Top Trends in Big Data for 2024 and Beyond

Why Finance Is Deploying Natural Language Processing

In the ever-evolving landscape of finance, Natural Language Processing (NLP) has emerged as a game-changer. This innovative technology, which enables computers to understand and interpret human language, is transforming the way financial institutions operate, enhancing efficiency, and offering new insights. Here's a deep dive into why finance is increasingly leveraging NLP and its profound impact on the industry.

NLP is a subset of artificial intelligence (AI) focused on the interaction between computers and human language. It involves various processes such as text analysis, sentiment analysis, machine translation, and more. For the finance sector, NLP's capabilities are opening up new avenues for data analysis and decision-making.

Enhancing Efficiency

Financial institutions deal with vast amounts of unstructured data daily, from news articles and reports to social media feeds and regulatory filings. Manually processing this data is time-consuming and prone to errors. NLP automates this process, enabling institutions to quickly and accurately sift through data, identify relevant information, and generate actionable insights.

One significant application is in the automation of routine document processing tasks. For example, legal and financial documents can be reviewed and analyzed using NLP algorithms, extracting key information and identifying any potential issues. This not only saves time but also reduces the risk of human error.

Improving Risk Management

Risk management is critical in finance. NLP helps in identifying potential risks by analyzing text data from various sources. For instance, it can detect negative sentiment in news articles about a particular company, signaling potential financial instability. This proactive approach allows institutions to mitigate risks more effectively.

By monitoring news sources, financial reports, and social media for signs of market volatility or emerging threats, NLP can alert risk managers to issues that may impact their portfolios. This early warning system allows for quicker responses to potential risks, enhancing the overall stability and security of financial operations.

Boosting Customer Experience

NLP enhances customer service by powering chatbots and virtual assistants that can understand and respond to customer queries in real-time. These AI-driven tools provide personalized service, handle routine inquiries, and free up human agents to focus on more complex issues, thereby improving overall customer satisfaction.

For example, a customer querying their account balance or transaction history can receive instant, accurate responses from an NLP-driven chatbot. More complex issues can be escalated to human agents, who are better equipped to handle them thanks to the time freed up by the chatbot.

Driving Investment Strategies

Investment firms use NLP to analyze market sentiment and predict stock movements. By processing news articles, social media posts, and financial reports, NLP models can gauge public sentiment and provide insights into market trends. This information helps investors make informed decisions and develop robust investment strategies.

NLP can also be used to analyze earnings calls and other corporate communications. By identifying key themes and sentiments in these communications, investors can gain deeper insights into a company's performance and prospects, informing their investment decisions.

Facilitating Compliance

Regulatory compliance is a major challenge for financial institutions. NLP aids in compliance by automating the monitoring of regulatory changes and ensuring that all necessary actions are taken to adhere to new guidelines. This reduces the risk of non-compliance and associated penalties.

For example, NLP can be used to monitor and analyze regulatory updates, flagging any changes that may impact the institution's operations. This allows compliance officers to stay on top of regulatory requirements and ensure that the necessary steps are taken to remain compliant.

Augmenting Market Research

NLP can significantly enhance market research by analyzing large volumes of text data from various sources. This includes news articles, social media, blogs, and forums, providing insights into market trends, customer preferences, and competitor activities.

For instance, financial analysts can use NLP to track sentiment around specific stocks or sectors, identifying emerging trends and potential investment opportunities. This allows for more informed and timely investment decisions, giving institutions a competitive edge.

Streamlining Operations

Beyond data analysis and customer service, NLP can streamline various other operations within financial institutions. This includes automating the generation of reports, summarizing lengthy documents, and even translating documents into multiple languages.

By automating these tasks, NLP frees up valuable time and resources, allowing financial professionals to focus on higher-value activities. This enhances overall productivity and efficiency within the organization.

The adoption of NLP in finance is a testament to the industry's commitment to innovation and efficiency. By leveraging this technology, financial institutions can process vast amounts of data quickly, manage risks effectively, enhance customer service, and ensure compliance. As NLP continues to evolve, its applications in finance are expected to expand, driving further transformation in the industry.

Stay ahead in the financial sector by embracing the power of NLP. Explore how this technology can revolutionize your operations and provide a competitive edge in an increasingly data-driven world.

This detailed blog outlines the various ways NLP is transforming the finance sector, highlighting its benefits and potential applications.

How AI And Business Analytics Foster Better Collaboration

It's not only people who collaborate one with another. Generative AI and business analytics are ... [+] getting in on the action, as well.

AFP via Getty Images

AI applications are having a very real impact on productivity and collaboration. According to MIT's Sloan School, generative AI can improve the performance of a highly skilled worker by 40% over their peers who don't use it. These improvements don't just apply to individual work, either.

When generative AI and business analytics are combined in an effective manner, it's often referred to as GenBI. As a non-tech person, I find it fascinating how technologies such as AI and and GenBI are being used to help make person-to-tech collaboration far more effective than it has been in the past. And even more interesting is how it can even help foster better person-to-person collaboration, as well.

