What Is NLP (Natural Language Processing)? - IBM

NLP: The Game-changing Technology For The Financial Sector

Finance institutions are using natural language processing (NLP) to analyse performance drivers and predict market trends, which is revolutionising the financial sector.

When it comes to market research, content evaluation, and risk management, NLP is quickening the speed of change in the financial sector. Demand for BERT (Bidirectional Encoder Representations from Transformers) NLP is rising among financial institutions. BERT is an open-source NLP machine learning framework. The purpose of BERT is to provide context to assist computers understand confusing terms in text.  This reflects the growing popularity of NLP in the financial services industry.

In today's financial world, data and information are becoming more important than ever before. With the help of NLP, financial firms around the world can gain a significant advantage by analysing vast amounts of text and words. This presents a great opportunity for growth and success in the industry.  

Overview of Natural Language Processing

The Bank of America is embracing natural language processing technology to stay competitive in the market! The use of natural language processing by other banks, such as HSBC, to improve processes and gather market data is becoming more widespread.

COIN, a piece of machine learning software developed by JPMorgan Chase, would supposedly aid the bank's legal departments in their assessment of thousands of legal papers via natural language processing.

So, What's the big deal about NLP in the finance industry? 

The Answer is Simple because the recovery of data from the kinds of unstructured sources that financial organisations often struggle to mine is now easy. 

An analysis of the AI Opportunity Landscape in banking found that 39% of the AI vendors in the sector offer solutions that involve NLP.

Benefits of Using Natural Language Processing in Financial Services

Finance and insurance firms utilise NLP to lessen the amount of routine, error-prone tasks that employees must do every day. It has a significant effect on both the efficiency with which applications are processed and the friendliness with which customers are treated. So, what other applications make NLP in the finance industry more beneficial?

NLP helps to undertake investment analysis, which entails compiling summaries of market knowledge to get a better understanding of the market. 

ESG evaluations are another way in which financial services organisations might utilise NLP to evaluate the competitive landscape. Investors often use ESG ratings as a guide when deciding which businesses to put their money into.

NLP may also be employed to guarantee compliance with regulations. Companies may analyse business data and identify particular key terms using text categorization and NER to see whether its traders are in compliance with applicable laws and regulations.

NLP can identify illicit transactions and scams, which are costly for financial organisations. The company may then employ text mining and NER to flag terms related to fraudulent conduct after using OCR to put the raw information into an acceptable form. 

NLP tools help manage risk throughout a financial institution by highlighting potential trouble spots. This lessens the potential for financial harm even more.

What NLP Means for the Future of Finance?

Financial institutions in the Post-COVID era rely on data produced by NLP systems for market analysis and risk assessment. Bank executives are using NLP systems to assess the impact of the epidemic on their businesses and make informed decisions.

By 2023, financial institutions are expected to save a total of 862 million hours owing to the use of AI-powered chatbots, making the investment and adoption of AI-related fintech apps an easy decision. The more at ease customers are with utilising conversational technology, the more time, money, and effort may be saved for everyone concerned.

The worldwide NLP market has been worth $9.2 billion and a survey by Quince Market Insights projects that this number will increase by 18.4 percent between 2020 and 2028. Therefore, the coming decade will see tremendous advances in the practical applications of natural language processing, and financial services are expected to be a major contributor to this expansion. 

Disclaimer

Views expressed above are the author's own.

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AI In Financial Services: Transforming Stock Trading

Atal Bansal is the Founder and CEO at Chetu, a global U.S.-based custom software solutions and support services provider.

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Artificial intelligence (AI) is transforming the way that investment decisions are made. Rather than relying primarily on intuition and research, traditional methods are being replaced by machine learning algorithms that offer automated trading and improved data-driven decisions.

The Evolution Of Stock Trading, Computers And AI

For decades, traders used intuition and manual research to select stocks. Stock pickers often used fundamental analysis, which evaluated a company's intrinsic value by researching its financial statements, management, industry and competitive landscape. Some used technical analysis, which identified patterns and trends by studying past price and volume data.

