INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

Finance and Investment Banking - Lack of Industry -

Specific AI solutions

Senthil Kumar N^1*, Sowmi Shruthiksha K², Preya Darsine S³

^1*Assistant Professor -III, School of Management Studies, Bannari Amman Institute of Technology,

Sathyamangalam, India

²Second year MBA, School of Management Studies, Bannari Amman Institute of Technology,

Sathyamangalam, India

³Second Year MBA, School of Management Studies, Bannari Amman Institute of Technology,

Sathyamangalam, India

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000341

Received: 07 October 2025; Accepted: 14 October 2025; Published: 22 November 2025

ABSTRACT

Existing workflows have been altered by the rapid adoption ofArtificial Intelligence (AI) across businesses, yet

there is a notable lack of industry-specific AI solutions in the finance and investment banking areas. Although

Artificial Intelligence (AI) has shown promise in technology, handling risks, and data analytics, most current

systems are based on generalist technologies that are unable to handle the particular intricacies of the financial

industry. Specialized AI models designed for the industry are required to address issues including client-specific

requirements, complex market behaviours, and regulatory compliance. This examines the effects of the lack of

custom AI solutions, emphasizing errors, underutilized data, and lost opportunities for creativity. By looking at

the drawbacks of flexible AI tools, we highlight how crucial it is to create focused AI applications in order to

fully realize automation's potential, improve decision-making, and keep an edge over competitors in the quickly

changing financial sector. In a rapidly AI-driven environment, closing this gap is essential for improving client

outcomes, increasing operational efficiency, and assuring conformity to regulations.

Keywords: Artificial Intelligence, Finance, Investment Banking, Regulatory Compliance, Automation, Risk

Management, Data Analytics.

INTRODUCTION

The financial and investment banking sectors are undergoing rapid digital transformation, yet the absence of

industry-specific AI solutions remains a significant hurdle. Unlike other industries, finance operates in a highly

complex and regulated environment, where market fluctuations, compliance requirements, and vast data sources

create challenges for AI adoption (Brock, 2021). Many existing AI models are designed for general applications

rather than tailored financial analytics, limiting their effectiveness in areas such as risk assessment, portfolio

management, and algorithmic trading.

Since financial organizations rely on a variety of data sources, such as market reports, regulatory filings, and

unstructured financial news, data fragmentation makes implementing AI even more difficult (Jiang, 2020).

Furthermore, generic AI solutions find it challenging to keep current due to changing compliance laws, which

raises the risk of non-compliance. Concerns are also raised by AI's "black-box" nature since investment

decisions need to be transparent and interpretable (Zhang & Evans, 2022).

Furthermore, because financial activities are high-stakes, investment banks frequently oppose technological

advancement. AI solutions must be designed to improve regulatory compliance, produce results that are easy to

understand, and interact smoothly with financial models in order to close this gap. In order to develop sector-

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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

specific solutions that promote accuracy, efficiency, and trust in investment banking, cooperation between AI

specialists and financial professionals is crucial.

Figure 1- AI Adoption Trends in Finance

DESCRIPTION

A.Finance

The vast field of finance includes the management of funds, investments, and financial hazards for governments,

corporations, and individuals. In order to guarantee economic stability and progress, it focuses on procedures

like financial planning, borrowing, investing, saving, and budgeting. Making educated judgments on asset

management, capital allocation, and wealth building requires a solid understanding of finance.

B. Investment Banking

Raising money, offering financial advising services, and enabling big transactions for governments, businesses,

and institutional investors are the main objectives of investment banking, a specialist area of finance. Investment

banks mostly handle intricate financial activities including asset management, initial public offers (IPOs), and

mergers and acquisitions (M&A), in contrast to retail banking, which caters to individual clients.

C. Significance

Innovation in the finance and investment banking sectors is greatly aided by the dearth of industry-specific AI

solutions. Rather from being a drawback, this gap enables the creation of customized AI models that tackle

particular financial issues including trading tactics, risk management, and regulatory compliance. Financial

organizations may improve client experiences, automate procedures, and make better decisions by using

specialized AI solutions. FinRobot and other AI-powered financial systems demonstrate how sophisticated

financial analysis and automation may be fuelled by huge language models.

The lack of industry-specific AI solutions significantly facilitates innovation in the finance and investment

banking industries. Instead of being a disadvantage, this gap makes it possible to develop tailored AI models

that address specific financial problems including trading strategies, risk control, and regulatory compliance.

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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

Using specialist AI technologies may help financial firms make better decisions, automate processes, and

enhance customer experiences. FinRobot and other AI-powered financial systems show how large language

models can enable complex financial automation and analysis.

DETAILED DESCRIPTION

A. Growing Market for AI Innovation

There is a great need for innovation because the financial industry currently lacks specialized AI solutions. AI

technologies that can manage intricate investment plans, risk assessments, and market forecasts are in high

demand among financial organizations. This is a fantastic chance for banks, fintech companies, and AI

developers to work together to create unique AI models that are tailored to the requirements of investment

banking.