What Is GenBI?

Generative BI combines generative AI and business analytics tools to help make analytics data more useful and accessible for business users and data teams alike. It works by connecting a generative AI solution with a business's existing data sources and analytics tools.

Notably, GenBI systems don't generally rely on more public-facing generative AI solutions like ChatGPT, largely due to the challenges of uploading so much data to a LLM, and the potential data security issues involved with sharing sensitive corporate data in such a way. However, these systems do still aim to provide a ChatGPT-like experience to create a straightforward and easy approach to business analytics.

Instead, as Avi Perez, CTO at Pyramid Analytics explained in a recent interview with DATAcated, GenBI tools "allow users to submit their request. We hand it to the LLM together with a whole bunch of descriptions of ingredients — what I call 'ingredients' — to say, 'Here's what is in the user's database.' And the LLM basically comes back with the recipe and says, 'This is how you should ask the question the user is asking for.' The question is then executed on the data itself."

Notably, as Perez explains, GenBI tools are the ones "talking to your data, not the LLM. And that makes it scalable, functional, secured, and I actually think it's the only way in the current era that we know of to make your corporate data work with an LLM."

How GenBI Is Being Used Today

By using the natural language processing models inherent to generative AI, the people using that information can enjoy greater ease of use as they try to gain relevant insights from their data.

This ensures that the GenBI tool actually delivers answers and results that make sense for the question or request that the user has submitted. With such a model, business users are often able to get answers to business analytics queries in less than 30 seconds, even when submitting a question with relatively vague or plain language.

With the help of GenBI, users are able to look at the information inside the data sources to quickly and effectively perform relevant business analytics tasks, such as creating dashboards and reports, forecasting future results, investigating business performance anomalies and more.

By helping deliver meaningful data points and reports based on the information request, users don't have to spend countless hours digging through data, coding queries or generating reports manually. Instead, they can focus on actually making use of that information for their own collaborative needs.

How GenBI Can Foster Greater Collaboration

As highlighted in a recent report by Zoom, AI can play a powerful role in improving collaborative efforts. According to the report, 75% of organizational leaders whose teams use AI say it has improved their collaborative abilities.

Ultimately, the biggest benefit of using AI tools comes through time savings. Collaboration often requires significant time. Tasks such as sharing action items, obtaining and evaluating data and sending communication are essential, but can become extremely time-consuming and detract from tasks that are bigger drivers toward the partnership's strategic goals and activities.

By automating routine or mundane tasks, as well as tasks that can be challenging for individual workers, AI — and GenBI — allow individuals involved in collaborative efforts to save time and focus on things like creative brainstorming sessions or decision-making.

As this example illustrates, GenBI can be a powerful resource for improving internal collaborations. GenBI can help individuals with data preparation and complex analysis, enabling them to work together to identify and communicate crucial insights that can have a very real impact on overall performance — all in an extremely time-efficient manner.

Essentially, GenBI helps democratize a business's available data so that organizations can optimize marketing strategies, boost their productivity, better understand the dynamics of their sales and logistics processes and more. With the capability to evaluate and utilize any of your organization's data points, collaborators can enjoy greater transparency and communication as they find solutions to major pain points.

Unleashing Greater Tech Potential

With GenBI tools, businesses have another resource in their arsenal to improve how they access and use data — and that can ultimately be a powerful game-changer in how they collaborate both internally and externally. By enabling all team members to better understand and utilize available data, organizations can operate in a more efficient manner and with more reliable data-backed insights.

When these insights are used to guide collaborative efforts, the potential outcomes at each stage of the partnership can be greatly enhanced and streamlined. From improving internal operations to ensuring clear and transparent communication with external partners, those who take full advantage of GenBI are poised to give themselves a distinct competitive advantage.

The Transformation In World Of Human-Computer Interaction

In February 2023, we were working with a market research analytics company to help them automatically summarise voice-based user conversations from the focus group discussions. Although the OpenAI's ChatGPT LLM model had been released three months earlier, in November 2022, it was not initially on our radar due to the scarcity of developer API access and the prevailing uncertainty in its ecosystem. However, the capabilities of the LLM model soon proved to far outperform any traditional approaches, fundamentally transforming how we handle tasks involving Natural Language Processing (NLP).

Advancements in technology typically occur through incremental changes. However, once an inflection point is reached, the technology and its adoption become widespread. To outside observers, it might seem as though the change appeared suddenly, out of the blue, but to those working deep in the field, it seems like a natural trajectory. Having said that, predicting the future trajectory of Human-Computer Interactions, a field we at the Technology Innovation Hub at IIT Mandi, are deeply involved in, is a task easier said than done. Let me explain why.

Human-Computer Interaction (HCI): "Technology in Harmony with Human Needs"

In most fields, we tend to envision technological progress as following a somewhat linear trajectory. Take, for instance, Moore's Law, which posits that the number of transistors on a microchip doubles approximately every two years. While the actual growth is exponential, our human minds simplify this into the notion that every two years, we'll have more powerful computers.

