A Comprehensive Study on Integration of Big Data and AI in Financial Industry and its Effect on Present and Future Opportunities

: This study evaluates the substantial influence of AI-technologies in the finance industry, with advancement expected to accelerate in the next few years. It also forecasts the expansion of AI adoption across various business sectors and the integration of AI-based operational networks with existing commercial systems to meet consumer demands. The financial industry will blend AI-based transaction channels with established systems, enhancing the customer experience and efficiency. This integration will streamline and advance transaction processes, making them more responsive to customer demands. The standard change in customer dealings is expected to be a significant transformation in the financial sector. The transformative potential of Big Data and AI in the financial sector goes beyond operational improvements; these technologies will create new opportunities for growth and development, giving financial institutions a modest point in operational efficiency and innovative product and service offerings. This study aims to investigate the overall impact of the convergence of Big Data and AI on the financial industry. It anticipates increased revolution, diversification of commercial applications, and smooth AI integration into existing systems. These technologies will shape the financial environment in the future, offering new opportunities for industry participants and consumers.


I. INTRODUCTION
Big Data analytics is typically the technical process of analyzing massive amounts of data to discover patterns, linkages, market trends, and customer preferences information that can assist businesses in making better-informed decisions.Artificial intelligence is the capability of computer systems to perform tasks that generally require human intelligence, such as speech recognition, decision-making, and pattern understanding.
In the age of the digital revolution, the financial industry is undergoing significant changes.There has been a drastic shift in the way financial institutions operated in the past, and this has become possible by the integration of AI and Big Data in the finance industry.
Every single day, the financial industry generates enormous data, ranging from customer information to transaction records.All praises to digitalization, these data are not only kept confidential but can also be quickly provided to customers when required for strategic planning and decision-making.With the advancement of these digital technologies, enormous datasets can be efficiently handled, analyzed, and recorded.Furthermore, unstructured data can be converted to systematically structured Data.Scam detection, credit grading, and customer relationships can be managed more efficiently due to the integration of these technologies into the system [1].
In finance, deep reinforcement learning is used.Artificial intelligence, particularly reinforcement learning, can optimize hedging tactics in derivative markets [2].IoT and banking integration are evolving, with AI and Big Data becoming increasingly crucial in understanding data from interconnected financial equipment [3].While not entirely financial in nature, AI and machine learning have a broader impact on the future of labor.They provide insight into how these technologies, including their applications in finance, are influencing the labor market [4].
Due to the integration of AI and Big Data in the finance industry, it is progressing toward digitalization and leading to an effective transformation.In addition to changing the financial services industry, these technologies are opening up previously unheard-of possibilities for expansion and innovation.Big Data offers a multitude of information that was previously incomprehensible.It is usually classified and identified based on its size, speed, and variations.AI tools, especially in machine learning (ML), aid in the evaluation and management of credit and its related risks in the finance industry [5].Nowadays, financial markets are utilizing this information to gain awareness of market demands, customer preferences, and scam evaluations.The importance of investigating how the integration of Big Data and AI can influence the financial markets and provide insights into challenges and opportunities for the present and future cannot be underestimated.If the financial market aims to be innovative, and progressive, and provide customers with better services, it must acknowledge these changes.This research intends to evaluate the current influence of AI and Big Data in the finance industry, examining how they can contribute to the industry's future growth and identifying the necessary changes these industries should implement in their systems.In doing so, it aims to provide a complete image of the evolving financial landscape and assist stakeholders in exploiting the potential of these disruptive technologies.
The major objectives of this research are: • To analyze the influence of AI and Big Data in various aspects of the financial industry.
• To analyze the current impact of AI and Big Data in the finance industry.
• To identify evolving demands and future opportunities created by AI and Big Data.• To provide structured recommendations for financial institutes to adopt these technologies for growth and development.