By the mid-1980s, more powerful computers and the popularization of personal computers spurred computer use in trading. The New York Times published an article in 1986 about how many large investors started using personal computers to "tap electronic libraries for stock market information."

Although some people may think AI is a new technology, the rudimentary concepts of AI and its subsets date back more than 50 years. The financial world continued to adopt AI technology as advancements in machine learning, deep learning and natural language processing occurred, resulting in higher levels of accuracy.

AI Technology

Today, artificial intelligence algorithms, which are more complex and sophisticated, make use of a wide range of technologies:

• Machine learning (ML) algorithms can analyze enormous volumes of financial data in real time, allowing them to spot patterns and trends and make more informed trading decisions.

• Deep learning (DL) algorithms use sophisticated neural networks, which mimic the human brain, to extract meaningful information from unstructured data, including text, audio and images.

• Natural language processing (NLP) allows computers to comprehend human languages in news articles, online sentiments and other information to identify events that move markets and assess investor sentiment.

• Predictive analytics use machine learning, deep learning, data mining, statistical modeling and other sophisticated technologies that can forecast future price movements and market volatility based on historical data and current trends.

• Visualization tools allow trading professionals to grasp complicated data sets better and learn from AI-generated forecasts and suggestions.

• Risk management systems' integration with AI algorithms allows it to monitor trading activity and assess possible risks.

How AI Is Benefiting Investment Trading

These tools have brought many benefits to investment trading, such as increased efficiencies, automated many aspects of trading and removed human emotions from decision-making. AI trading programs make lightning-fast decisions, enabling traders to exploit market conditions.

Enhanced decision-making occurs because AI technologies like machine learning, deep learning and NLP can analyze massive amounts of data and find patterns that people would otherwise be unable to detect. With AI, human emotions do not impact stock picking because algorithms make data-driven decisions.

Trading in global markets is now more readily available because AI algorithms can work 24/7, creating opportunities in different time zones. Risk management integration helps protect traders from making ill-informed decisions based on bias, fatigue and emotions.

The Future Course Of AI In The Financial World

AI algorithmic trading's impact on stocks is likely to continue to grow. Software developers will develop more powerful and faster algorithms to analyze even larger datasets. The programs will continue recognizing complex patterns, adapting faster to changing market conditions and adjusting trading strategies in nanoseconds. The financial markets landscape may become dominated by AI trading, which could consolidate power with a few firms that can develop the most sophisticated programs.

One factor weighing on the future of AI-driven investment strategies and trading is whether AI can replicate human intuition and creative thinking. Who has the advantage? Is it the trader using only an advanced software program, investors combining their knowledge, instincts, imagination and inventiveness with AI tools, or ones who don't use technology that make the best financial decisions?

AI has emerged as a transformative force, reshaping industries and practices. As we navigate this new era of technological innovation, the future unfolds between the realms of human ingenuity and algorithmic precision.

The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.

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The Dawn Of Agentic AI Systems: Revolutionizing Financial Services

Vishal Dugar, Growth Leader at Cognizant Technology Solutions, has 20+ years of global experience in banking & financial services.

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The financial services sector is undergoing a profound metamorphosis with the advent of agentic AI systems. Based on my career-long experience in IT and digitization, I believe that these systems, underpinned by cutting-edge technologies such as artificial intelligence (AI) and machine learning (ML), are poised to revolutionize the industry by significantly boosting productivity and fostering innovation.

Agentic AI systems are sophisticated autonomous entities endowed with the capability to execute tasks, make informed decisions and learn from interactions independently, without necessitating human intervention. These systems transcend conventional artificial intelligence by integrating a "chaining" mechanism. This advanced feature enables the AI to execute a series of actions in response to a singular request, effectively deconstructing intricate tasks into smaller, more manageable components.

The evolution of agentic AI systems over the past few decades has been nothing short of transformative. In the early stages, automation within the financial sector was confined to rudimentary tasks such as transaction processing. These initial systems were relatively simplistic, designed to handle repetitive and straightforward operations. But technological advancements like machine learning have propelled agentic AI systems into a new era of sophistication.

