I. INTRODUCTION:

Artificial intelligence (AI) is the buzz word coined by John Mc Carthy, American Stanford University, Computer and Cognitive scientist, describes AI as most striking feature of software in imitation of a natural person in thinking manner a natural person behave, making logical decisions and selecting best alternatives among various options in order to achieve specific goal. A combination of human intelligence and artificial intelligence has alleviates the prospective growth and progress of in an unmatched way never experienced before. AI has brought tremendous changes in the fields such as education, aviation, health care, medical diagnosis, electronic trading, remote sensing, robotics and so on, Apart from these AI technology has been used in financial service sector in market analysis, personal finance and wealth management, insurance, banking, retail lending, credit score, process automation, in corporate finance fraud detection, default risks etc many more domains to enhance and enrich customer and stakeholders experience by offering innovative services with the help of AI technology.

According to NASSCOM Report, “Implications of AI on Indian economy” on average An increase in unit of AI intensity by companies AI contributes 67.25 US Million dollars, or 2.5 percent GDP growth to Indian economy, As per the survey AI services is predict to grow by 17.4 percent in year 2021-22 across the globe. According to the survey conducted by Deloitte 70 percent of the financial firms rely on machine learning to estimate cash flows, credit scores, detect frauds. Algorithms in emerging technologies are the major drivers for financial sector growth and development. According to RBI report 2020, public sector banks has witnessed 234 percent year-on-year increased fraud cases i.e, 80% of total cases reported, private banks has reported more than 500 percent increase which depicts 18 percent fraud cases in banking sector. AI inference will helps in prediction of gaps and reduce financial frauds significantly.

II. REVIEW OF LITERATURE:

Balmaseda et.al (2023) opines adoption of AI with deep graph learning help firms to analyse data, reduce potential threats with respect to financial transactions, improves the decision making of financial manager which help them to mitigate risks and assist customers in solving real life problems.Hermann and Masawi(2022) observed that an increase adoption of AI in banking industry has improved customer experience and the objective of AI adoption is to analyse the credit risks and fraud detection. Sezer et.al (2020) opines AI has brought transformation in finance industry helps the firms to analyse larger financial data accurately and helps financial managers to take informed decisions, also reduces potential risks associated with financial transactions of business. Lui. A, and Lamb, G.W (2018) addressed the major challenges related with execution of AI technologies in finance industry the authors opines that AI challenges could undermine the trust and confidence of investors and other stakeholders of business, the article specifically describes the usage of Algorithms in AI applications on Banks. Sarvady G (2017) has examined the extent use of AI applications and cost associated with AI channels strategic initiatives in the financial services sector. Ludwig E. (2018) explains the growth of AI applications in banking and financial service industry. Author has identified the role of AI in customer data analysis for credit scoring, lending loans, finally he suggests the measures to prevent frauds and maltreat of AI software’s. Nunn Robin (2018) made an attempt to describe the bias issues of AI and solutions to financial institutions would helps to strike the balance between AI applications with algorithms and population. He speaks that an increase in work force diversity, reduces the inherent biases in AI applications. Stella G (2016) in her research paper addressed the ethical issues in AI systems and emphasized on ethical learning environment, identify biases in algorithms, scrutinizing ethical dilemma and setting moral ethical standards. Daks M (2018) describes the importance of AI uses for banking and financial service providers with the help of “case study of acquisition of artificial intelligence company layer 6 by TD bank”. Author quotes the examples of financial institutions who adopted AI application for third party providers. FRTP Research in finance industry snapshot look into the role of AI introduction in Indian banks and states it was found that no loss of jobs AI chat bot tool acknowledge the effort of human staff as carried observation on SBI and YES Bank. Guy A Messick (2017) has made an attempt to explain the integration of AI applications with digital ecosystems of financial service industry and he look into the importance of AI in delivering customized services to its consumers. Meinert M.C (2018) describes the importance of AI in encountering the cyber crimes in finance sector and enriching customer experience and also focused on role on AI in increased cyber attacks.

III. OBJECTIVES OF THE STUDY:

1. To investigate the current state of AI adoption in financial services.

2. To identify the potential benefits and value drivers of AI adoption in financial services

3. To examine the challenges in adoption of AI in Finance Industry.

4. To look over the recent trends and developments in adoption of AI in finance industry from global perspective.

IV. RESEARCH METHODOLOGY:

The present study is purely qualitative in nature author has made an effort to offer insights to readers about changing landscape of AI applications in Finance industry. Author has collected information from various secondary sources which is published by NASSCOM, Deloitte, PWC, IFC, NVIDIA, RBI, Money outlook and other repute journals were used. The data has been collected and presented with the help of well defined inclusion and Exclusion criteria. The present study aims at understanding the facts and importance of Artificial intelligence applications in different domains with reference to financial services

Inclusion criteria:

The works pertaining to Artificial intelligence and financial services are the keywords used to collect information, only AI applications related to finance sector was majorly concentrated. Global and national works related to AI in finance was used in the study, besides author has used the reports generated by various agencies, financial institutions etc were used.