II. LITERATURE REVIEW
The financial industry is undergoing an evolution due to the integration of Big Data and Artificial Intelligence (AI).This literature review examines the current state and prospects of this revolutionary impact on financial processes, decision-making, and services.The 21st century has become a transformative era, due to the invention of AI, and has completely reshaped our society.AI has begun to transform the way we live, particularly in financial services, including banking transactions, stock investing, insurance, and loan services [6].It precisely covers a wide range of financial businesses, including crowdfunding platforms, operational peerto-peer lending, wealth management, SME finance, and asset management platforms, trade management, crypto-currency, money/remittance transfer, mobile payments systems, and so on [7].While technologies capable of performing human-based tasks cannot replace real humans, today's AI systems can perform activities that are well-defined and typically require human intelligence quite effectively [8].
A study highlighted the potential of blockchain technology in the financial sector, emphasizing its decentralized nature and the use of AI algorithms for secure and transparent transactions [9].Big Data plays a crucial role in the financial sector, integrating AI algorithms with vast datasets to provide actionable insights, enhance decision-making, and optimize operational efficiency [10].AI-driven analytics efficiently manage large volumes of financial data, enabling predictive modeling, risk assessment, and real-time decision-making for improved financial outcomes [11].
Machine learning algorithms are being used to provide personalized financial recommendations, thereby improving customer experiences and increasing engagement and satisfaction [12].AI technologies are revolutionizing traditional financial practices, enhancing efficiency and accuracy [13].They are being used for fraud detection and portfolio optimization, automating complex financial tasks, and enhancing industry performance [14].A study emphasized the significant role of AI-powered Big Data analytics in detecting and preventing financial fraud, utilizing advanced algorithms to analyze vast datasets and detect anomalous patterns [15]. .Digitalization has boosted AI-based business models, with numerous institutions entering the market.Banks and insurers are either spinning off AI expertise or paying billions to acquire it.Machine learning has significantly enhanced market efficiency and investor returns by integrating it into trading strategies, risk management, and investment decisions [16].Research on the ethical implications of AI and Big Data in finance has highlighted potential biases and the need for responsible AI practices in financial decision-making [17].Regulatory challenges might arise from the integration of Big Data and machine learning in finance, such as data privacy, algorithmic transparency, and the evolving regulatory landscape [18].

III. IMPACT OF AI AND BIG DATA IN BANKING INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES
AI and Big Data are transforming the banking industry in various ways, including enhancing customer experience, improving risk management, increasing efficiency, and creating new business models.Fraud poses a significant challenge for banks and financial institutions, with potential losses reaching billions of dollars annually.These institutions often keep sensitive data Earlier versions of fraud detection systems relied on a set of guidelines that today's fraudsters can easily circumvent.Therefore, an increasing number of businesses are turning to machine learning to identify and stop illegal financial activities.AI has the potential to help banks improve their efficiency, reduce costs, and enhance customer experience [19].Here are some examples of how AI and Big Data are impacting the banking industry and the opportunities they present for both the present and the future: • Customer Experience: The integration of Big Data analytics, artificial intelligence, and blockchain technologies in business and management opens doors for present and future opportunities.The potential of these technologies lies in transforming business operations and creating new business models through digitalization [20].AI and Big Data can help banks provide personalized and seamless services to customers through chatbots, voice assistants, robo-advisors, and recommendation systems.For example, ChatGPT is a language model that understands and generates human-like responses to user questions using natural language processing and machine learning.Banks can use ChatGPT and similar models to offer 24/7 customer service, financial advice, and product suggestions.• Risk Management: AI and Big Data can help banks in detecting and preventing fraud, money laundering, cyberattacks, and other risks by analyzing large volumes of data and identifying patterns, anomalies, and behaviors.Cyber-security is an application of AI that is specifically designed to protect computer networks, programs, and data from attacks, illegal access, amendments, or destruction [21] For example, AI can help banks monitor transactions, verify identities, flag suspicious activities, and alert customers and authorities in real time.• Efficiency: Another significant contribution of AI is its automation, providing authentic insights and recognizing patterns and trends in datasets, thereby making tasks more efficient and less time-consuming [22].This helps reduce costs, errors, and interruptions and enhances overall value and efficiency.For instance, Artificial intelligence assists banks in identifying the reliability of borrowers, providing their customers with financial loans, and hence processing their applications for loans in a smooth and transparent way.• Business Models: Predictive analytics, social banking, behavioral finance, and open banking are some of the creative and novel services that banks offer, made possible by the tools of Big Data and AI in the Era of digitalization.These services improve the quality of banking, enhance customer relationships, and help create new revenue streams.For instance, artificial intelligence assists the banks in detecting data on the customer's preferences and offers them solutions to their problems within the time frame.