However, in the realm of Human-Computer Interactions (HCI), such linear inferences prove challenging. HCI isn't a standalone discipline; rather, it's intricately intertwined with other fields like Computer Science, Industrial Design, Cognitive Psychology, and Linguistics, each dictating its trajectory, with potential independence and insularity. Consequently, for every technological advancement, HCI must pivot its response by spiralling around it and drawing the relevance from these adjacent domains. Thus, for breakthroughs or advancements to materialise in HCI , it's imperative for relevant and inclusive progress to occur across its associated fields.

Consider the famous 1968 event led by Douglas Engelbart, where he unveiled a functioning computer system with capabilities that are now commonplace, such as word-processing, real-time collaboration, video-conferencing, and the use of a computer mouse to navigate the computer screen, among others. This event is widely referred to as "The Mother of All Demos," as it offered a glimpse into the future of HCI. However, it would take another two decades for Engelbart's vision to materialise fully. The realisation of this future necessitated advancements such as the development of Graphical User Interfaces based operating systems, invention of the internet, enhancement of computing power in microchips, and the evolution of more intuitive interactions, ultimately leading to computers becoming truly personalised.

In essence, the goal of the field of HCI is to adapt technological advancements for widespread human use. However, in many instances, HCI also drives their progress, aiming to make technology's benefits accessible to the majority. HCI is adept at identifying pertinent problems to address because it puts the needs of the humans ( the end user) at the centre.

For instance, one researcher at our technology hub is working on developing "Electroencephalogram (EEG)-based Early Detection of Parkinson's Disease" as a substitute for commonly known MRI scans. Administering MRIs to patients with the disease  is challenging due to their involuntary movements. However, utilising EEG presents its own challenge: handling vast amounts of data and effectively applying data analytics to isolate signals crucial for accurate detection. Data analysis of EEG output is more complex to read for the medical technician than reading an MRI report. Preparing the patient for an EEG is easier than for an MRI scan. If EEG Data analysis is made simpler, EEG based data output  could potentially supplant MRI scan reports  for the patients. This is an example where HCI should help assess the needs of the end users (the patient and the medical technician both need to benefit) and push the innovation making life easier.

So thoughtfully we have distilled the goal of HCI through the mission statement of "Technology in harmony with Human needs".

The Convergence of Language and AI – world of HCI is getting upended

With this backdrop, let us plot a trajectory of Human-Computer interactions amidst the rapid advancements in Natural Language Processing (NLP) and Generative Design. At the core of this revolution is the demonstration of the ability of computers to genuinely "comprehend" human intentions and deliver results aligned with their expectations.

In less than a year since their debut, Large Language Models (LLMs) have already evolved into Large Multimodal Models (LMMs), capable of generating information across various data types or modalities, including text, images, audio, and video. 

However, the most pivotal advancement would lie in their capacity to "think," "decide," and "take action" autonomously. This capability marks a significant step towards their inevitable evolution into Large Action Models (LAMs). With LAMs, these models will possess the ability to execute actions on behalf of users frictionless, requiring minimal input. Similarly, the field of Generative Design has witnessed an evolution from Generative Images to an early stage of Generative Videos. However, but what we are ultimately moving towards is Generative Immersive Worlds

The current and forthcoming advancements indicate a transition from hardware-led innovations in software to software-led innovations in hardware. The objective is to develop interfaces and tools where the system adapts to the user and the user adapting to the system.

The elixir would be to integrate these models to create a truly ever-active hyper personalised assistant with user-specific contextual memory, that can take actions on the user's behalf. As an example, something as trivial as proactively emailing that you might reach 10 minutes late, or calling an ambulance when it detects unusual inactivity or distress.  

Building trust in automated models poses a significant challenge. Therefore, our primary objective as a Human-Computer Interaction (HCI) field is to develop technologies that humans can confidently "trust" and the interaction feels "natural". Just as we don't doubt the accuracy of a calculator, our aim is to instil trust in users regarding these advanced models. We aim to do this by employing emotive and perceptual engineering to make tools for micro-intervention that feel hyper-personalised. We aim to do this guided by the principle: "*Don't make them think, think for them*," with "them" referring to the end user. 

Over the last few decades, there have been incremental changes across fields burbling across all fields leading to the emergence of revolutionary prototypes such as augmented reality-based, brain-computer interfaces, virtual reality, screenless, keyboard less, voice-controlled devices. And today, we find ourselves perched on the precipice, where all of these advancements are on a collision course, pointing towards an imminent revolutionary tipping point. 

The role of Human-Computer Interaction (HCI) lies in adeptly amalgamating these advancements to craft intuitive devices and tools with corresponding applications, placing user needs at the forefront. We may soon be called Human-Centred Interaction as the new HCI!

Disclaimer

Views expressed above are the author's own.

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