Exclusion Criteria:

Works other than finance domain related to AI were excluded from the present study.

The works available in other than English language has been excluded and numerical data has been excluded.

V. Applications of AI in Finance Industry:

There are some important areas within finance industry where AI applications have significant impact and gives value over traditional financial services.

1. Artificial Intelligence and Credit decisions:

Artificial intelligence helps in providing more precise information about potential borrower of loans with least cost. Many financial institutions such as banks, fintech companies are involved in lending money use crucial applications like machine learning to determine the credit scoring. In past such decisions were made by financial analysts after collecting information about borrowers. Credit scores provide by AI is obtained from complex and sophisticated rules compared with traditional approaches. AI applications help the financial institutes and lenders to differentiate between default risk applicants and credit worthy applicants but lack an extensive credit history. Advanced classified algorithms is used to determine the various explanatory variables i.e, savings, past credit, transaction history with institutions, demographic factors etc helps the lenders to arrive at final scores which describe the ability of individual to receive the loan. The advantage of AI based credit scoring is very useful in making unbiased decisions because of non- human involvement. The mood of institutional employees or any other factors does not affect decision making. Also help new individuals to improve their credit history and enhance their ability to repay loans regardless.

2. Prevention of Frauds:

It is the important area where the financial institutions highly rely on machine learning to face challenges posed by cyber crimes and frauds, frauds like credit card frauds, money laundering has grown very highly in the recent days due to increased online transactions, wide spread of e-commerce and third party integration. Fraud detection system helps to understand the behavior of clients, their buying patterns, helps to trigger the security mechanism when something seems to be different. Financial giants such as CITI, Goldman Sachs, and American express have the ability to detect fraud. The complex algorithms helps to analyze interactions among variety of situations also to build unique variety patterns that are used for real time updating.

3. Artificial Intelligence and Trading:

Investments supported with data driven technology has been rise over last five years it is nearly about trillion dollars investment in 2018. Popularly called as quantitative, high-frequency and algorithm trading. Modor Intelligence report specified that around 60-73% of overall equity trade in 2020 were dealt with AI support applications. Algorithm trading combines the feature of machine and deep learning from different domains. Few applications of the system helps to predict asset returns other components were based on econometric and asset pricing theories. AI trading has gained significance among individual data science practitioners, helps them to build own trading patterns, either on machine or through cloud computing. AI recommends investors for strong portfolio to meet long term and short term goals. Majority of the institutions rely on AI for portfolio management. Bloomberg has just released the Alpaca Forecast AI prediction matrix a price forecasting app tool for investors. It integrates the characteristics of real time market data with advance learning engine. This application helps to identify pattern of price movements for accurate predict. AI hedge funds which integrate the predictions of investors and allow them to manage earnings as well as funds own crypto currency.

4. Personalized Banking Experience:

Artificial intelligence aims at exploring new methods to provide value added benefits and comforts to individual users to enrich experience. In Banking Sector, AI provides smart chat bots that helps the clients to receive comprehensive self-help solutions which reduces the human involvement. A number of AI applications offer advisory services and help investors to achieve financial goals. The AI systems track individual income, check payments, payment schedules, essential expenses and spending habits. Big giants such as Well fargo, Bank of America and chase has introduced mobile applications which helps investors to remind payments, plan their expenditure and interact with investors to make the transaction hassle-free.

Chat bots helps the customers and institutions to leverage the huge data sets to understand the behavior of investors and offer suggestions.

5. Robotic Advisory services:

The wealth management services in which AI recommends the portfolios to investors based on their preferences, goals and risk associated with investments. The investors should deposit funds in their accounts. The process of investment begins with pricking up of security to invest, purchasing and re-balancing of portfolios in order to ensure customer has attained desired goals. The main advantage of this AI systems are hassle free transactions, user friendly and investors does not require sound financial knowledge Robo-advisor’s are comparatively cheaper than wealth managers.

6. Artificial Intelligence and Automation process:

Artificial Intelligent software helps to verify the data and provides generated reports based on given information robotic automation process eliminates the errors and helps the financial institutions to refocus on workforce efforts on process that need to processed by human beings, Ernst & Young has reported 50-70% reduced costs due to automation of financial services. Big giant JP Morgan Chase executed Robotic Process Automation to extract data sets, designing consumer regulations and managing consumer documents. Robotic Process Automation helps JP Morgan Chase to manage cash flows. It is very important to provide ID to avoid fraudulent practices many Neo-bankers and fintech companies were using mobile applications to verify or match the ID and other credentials.