IV. IMPACT OF AI AND BIG DATA IN STOCK INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES
AI and Big Data are also significantly impacting the stock industry, enhancing trading strategies, improving market efficiency, and creating new investment opportunities.A survey has found that insurance claims are the most unsatisfactory part of the entire insurance product consumption process.This is because the claims process often requires a significant amount of detailed documentation.In case of dispute, multiple investigations and verifications are required, making the process complex and draining for customers.Moreover, at the same time, there are many cases of unreasonable claims in today's insurance industry.This can be attributed to policyholders hastily signing contracts without understanding the terms and conditions thoroughly.Additionally, some insurance companies also suffer from misleading sales and lax underwriting.Compared with traditional manual claims, intelligent claims can be quickly viewed, accounted for, and fixed, which can save a lot of time and improve the efficiency of claims processing.Here are some examples of how AI and Big Data are impacting the stock industry and its present and future opportunities: • Trading Strategies: According to a paper published in The Journal of Finance, algorithmic trading has a positive influence on the financial market.The authors of the paper analyzed the effect of algorithmic trading on market quality and found that it narrows spreads, reduces adverse selection, and enhances the informativeness of quotes [23].AI and Big Data can help traders and investors analyze large amounts of data from various sources, including market prices, news, social media, and sentiment, to generate insights, signals, and predictions for optimal trading decisions.For instance, AI algorithms can perform trades more quickly, at lower costs, and with less human bias, while also having the potential to scan market anomalies and provide clear and transparent trading opportunities.Furthermore, AI plays a significant role in structuring trade patterns and adaptive trading strategies, enabling them to learn it deeply and give feedback to adapt according to the updated market.

V. IMPACT OF AI AND BIG DATA IN INSURANCE INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES
AI and its tools have a significant influence on every aspect of insurance, from allocation to financing, pricing to claims.AI and its advanced technologies are already shaping distribution and underwriting, with strategies being valued, purchased, and bound in near real-time [25].The stock market may be the most sophisticated technique.A person exchanges shares for stocks with an entrepreneur and gets some cash back in the meanwhile.Following this model can be a potential investment strategy.However, at this stage, costs and liquidity may not be stable.Machine learning is a kind of equipment to encourage us to achieve what we want.ML has different techniques and methods to implement the prediction framework, for instance, fundamental analysis, technique analysis, market computation, etc. Academic research suggests that the usage of artificial neural networks, comprising recurrent neural networks, which efficiently apply machine learning, is the most evident technique [26].
Here are some of the main impacts and opportunities of AI and Big Data in the insurance industry: • Customer Experience: AI and Big Data can help insurers provide personalized and seamless services to customers through chatbots, voice assistants, robo-advisors, and recommendation systems.These technologies can offer 24/7 customer service, financial advice, and product suggestions based on the customer's needs, preferences, and behaviors.• Risk Management: AI and Big Data can help insurers detect and prevent fraud, money laundering, cyberattacks, and other risks by analyzing large amounts of data and identifying patterns, anomalies, and behaviors.These technologies can help insurers in monitoring transactions, verify identities, flag suspicious activities, and alert customers and authorities in realtime.• Efficiency: Automation, which is an innovative and widely used tool of AI and Big Data, assists insurance market consumers in various processes such as underwriting, pricing, claims, compliance, reporting, and auditing.This helps reduce the cost of services, enhance value, and improve efficiency in the market.AI would also help to detect the reliability of the customers and provide them with insurance services in streamline.Not only that, it assists the insurers in getting loans in less time.• Business Models: AI and Big Data help the insurance market to create services tailored to customers' preferences and demands, providing solutions to their financial problems within their timeframe.Due to these services provided by the industry there is rapid growth in consumer relationships loyalty, satisfaction, and revenue.AI and Big Data are not only changing the financial industry, but also creating new challenges and opportunities for them.Some of the challenges include ensuring data quality, security, and privacy, complying with regulations and ethical standards, managing talent and skills, and fostering trust and transparency.Some of the opportunities include collaborating with FinTech and other players, leveraging cloud and edge computing, and adopting agile and customer-centric approaches.To succeed in the AI and Big Data era, the financial industry needs to embrace innovation, transformation, and differentiation.

