]

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Good Data Quality Is the Secret to Successful GenAI Implementation

You wouldn’t build a house without a concrete foundation. So why are many technology leaders attempting to adopt GenAI technologies before ensuring their data quality can be trusted? Reliable and consistent data is the bedrock of a successful AI strategy. Incomplete or inconsistent data prompts GenAI models to propose equally unreliable outputs, calling the basic […]

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Data-Driven Defense: AI as the New Frontier in Business Security

Major business setbacks due to risk management failures happen every year. They are also some of the costliest, adding up to millions of dollars in regulatory fines, lawsuits, payouts, and lost brand value. Leaders want to avoid these types of issues and rely on sound internal data management to mitigate risk and maintain confidence and […]

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Maximizing Business Value with Generative AI

Have we ever seen something get adopted so quickly as generative AI (GenAI) compared to the past? Think about it: ChatGPT launched in 2022 and gained 100 million users in two months. In comparison, we have been hearing about AI for a few years, but the adoption rates of AI have varied from 25% to […]

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Why It’s Time to Rethink Generative AI in the Enterprise

If you’ve been keeping an eye on the evolution of generative AI (GenAI) technology recently, you’re likely familiar with its core concepts: how GenAI models function, the art of crafting prompts, and the types of data GenAI models rely on. While these fundamental components within GenAI remain constant, the way they’re applied is transforming. The […]

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Why the Rise of LLMs and GenAI Requires a New Approach to Data Storage

The new wave of data-hungry machine learning (ML) and generative AI (GenAI)-driven operations and security solutions has increased the urgency for companies to adopt new approaches to data storage. These solutions need access to vast amounts of data for model training and observability. However, to be successful, ML pipelines must use data platforms that offer […]

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Generative AI Challenges and Opportunities for Modern Enterprises

Generative AI (GenAI), machine learning (ML), and large language models (LLMs) are all becoming increasingly important to modern enterprises, but achieving measurable value from AI is still a challenge. Part of the issue is that a well-trained AI model relies on a large amount of data, and for many companies, organizing and making use of […]

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How GenAI Bridges the Data Gap Between CMOs and CFOs

Marketing budgets are never entirely safe. While it may seem like pressure is easing as global economic estimates turn slightly sunnier, consumer demand is still getting more expensive to capture and close – which means scrutiny from finance chiefs is as tough as ever. To keep investment flowing, CMOs need to get better at not only boosting […]

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Author: Harriet Durnford-Smith

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Jan 9 2024

Balancing Act: The Value of Human Expertise in the Age of Generative AI

Humans are considered the weakest link in the enterprise when it comes to security. Rightfully so, as upwards of 95% of cybersecurity incidents are caused by human error. Humans are fickle, fallible, and unpredictable, making them easy targets for cybercriminals looking to gain entry to organizations’ systems. This makes our reliance on machines that much more important. […]

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2024: Fewer Hallucinations, Private LLMs, and IP Challenges for GenAI Content

For those of us who have been in the AI field for a while, we’ve weathered at least two “AI winters,” interspersed with phases of rapid progress. However, 2023 stands out as a pivotal moment in the trajectory of AI. ChatGPT and other large language models (LLMs) have democratized AI for non-experts, offering immense utility, […]

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The Benefits of Generative AI for Banking & Financial Leaders

Generative AI is a subset of Artificial Intelligence (AI) that focuses on creating artificial data or content. It uses deep learning algorithms to generate images, videos, or audio based on the data given to it. Instead of learning from data, generative AI creates brand-new data.

Generative AI is transforming data analytics in the financial services industry, presenting new opportunities to enhance customer service, increase revenue, improve security, reduce risks, optimize investments and strategic planning, and more. Here are some common uses and benefits of generative AI in financial services:

Chatbots: Banks can use generative AI to create chatbots that mimic human conversation through text or voice interactions. Using chatbots can improve customer service, cut costs, and boost revenue. For example, chatbots can save banks money by automating routine customer service functions such as answering questions about account balances and performing routine tasks such as making transfers and sending messages. More advanced uses include providing personalized recommendations and sales based on a customer’s history and activity.

Fraud Detection and Prevention: Generative AI is supplementing traditional fraud analytics with models that can identify abnormal patterns in large volumes of financial transactions so that financial institutions can halt suspicious transactions faster. Financial companies are also using generative AI to create synthetic data that simulates fraud so they can develop more robust fraud detection algorithms.

Anti-Money Laundering: Using generative AI to analyze large volumes of financial data such as transactions, accounts, customer profiles, and company information. Know Your Customer (KYC) data can identify patterns and anomalies that may indicate money laundering activities.

Credit Risk Assessment: Generative AI models can determine credit risk more accurately and much faster by analyzing vast amounts of data, including financial statements, credit scores, transaction histories, and other relevant data. This can lead to better lending decisions that reduce credit risk.

Credit Reporting: Companies in the financial services industry can use generative AI to automatically create credit reports and other financial documents. This can streamline loan application and approval processes, reducing paperwork and improving efficiency.

Algorithmic Trading: Traders can use generative AI to potentially achieve higher returns. Generative AI helps develop trading algorithms that produce trading signals for when to buy or sell a security and that predict market movements.

Portfolio Management: Generative AI can help optimize portfolio allocations by generating asset combinations and simulating their performance. Portfolio managers can use this information to build efficient portfolios based on criteria such as risk tolerance and return objectives.

Asset Management: Businesses can use generative AI to analyze market data and forecast asset prices, interest rates, and other economic trends. This information is valuable for making investment decisions and managing financial assets. Generative AI excels in analyzing unstructured data, such as social media sentiments and news articles to help investment managers gain insights into investor perceptions and market shifts.

Strategic Planning: A company in financial services can leverage generative AI to develop predictive models for financial metrics such as customer churn, account balances, and revenue. Better forecasts of these metrics can improve strategic planning and resource allocation.

Generative AI and the Actian Data Platform

Generative AI is a versatile tool that presents many opportunities for data analytics within the financial service industry. However, generative AI requires the right data platform to be successful. The Actian Data Platform is the first as-a-service solution to unify analytics, transactions, and integration. Its flexible cloud, on-premises, and hybrid cloud architecture brings you trusted, real-time insights, making it easier to get from data source to decision with confidence. The Actian platform’s low, no-code integration with data quality and transformation options make it easier and more flexible to address more generative AI needs/use cases.

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Generative AI and the Actian Data Platform