7. Artificial Intelligence and Insurance Sector:

AI applications and systems help the insurer’s asses and detect frauds thereby errors are eliminated due to absence of human factor. Machine learning and natural language understanding ensures insurers helps in providing information, sec filling and so on. AI act as watch dogs to fight against frauds. Samsung notes in post about insurance fraud prevention. The cognitive machine learning algorithms have reached 75% accuracy rate for detecting fraud claims in insurance sector. Machine learning tools help to determine the claims and potential claims involved in insurance. These tools may help insurer to analyze images, sensors and to verify insurer’s historical data. AI risk assessment helps insurers to customize their plans and premiums exactly needed for them. An insurer can look at his claims and he can settle them.

Artificial Intelligence and Financial Inclusion:

Financial inclusion is majorly associated with providing equitable services to unbanked or unprivileged, weaker sections of society. Lack of access to financial services, geographical barriers, income levels, poor economic conditions and past lending experiences are the major reasons for financial inclusion. Financial inclusion is an initiative that contributes towards achievement of Sustainable Development Goals (SDG) by 2030 i.e, eliminating poverty and eliminating inequality among people and nations. AI model using alternative data helps to achieve financial inclusion goals which provide loans to those poor and needy does not have any track record of borrowing. China has executed this model called as Lu et.al experiment the model uses AI loan screening which has been approved as training data. The results of the experiment reveals that traditional data, online shopping history, mobile history, social media history to AI processing with the above data sets loans has been approved to the people who remained unbanked due to low income, education and home ownership.

Source: https://market.us/report/ai-in-finance-market/

RECENT TRENDS AND DEVELOPMENTS IN FINANCE INDUSTRY:

1. Role of AI adoption is comparatively increased in fraud detection financial institutions leverage the Machine learning algorithms to prevent fraudulent practices in 2024, it is estimated that 60% financial institutions adopt AI driven fraud detection systems. This initiative may help to detect and mitigate suspicious transactions in real time.

2. Algorithm trading is experiencing significant growth due to advances in technologies. AI algorithms are capable of trading at higher speed with great extent of accuracy, based on complex data analysis.70% of trading volumes in developed nations and it is expected to grow year on year. The role of AI in optimizing trading strategies and improving market efficiencies.

3. The global financial market valued at 12.4 billion USD in 2023 and it is expected to grow reach 73.9 billion by 2033 with 19.5% CAGR.

4. AI driven chatbots, virtual assistants assisting clients in delivery of financial services has emerged as opportunity for AI adoption in global financial markets.

5. Usage of Natural Language Process (NLP) for sentimental analysis of large volume of unstructured data such as news paper, social media is another emerging trend AI adoption in finance industry reduces risk using predictive analysis.

VI. Challenges of AI in Finance Industry:

• Privacy and Security:

Data is considered as important aspect which feeds AI algorithms the major challenges arise with data privacy. Though there are some challenges with data hacking but amount of data required to feed AI algorithm has become key challenge in execution of AI solutions. Hackers may use data to manipulate AI systems to their advantages. It is very important to test Algorithms for these cyber attacks to ensure robustness before execution.

• Lack of Integration:

Financial institutions are highly dependent on legacy systems. Due to volume and variety of data used these systems handle may not be suitable for direct AI applications. The models should be integrated with existing systems and challenges may arise with vendors are managing different applications. Most of the web applications are not AI friendly.

• AI and Cyber Security:

AI is considered as a double edge sword needs to manage carefully otherwise it may leads to improper judgments and incidents. Even though it provides solution to every problem. The execution of AI applications may subject to new way exploitations and abuse by malicious attackers. The attacks are related to data integrity, confidentiality and on data availability.

• Operational and Maintenance Challenges:

ML models are developed by data scientists. In order to develop AI applications data scientists need to collaborate with other teams like engineering, operational and other business. It has become challenge to integrate, communicate and coordination with various stakeholders of business. Collection of data and cleaning data is highly manual work in order to perform these activities requires more time. AI models undergo extensive testing and validation of data but organizations are not yet prepared for it. Since financial institutions are dynamic in nature and ever changing regulations should be fine tuned with algorithms in order to meet requirements.

• Skills and talent pools:

Organizations should identify the specific skill sets to execute AI applications within organizations. The major skills required to AI Implementation are related to project management, business analysis, domain expertise, data science and data engineering, user interface design, software development etc then management should decide to train skills to existing staff or should hire outsiders like data scientists and AI specialists to perform the work. Organizations are hiring pool of talents to fill skill gaps through university hires, external partners, consultants, vendors and also training in house people to use AI applications.

• Financial and Budget roadblocks:

Finance is the major constraint to slow down the use of AI in Financial sector now the financial institutions are looking forward to increase AI –led Investment projects, to achieve better performance. Financial service executives plan to invest in next five years from Asia pacific and North American 90 and 89% respectively. Most investment in AI led projects with system upgrades will help to resolve legacy system issues and improves efficiency.