VII. POPULATIONS AND SAMPLING A. Target Population
The research was intended to evaluate the influence of AI and Big Data in the finance industry, so the target population is the financial industry.For this purpose, three main finance industries including banks, insurance, and stock industries are selected.

B. Sampling Method
Simple random sampling is used for this research.

C. Questionnaire Development
• Design: The questionnaire was designed according to the demand of research to evaluate the influence, challenges, and opportunities provided by AI and Big Data in the financial industry.• Question Types: The questionnaire was divided into four portions.The first portion was for personal information, the second portion consisted of a set of questions that determine the level of awareness of Big Data and AI Data, the third portion was to assess if the industry is using these tools in their structure already and what current opportunities they are enjoying while using these tools, last section of the questionnaire contained open-ended questions.This section was constructed to know what future opportunities can be predicted for finance growth by utilizing these tools.• Data Collection: Data for this research were collected through a questionnaire.The survey was conducted by physically distributing the questionnaire to various finance sectors, and data were collected in steps.The responses provided a practical view of the influence of AI and Big Data in the professional industry.• Data Analysis: The results yielded from the survey underwent thematic analysis.Since open-ended questions were also used, themes and logic behind the answers would be identified.This paper provides a comprehensive examination of AI and Big Data in various aspects of the finance business.The study also examines the current and future potential made available by the integration of Big Data and AI in the banking industry.Therefore, it is crucial to learn about finance industries such as banks, insurance industries, and the stock market to understand how Big Data and AI are altering the financial industry now and what opportunities may emerge in the future.The comparative aspects enrich the study by considering various perspectives, situations, or time frames within the financial industry.To support the facts and data, various forms of research have been incorporated into this study.After conducting various types of research, it is essential to properly review their findings and gather data from them.This study has integrated data from previous research work on AI into the analysis to improve the quality of this study.In addition, some questionnaires were physically distributed across different business sectors, and data was collected step by step.As a result, this research yields notable findings, which are detailed in the results section.

VIII. RESULTS
Significant results were generated from the survey conducted by the questionnaire.The main findings of the research indicated that the integration of AI and Big Data into the finance industry increased the current opportunities, such as increased

A. Overview of AI and Big Data Integration
The integration of AI and Big Data analysis revealed comprehensive and efficient results in the financial industry.These results provide insights into adoption rates, key players, and emerging trends within the industry.

B. Experimental Research Findings
The results of the experiments used in this research provided valuable insights into the influence of AI and Big Data on financial growth.The experiments showed that when the financial industries integrated AI and Big Data into their systems, yielded positive outcomes.The following are some statistics of experiments conducted in different financial sectors.The experiments were carried out in different phases to precisely analyze the potential of AI and Big Data in these sectors.

C. Thorough Examination of AI and Big Data in Finance
A detailed investigation of the implementation of AI and Big Data in different sections of finance was conducted.The results showed that these tools have significant potential and influence in the finance industry, with widespread applications in credit scoring, fraud detection, and customer relationships.

D. Current and Future Potential in Banking
This study emphasized the current and future influence of AI and Big Data in the finance industry.It was found that when AI and Big Data were integrated into the financial industry's structure, areas such as credit management, operations, trading, and consumer relationships reached to a more advanced level.Moreover, it shifted the entire orthodox system towards digitalization and automation, which was less time-consuming and had the least bias and scam.

E. Understanding the Changing Aspects of Finance Industry
To understand and identify the evolving structure of the finance industry, data were collected from the most economically contributing industries of the system i.e.Banks, stock, and insurance market.The study involved a comparative analysis to evaluate precise results, considering different scenarios, situations, and time frames within the finance market.