Modern agentic AI systems are now endowed with the capability to execute complex functions. They are adept at conducting comprehensive risk assessments, analyzing vast datasets to identify potential threats and opportunities. Furthermore, these systems have revolutionized portfolio management, enabling the dynamic and strategic allocation of assets based on real-time data and predictive analytics.

This progression underscores a broader trend toward increased reliance on intelligent systems to enhance decision-making processes and operational efficiency in finance. The integration of AI and ML into agentic AI systems represents a significant leap forward, transforming them from mere tools of convenience into indispensable components of modern financial infrastructure.

The foundational pillars of agentic AI systems encompass several advanced technologies, each playing a crucial role in their functionality and efficiency:

• AI and ML are at the core of these systems, providing the capability to assimilate vast amounts of data and continuously improve performance through iterative learning processes. AI encompasses a broad range of techniques that enable machines to mimic human intelligence, including problem-solving, decision making and pattern recognition. ML, a subset of AI, focuses on developing algorithms that allow systems to make predictions and learn from available data. This dynamic learning ability ensures that agentic AI systems can adapt to new information and evolving scenarios, enhancing their effectiveness over time.

• Natural Language Processing (NLP) is another critical component, enabling these systems to understand, interpret and generate human language. NLP combines computational linguistics with AI to process and analyze large volumes of natural language data. This capability allows agentic AI systems to engage in meaningful and contextually appropriate interactions with users, facilitating seamless communication and improving user experience. By understanding nuances such as tone, intent and context, NLP ensures that these systems can respond accurately and empathetically to human inputs.

• Blockchain technology underpins the security and transparency of transactions within agentic AI systems. Blockchain is a decentralized ledger that records transactions across multiple computers in a way that ensures the data cannot be altered retroactively. This technology provides a robust framework for secure data exchange, protecting against fraud and ensuring the integrity of information. By leveraging blockchain, agentic systems can offer transparent and verifiable transactions, fostering trust and reliability among users. It should be noted that blockchain is not a must for agentic AI systems.

Together, these technologies create a synergistic framework that empowers agentic AI systems to operate with high levels of intelligence, adaptability and security. The integration of AI and ML drives continuous improvement and learning, NLP facilitates effective human-machine communication and blockchain ensures the integrity and transparency of transactions. This combination of advanced technologies positions agentic AI systems at the forefront of innovation, capable of transforming various industries and enhancing the way we interact with technology.

Agentic AI systems have various applications in the financial services world:

Automated Trading: Autonomous systems are capable of executing trades with remarkable speed and precision, utilizing advanced algorithms to optimize returns. For instance, hedge funds can employ AI-driven trading bots to meticulously analyze market trends and execute trades with impeccable timing, thereby maximizing profitability.

Fraud Detection: These intelligent agentic AI systems excel at identifying fraudulent activities by scrutinizing transaction patterns and detecting anomalies. Financial institutions can leverage agentic AI to monitor and prevent fraudulent transactions in real time, enhancing security and trust.

Personalized Financial Advice: Intelligent agentic AI systems can analyze vast amounts of customer data to offer bespoke financial advice, empowering clients to make well-informed decisions. Robo-advisors can deliver personalized investment strategies tailored to individual risk profiles, ensuring optimized financial planning.

Portfolio Management: Agentic AI-powered portfolio management tools offer tailored investment strategies based on individual risk tolerance and financial objectives. These tools continuously monitor and adjust portfolios to maximize returns, ensuring that investment strategies remain in harmony with market conditions.

Regulatory Compliance: Agentic AI facilitates regulatory compliance by automating monitoring and reporting processes. This encompasses anti-money laundering (AML) and "know your customer" (KYC) initiatives, where AI systems swiftly detect suspicious activities and ensure adherence to regulatory standards.

In conclusion, the advent of agentic AI systems marks a pivotal transformation in the financial services sector, to the tune of something I have not seen in my entire 20-plus-year career. By leveraging advanced technologies such as AI, ML, NLP and blockchain, these systems can enhance operational efficiency, security and decision-making processes. As agentic AI systems continue to evolve, they promise to drive innovation and redefine the landscape of financial services, making them indispensable tools for the future.

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