VII. Principle findings of the study:

• AI has gained significant importance and it is used by financial industry in different domains such as market analysis, insurance, credit scoring, customer services, wealth management with the assistance of technology.

• The finance industry is paying attention towards AI investments and tends to minimize the work force and there by organizations achieve customer satisfaction and enrich their experience which is an objective of marketing team.

• AI Supports and helps in real time optimization of sales and marketing functions.

• AI increases digital literacy and helps to minimize transactional risks.

VIII. CONCLUSION:

The present study aims at identifying major applications of AI in financial service industry and the challenges associated with AI systems. It was observed that many innovations have taken place and AI is playing prominent role in Indian Financial Service Industry. A combination of AI and human interference has alleviates prospective growth. The customer demands has grown for digital financial services and become tech savy, financial institutions are adopting digital services it may increase the IT budgets and AI implementation becomes critical for Financial Industry success and evolve as new competitor for other industries.

IX. REFERENCES:

1. Balmaseda, V., Coronado, M., de Cadenas-Santiago, G. Predicting systemic risk in financial systems using Deep Graph Learning. Intelligent Systems with Applications. 2023; 19: 200240. https://doi.org/https://doi.org/10.1016/j.iswa.2023.200240.

2. Herrmann, H., Masawi, B. Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review. Strategic Change. 2022; 31(6): 549–569. https://doi.org/10.1002/jsc.2525.

3. Sezer, O. B., Gudelek, M. U., Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied SoftComputing, 90, 106181. https://doi.org/https://doi.org/10.1016/j.asoc.2020.106181

4. Lui, A., Lamb, G. W. Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector. Information & Communications Technology Law. 2018; 27(3): 267–283.

5. Sarvady, G. Chatbots, Robo Advisers, & AI: Technologies presage an enhanced member experience, improved sales, and lower costs. Credit Union Magazine. 2017; 83(12): 18–22.

6. Ludwig, E. Regulators have their eye on AI. American Banker. 2018; 183(130): 1.

7. Nunn, Robin. Workforce Diversity Can Help Banks Mitigate AI Bias. American Banker. 2018; 183(104): 1

8. Satell, G. Teaching an Algorithm toUnderstand Right and Wrong. Harvard Business Review Digital Articles. 2016: 2–5.

9. Daks, M. Banking on Technology: Artificial intelligence helping banks get smarter. Jbiz. 2018; 31(7)

10. Guy A. Messick. Artificial Intelligence: The Ultimate Disrupter. Credit Union Times. 2017; 28(38): 12.

11. Meinert, M. C. Artificial Intelligence: The Next Frontier of Cyber Warfare? ABA Banking Journal. 2018; 110(3): 43

12. https://community.nasscom.in/communities/ai/nasscom-ai-adoption-index-2022.

13. https://www.pwc.in/assets/pdfs/research-insights/2022/ai-adoption-in-indian-financial-services-and-related-challenges.

14. https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-ai-2022.html

15. https://www.nvidia.com/en-us/industries/finance/ai-financial-services-report-2022/

16. https://www.outlookindia.com/business/83-indian-financial-organisations-say-ai-enhances-customer-experience-finds-study-news-184589

Received on 27.08.2025 Revised on 11.10.2025

Accepted on 16.11.2025 Published on 11.05.2026

Available online from May 14, 2026

Asian Journal of Management. 2026;17(2):135-140.

DOI: 10.52711/2321-5763.2026.00020

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Features	Description
Report Coverage	Revenue Forecast, Market Dynamics, COVID-19 Impact, Competitive Landscape, Recent Developments
Segment coverage	Component (Solution, Services) by Deployment mode (Cloud-based, On-premise), by Application (Virtual Assistant (Chatbots), Business Analytics and Reporting, Customer Behavioural Analytics, Fraud Detection, Quantitative and Asset Management, Other Applications) Region
Regional Analysis	North America – US, Canada; Europe – Germany, France, The UK, Spain, Italy, Russia, Netherlands, Rest of Europe; Asia Pacific – China, Japan, South Korea, India, New Zealand, Singapore, Thailand, Vietnam, Rest of APAC; Latin America – Brazil, Mexico, Rest of Latin America; Middle East & Africa – South Africa, Saudi Arabia, UAE, Rest of MEA
Competitive Landscape	Capgemini, Google, Oracle Corporation, HCL Technologies Limited, SAP SE, FiCO, TIBCO Software, Inc., ComplyAdvantage, IBM, Inbenta Holdings Inc., Cisco Systems, Inc., Amazon Web Services, Inc., Saleforce, Inc., Intel Corporation, Hewlett Packard Enterprise Development LP, Microsoft, Cognizant, Other Key Players