F. Integration of AI and Big Data into the Study
As the primary method of data collection was used in this research, previous data and AI analysis were also incorporated into the study to conduct a comparative analysis of Big Data and AI and their influence on the financial industry.This approach also predicted the current influence of these tools in the market and what type of opportunities they can provide in the future.A comparative analysis between AI and Big Data was done to check the efficiency and demand of both and the following results were achieved.

Figure 1: Impact of AI and Big Data in Financial Industry
It was evaluated that the stock market was more familiar with and actively utilizing AI and Big Data in their job structures.Due to integrating AI and Big Data into their systems, they were growing more in the finance industry than the rest of the industries.

IX. CHALLENGES AND CONSIDERATIONS
The research acknowledges the ethical implications of AI and Big Data in finance, emphasizing the importance of responsible practices.Regulatory challenges, including data privacy and algorithmic transparency, are recognized as potential obstacles to the widespread adoption of AI and Big Data in the financial industry.

X. FUTURE TRENDS AND OPPORTUNITIES
The study predicts that if AI and Big Data are used properly, they can significantly transform existing financial trends and quality to a diversified and progressed level.It also predicts that in the current scenario AI and Big Data can be used in a more efficient way to shape the future to provide the customer with more valuable opportunities.

XI. RECOMMENDATIONS
• For a successful and diversified financial system, AI and Big Data must be used for generating quality data, validation, data cleansing, and governance.• Cybersecurity is another significant application of AI and Big Data that should be incorporated into financial setups to ensure the confidentiality of sensitive data, detect scams, and ensure transparent audits.• Financial sectors should also implement talent management strategies and provide training in ML and Data Science to acquire individuals with expertise in digitalization and deep learning.

XII. CONCLUSION
In conclusion, this investigation has explained that if a structured implementation of AI and Big Data is done in the finance sector it can raise the potential of financial industries to the next level.This represents not only a transformation of the current functionalities within financial industries but also a significant step toward progression and development.The combination of AI and Big Data is highly innovative, if used properly, it has the potential to reshape current opportunities and nurture future opportunities for the welfare of mankind.
the risk of security breaches.As a result, technological advancements have made financial fraud a major threat.

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Market Efficiency: Efficiency and transparency in the stock market are maintained by AI and Big Data by enhanced liquidity, reduced information asymmetry, and lower transaction costs.For instance, AI becomes instrumental in aiding the market customers to get access and process their relevant information within their time frame including their earnings reports, financial statements, and corporate actions, and hence reduces hustle and bias.Besides this, detection of market mishandling, scams, and mismanagement are also eliminated, supervised, and regulated by AI.• Investment Opportunities: In research conducted byRoncalli (2018), the significance of machine learning (ML) in trading is explained.The study highlighted how AI tools enhance the validity of factor models and portfolio optimization[24].These tools help in the creation of innovative services such as robo-advisors, smart beta, and thematic investing.For instance, robo-advisors can provide personalized financial advice, automation, and portfolio management based on the investor's demands and preferences.Additionally, Smart Beta can integrate the benefits of both passive and active investing by the use of AI and Big Data.AI and Big Data are also used in Thematic investing to recognize and invest in long-term trends and themes, such as ESG, technology, and healthcare.
This research adopted a quantitative cross-sectional approach through a survey to evaluate the influence of Big Data and AI in the Financial industry and current and future opportunities.B.Scope of the ResearchThis research will focus on the following areas:• Impact of Big Data in Finance: The different sources of Big Data and AI which are connected to the finance industry, data collecting methodology and insights can be obtained from it.• AI in Finance: Various AI tools like ML Machine learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) and their relevance to the finance industry will be identified.• Impact of AI and Big Data in Future Trends: This Study will explore the future demands of AI and Big Data in finance and how they can positively reshape their future.
customer satisfaction, less pricey, less time consuming, scam detection and management credibility, and creation of new opportunities in the industry.

Table 2 : Questionnaire-based Data Collection
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