B. Improved AI-Driven Decision-Making

Even though there are broad AI tools available, creating AI solutions tailored to the financial industry can

improve trading, M&A, and portfolio management decision-making. Businesses may increase the accuracy of

risk forecasting, fraud detection, and asset appraisal by developing AI models specifically for financial data.

This will result in more profitable and robust investment plans.

C. Enhanced Regulatory Compliance

Although regulatory restrictions have hindered the use of AI, they also present a chance to create AI solutions

that meet compliance standards. Financial businesses may enhance auditing procedures, boost transparency,

and comply with international standards such as Basel III, GDPR, and SEC rules with the use of Explainable

AI (XAI). AI solutions with a compliance focus will be crucial for safe and compliant financial operations.

D. Data-Driven Competitive Advantage

Large amounts of market, transaction, and economic data are essential to investment banking. Businesses can

create sophisticated AI models that effectively handle and interpret fragmented data, giving them a competitive

edge, by filling the present AI gap. Businesses may make quicker and better-informed investment choices with

the use of AI-powered sentiment analysis, algorithmic trading, and predictive analytics.

E. Increased Collaboration

More cooperation between regulators, financial analysts, and AI developers is being encouraged by the need for

AI solutions tailored to the banking industry. Because of this cross-sector cooperation, AI models are created

with realistic financial knowledge, increasing their usefulness, efficiency, and conformity to banking

regulations. The upcoming generation of AI-powered investment tools will be driven by these collaborations.

F. Personalized Financial Services

The use of AI to finance is creating opportunities for highly customized financial services. Custom financial

advice, automated wealth management, and enhanced client experiences are all possible with tailored AI

solutions. AI will provide institutional and individual investors with more intelligent, data-driven decision-

making capabilities as it becomes increasingly tailored for the financial sector.

ANALYSIS AND INTERPRETATION

A. Innovation Opportunities

Opportunities for highly tailored financial services are being created by the application of AI to finance. Tailored

AI solutions provide automated wealth management, personalized financial advising, and improved customer

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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

experiences. As AI becomes more specialized for the financial industry, it will give institutional and individual

investors the ability to make more informed, data-driven decisions.

B. Greater Flexibility and Customization

Since there are now no industry-specific AI tools available, financial institutions can take advantage of more

general, wider AI technology and tailor them to suit their own requirements. Businesses may customize AI

technologies to their operations, increasing flexibility, rather of being limited by established solutions. AI may

be used, for example, to provide clients with individualized banking experiences or customized investing

recommendations according to their risk tolerance and personal preferences.

C. AI Integration with Regulatory and Compliance Frameworks

Investment banking and financial services are highly regulated industries. There is a chance to create AI apps

that not only address business issues but also smoothly mesh with regulatory frameworks because there aren't

any industry-specific AI solutions available. AI systems may be developed to simultaneously satisfy corporate

and regulatory requirements by automating reporting, monitoring operations, and ensuring compliance.

D. Advancement in Risk Management

An essential component of finance and investment banking is risk management. Financial institutions have the

opportunity to create systems that particularly handle the risk profiles and complexity of their portfolios in the

absence of pre-existing AI solutions. By improving predictive skills, these tailored AI models may help

institutions anticipate and take proactive steps to mitigate risks including loan defaults, market volatility, and

cybersecurity breaches.

CONCLUSION

Industry-specific AI solutions are currently lacking in the finance and investment banking industries, but this

vacuum offers enormous potential for innovation, cooperation, and expansion. Businesses who make

investments in regulatory-friendly models, data-driven decision-making tools, and bespoke AI development

will have a competitive advantage in the changing financial environment as AI adoption grows. AI in finance

has a bright future, and filling up existing gaps will open up new avenues for productivity, precision, and

profitability.

REFERENCES

1. Brock, J. (2021). AI in Finance: Challenges and Opportunities. Financial Technology Review.

2. Jiang, Y. (2020). Data-Driven Finance: The Role of AI in Investment Banking. Journal of Financial

Innovation, 12(3), 45-62.

3. Zhang, L., & Evans, M. (2022). AI and the Future of Financial Services: Regulation and Risk Management.

4. Harvard Business Review. Accenture. (2023). "AI in Banking: Trends and Opportunities."

5. Accenture (2020). AI in Banking: A New Frontier for the Industry.

6. Deloitte. (2022). "The Future of Artificial Intelligence in Financial Services."

7. World Economic Forum. (2023). "AI and Financial Services: Risks and Benefits."

8. McKinsey & Company. (2022). "The Role of AI in Transforming Investment Banking."

9. KPMG (2020). Artificial Intelligence in Risk Management for Financial Institutions.

10. PwC. (2021). "Building Trust in AI: A Framework for Financial Institutions."

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